Aim/Purpose: This paper examines the behavior of financial firm employees with regard to information security procedures instituted within their organization. Furthermore, the effect of information security awareness and its importance within a firm is explored. Background: The study focuses on employees’ attitude toward compliance with information security policies (ISP), combined with various norms and personal abilities. Methodology: A self-reported questionnaire was distributed among 202 employees of a large financial Corporation Contribution: As far as we know, this is the first paper to thoroughly explore employees’ awareness of information system procedures, among financial organizations in Israel, and also the first to develop operative recommendations for these organizations aimed at increasing ISP compliance behavior. The main contribution of this study is that it investigates compliance with information security practices among employees of a defined financial corporation operating under rigid regulatory governance, confidentiality and privacy of data, and stringent requirements for compliance with information security procedures. Findings: Our results indicate that employees’ attitudes, normative beliefs and personal capabilities to comply with firm’s ISP, have positive effects on the firm’s ISP compliance. Also, employees’ general awareness of IS, as well as awareness to ISP within the firm, positively affect employees’ ISP compliance. Recommendations for Practitioners: This study can help information security managers identify the motivating factors for employee behavior to maintain information security procedures, properly channel information security resources, and manage appropriate information security behavior. Recommendation for Researchers: Researchers can see that corporate rewards and sanctions have significant effects on employee security behavior, but other motivational factors also reinforce the ISP’s compliance behavior. Distinguishing between types of corporations and organizations is essential to understanding employee compliance with information security procedures. Impact on Society: This study offers another level of understanding of employee behavior with regard to information security in organizations and comprises a significant contribution to the growing knowledge in this area. The research results form an important basis for IS policymakers, culture designers, managers, and those directly responsible for IS in the organization. Future Research: Future work should sample employees from another type of corporation from other fields and should apply qualitative analysis to explore other aspects of behavioral patterns related to the subject matter.

Aim/Purpose: This study examines the kind of community value companies should provide when strengthening the relationship between customers and brands through the establishment of an online brand community, and how this kind of community value promotes customers’ sense of community engagement and willingness to spread brand reputation. The paper also discusses how an enterprise’s brand symbolism affects the relationship between community value and customers’ engagement in online brand community. This study explored the important role of brand symbolism in the establishment of an online brand community. Background: Many companies want to create online brand communities to strengthen their relationships with consumers as well as to provide better service and value to consumers, for example, Huawei’s Huafen community (club.huawei.com), Apple’s support community (support.apple.com/zh-cn), and Samsung’s Galaxy community (samsungmembers.cn). However, these brand communities may have different interests and consumer engagement about the kind of community value to offer to their customers. Methodology: This study uses data collection from questionnaire surveys to design a quantitative research method. An online questionnaire survey of mobile phone users in China was conducted to collect data on social value, cognitive value, brand symbolism, customer community engagement, and brand recommendation. The brands of mobile phone include Apple, Huawei, Samsung, OPPO, VIVO, MI, and Meizu. The researcher purchased a sample service of WJX, an online survey company (www.wjx.cn), and WJX company distributed the questionnaire to research participants. The WJX company randomly selected 240 subjects from their sample database and then sent the questionnaire link to research participants’ mobile phones. Among the 240 research participants, the researcher excluded participants who lacked online brand community experience or had invalid data to qualify for data collection. After the researcher excluded participants who did not qualify for data collection, only 203 qualified questionnaire surveys advanced to the data collection and analysis phase, which was the questionnaire recovery rate of 84.58%. For the model analysis and hypotheses testing, the researcher used statistical software IBM SPSS Statistics and AMOS 21 and Smartpls3. Contribution: This study deepens the body of literature knowledge by combining online brand community value and brand symbolic value to explore issues that companies should consider when establishing an online brand community for their products and services. This study confirms that brands with high symbolic value establish communities and strengthen social values in the online brand community rather than reducing brand symbolism. Online brand community involves a horizontal interaction (peer interaction) among peers, which can have an effect on the symbolic value of brand (social distance). Findings: First, online brand community value (both cognitive and social value) has a positive impact on customer community engagement. Second, customer community engagement has a positive impact on customers’ brand recommend intention. Third, the customer community engagement is a mediator between the online brand community value and the customer brand recommend intention. Most importantly, fourth, the symbolic value of the brand controls the relationship between community value and customer community engagement. For brands with high symbolic value, the community value should emphasize cognitive value rather than social value. For brands with a low symbolic value, the community provides cognitive or social value, which is not affected by the symbolism of the brand. Recommendations for Practitioners: Practitioners can share best practices with the corporate sectors. Brand owners can work with researchers to explore the characteristics of their online brand communities. On this basis, brand owners and researchers can jointly build and manage online brand communities. Recommendation for Researchers: Researchers can explore different perspectives and factors of brand symbolism that involve brand owners when establishing an online brand community to advance consumer engagement, community value, and brand symbolism. Impact on Society: Online brand community is relevant for brand owners to establish brand symbolism, community value, and customer engagement. Readers of this paper can gain an understanding that cognitive and social values are two important drivers of individual participation in online brand communities. The discussion of these two factors can give readers and brand owners the perception to gain more understanding on social and behavior activities in online brand communities. Future Research: Practitioners and researchers could follow-up in the future with a study to provide more understanding and updated research information from different perspectives of research samples and hypotheses on online brand community.

Aim/Purpose: This paper presents a study of Virtual Communities of Practice (VCoP) evaluation methods that aims to identify their current status and impact on knowledge sharing. The purposes of the study are as follows: (i) to identify trends and research gaps in VCoP evaluation methods; and, (ii) to assist researchers to position new research activities in this domain. Background: VCoP have become a popular knowledge sharing mechanism for both individuals and organizations. Their evaluation process is complex; however, it is recognized as an essential means to provide evidences of community effectiveness. Moreover, VCoP have introduced additional features to face to face Communities of Practice (CoP) that need to be taken into account in evaluation processes, such as geographical dispersion. The fact that VCoP rely on Information and Communication Technologies (ICT) to execute their practices as well as storing artifacts virtually makes more consistent data analysis possible; thus, the evaluation process can apply automatic data gathering and analysis. Methodology: A systematic mapping study, based on five research questions, was carried out in order to analyze existing studies about VCoP evaluation methods and frameworks. The mapping included searching five research databases resulting in the selection of 1,417 papers over which a formal analysis process was applied. This process led to the preliminary selection of 39 primary studies for complete reading. After reading them, we select 28 relevant primary studies from which data was extracted and synthesized to answer the proposed research questions. Contribution: The authors of the primary studies analyzed along this systematic mapping propose a set of methods and strategies for evaluating VCoP, such as frameworks, processes and maturity models. Our main contribution is the identification of some research gaps present in the body of studies, in order to stimulate projects that can improve VCoP evaluation methods and support its important role in social learning. Findings: The systematic mapping led to the conclusion that most of the approaches for VCoP evaluation do not consider the combination of data structured and unstructured metrics. In addition, there is a lack of guidelines to support community operators’ actions based on evaluation metrics.

Table of Contents for Volume 15, 2020, of the Interdisciplinary Journal of Information, Knowledge, and Management

Aim/Purpose: This paper reports a case study of organizational transition from a non-competitive selection method to a novel bidding method for the selection of consultants in the Architectural and Engineering (A/E) industry. Background: Public procurement agencies are increasingly relying on external consultants for the design of construction projects. Consultant selection can be based on either competitive bidding, or quality-based criteria, or some combination between these two approaches. Methodology: Different sources of information were reviewed: internal documents, and quantitative data from the enterprise software platform (ERP). In addition, informal and unstructured interviews were conducted with relevant officials. Contribution: As there are mixed opinions in the scientific literature regarding the use of competitive bidding for the selection of consultants in the A/E industry, this paper contributes a detailed review of a transition to a competitive selection method and provides a financial and qualitative comparison between the two methods. In addition, the method implemented is novel, as it delegates most of the responsibility of hiring and managing consultants to one main contractor. Findings: While the new selection method was intended to reduce bureaucratic overload, it has unexpectedly also succeeded to reduce costs as well. Recommendations for Practitioners: It may be more efficient and profitable to adopt the selection method described in this study. Recommendation for Researchers: Similar methods can be applied to other industries successfully. Impact on Society: Our method was applied in a public organization and resulted in a better outcome, both financial and managerial. Adopting this approach can benefit public budgets. Future Research: The selection, data storage, and analysis methods are interrelated components. Future analysis of these components can help better shape the consultant selection process.

Aim/Purpose: This study aims at investigating the factors that influence students’ continuous intention to use Moodle, as an exemplar of learning management systems (LMSs), in the post-adoption phase. Background: Higher education institutions (HEIs) have invested heavily in learning management systems (LMSs), such as Moodle and BlackBoard, as these systems enhance students’ learning and improve their interactions with the educational systems. While most studies on LMSs have focused on the pre-adoption or acceptance phases of this technology, the determinant factors that influence students’ continuance intention to use LMSs have received less attention in the information systems (IS) literature. Methodology: The theoretical model for this study was primarily drawn from the expectation-confirmation model (ECM). A total of 387 Kuwaiti students, from a private American University in the State of Kuwait, participated in this study. Partial least squares (PLS) was employed to analyze the data. Contribution: This study contributes to the existing scientific knowledge in different ways. First, this study extends the expectation confirmation model (ECM) by integrating factors that are important to students’ continuous intention to use LMSs, including system interactivity, effort expectancy, attitude, computer anxiety, self-efficacy, subjective norms, and facilitating conditions. Second, this study adds on a Kuwaiti literature context by focusing on the continuous intention to use LMSs, which is, to the best of our knowledge, the first study that extends and empirically assesses the applicability of the ECM in the LMSs context in a developing country – Kuwait. Third, this study conceptually and empirically differentiates between satisfaction and attitude, as two separate affect constructs, which were taken as interchangeable factors in ECM, and were disregarded by a large number of prior ECM studies concerned with continuous use intention. Finally, this study aims to assist HEIs, faculty members, and systems’ developers in understanding the main factors that influence students’ continuance use intention of LMSs. Findings: While subjective norms were not significant, the results mainly showed that students’ continuous intention to use Moodle is significantly influenced by performance expectancy, effort expectancy, attitude, satisfaction, self-efficacy and facilitating conditions. The study’s results also confirmed that satisfaction and attitude are two conceptually and empirically different constructs, conflicting with the views that these constructs can be taken as interchangeable factors in the ECM. Recommendations for Practitioners: This study offers several useful practical implications. First, given the significant influence of system interactivity on performance expectancy and satisfaction, faculty members should modify their teaching approach by enabling communication and interaction among instructors, students, and peers using the LMS. Second, given the significant influence of performance expectancy, satisfaction, and attitude on continuous intention to use the LMS, HEIs should conduct training programs for students on the effective use of the LMS. This would increase students’ awareness regarding the usefulness of the LMS, enhance their attitude towards the LMS, and improve their satisfaction with the system. Third, given the significant role of effort expectancy in influencing performance expectancy, attitude, and students’ continuous intention to use Moodle, developers and system programmers should design the LMS with easy to use, high quality, and customizable user interface. This, in turn, will not only motivate students’ performance expectancy, but will also influence their attitude and continuous intention to use the system. Recommendation for Researchers: This study conceptually and empirically differentiates between satisfaction and attitude, as two separate affect constructs, which were taken as interchangeable factors in ECM and were disregarded by a large number of prior ECM studies concerned with continuous use intention. Hence, it is recommended that researchers include these two constructs in their research models when investigating continuous intention to use a technology. Impact on Society: This study could be used in other countries to compare and verify the results. Additionally, the research model of this study could also be used to investigate other LMSs, such as Blackboard. Future Research: This study focused on how different factors affected students’ continuous intention to use Moodle but did not consider all determinants of successful system, such as system quality, information quality, and instructional as well as course content quality. Thus, future research should devote attention to the effects of these quality characteristics of LMS.

Aim/Purpose: The main aim of this study is to identify the factors that influence the continuance intention of use of innovative systems by non-academic employees of a private university and associated academic institutions in Bangladesh. Background: The targeted academic institutions have introduced many new online services aimed at improving students’ access to information and services, including a new online library, ERP or online forum, and the jobs-tracking system (JTS). This research is focused only on the JTS for two reasons. First, it is one of the most crucial systems for the Daffodil Family, as it enables efficient working across many institutes spread across the country and abroad. Second, it is employed in a wide variety of organisational institutes, not just the university. This study aims to discover negative factors that lead to a decrease in users’ intentions to continue using the system. The ultimate goal is to improve the motivation among administrative staff to use technology-related innovation by reducing or eliminating the problems. Methodology: G* power analysis was employed to determine the expected sample size. A questionnaire survey was conducted of 211 users of a new job tracking system from a private university in Bangladesh, to collect data for testing the suggested research model. The data was analysed using the structural equation technique, which is a powerful multivariate analysis mechanism. Contribution: This research contributes to the body of literature and helps better understand users’ continuance intention in the post-implementation phase of the JTS. It complements the micro-level examinations of continuance intention of using IT, by building on our understanding of the phenomenon at the individual level. Specifically, this study examines the role of technostress where organisations invest in IT to make their users more comfortable with innovative and new technologies like the JTS. Findings: This research develops a theoretical advancement of the expectation-confirmation theory, with implications for IT managers and senior management dealing with IT-related behaviour. All proposed hypotheses were supported. Specifically, the predictors of exhaustion – work overload, work–life balance, and role ambiguity – are significant. The core factors for satisfaction, perceived usefulness, and confirmation, are also found to be significant. Finally, satisfaction and exhaustion significantly influence continuance intention, in both positive and negative ways. Recommendations for Practitioners: This study gives an idea about some of the difficulties that people face when implementing new and innovative IT, particularly in academia in Bangladesh. It offers insights into strategies the management may want to follow when implementing new technology like the JTS. This study suggests strategies to increase satisfaction and reduce technostress among new users to enhance organisational support for change. Recommendation for Researchers: Methodologically, the study provides researchers about the technique that reduces the threat of the common method bias. First, it created a psychological separation between criterion and predictor variables. Second, the threat of common method variance was actively controlled by modelling a latent method factor and by using marker variables that researchers can use in their work. This study complements the micro-level examinations of continuance intention of using IT by building on our understanding of the phenomenon at the individual level. Researchers can extend this model by integrating other theories. Impact on Society: The findings of the study indicate that work overload, work–life conflict, and role ambiguity create tiredness, leading to lower user satisfaction with the system. Perceived usefulness and confirmation have an increasingly similar effect on users’ satisfaction with the system and their subsequent continuance intention. These findings tell university administrators what measures they should take to improve continuance intention of using innovative technology. Future Research: Future studies could conceptualise a five-factor personality model from the personal perspective of users. This model can also be extended by including the dimensions of absorptive capacity, i.e., the dynamic capabilities of users. Absorptive capacity of understanding, assimilating, and applying might influence the user’s perception of usefulness and confirmation of using JTS.

Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.

Aim/Purpose: The aim of this paper is to propose a new information security controls framework for blockchain technology, which is currently absent from the National and International Information Security Standards. Background: Blockchain technology is a secure and relatively new technology of distributed digital ledgers, which is based on inter-linked blocks of transactions, providing great benefits such as decentralization, transparency, immutability, and automation. There is a rapid growth in the adoption of blockchain technology in different solutions and applications and within different industries throughout the world, such as finance, supply chain, digital identity, energy, healthcare, real estate, and the government sector. Methodology: Risk assessment and treatments were performed on five blockchain use cases to determine their associated risks with respect to security controls. Contribution: The significance of the proposed security controls is manifested in complementing the frameworks that were already established by the International and National Information Security Standards in order to keep pace with the emerging blockchain technology and prevent/reduce its associated information security risks. Findings: The analysis results showed that the proposed security controls herein can mitigate relevant information security risks in blockchain-based solutions and applications and, consequently, protect information and assets from unauthorized disclosure, modification, and destruction. Recommendations for Practitioners: The performed risk assessment on the blockchain use cases herein demonstrates that blockchain can involve security risks that require the establishment of certain measures in order to avoid them. As such, practitioners should not blindly assume that through the use of blockchain all security threats are mitigated. Recommendation for Researchers: The results from our study show that some security risks not covered by existing Standards can be mitigated and reduced when applying our proposed security controls. In addition, researchers should further justify the need for such additional controls and encourage the standardization bodies to incorporate them in their future editions. Impact on Society: Similar to any other emerging technology, blockchain has several drawbacks that, in turn, could have negative impacts on society (e.g., individuals, entities and/or countries). This is mainly due to the lack of a solid national and international standards for managing and mitigating risks associated with such technology. Future Research: The majority of the blockchain use cases in this study are publicly published papers. Therefore, one limitation of this study is the lack of technical details about these respective solutions, resulting in the inability to perform a comprehensive risk identification properly. Hence, this area will be expanded upon in our future work. In addition, covering other standardization bodies in the area of distributed ledger in blockchain technology would also prove fruitful, along with respective future design of relevant security architectures.

Aim/Purpose: The study attempts to analyse the influences of knowledge management capacity on company performance and supply chain practices. It also examines whether supply chain practices significantly and positively impact company performance. Background: Knowledge management capacity is an essential tactical resource that enables the integration and coordination among supply chain stakeholders, but research examining the link between knowledge management capacity and supply chain practices and their impacts on company performance remains scarce. Methodology: The study uses correlation analysis and factor analysis to confirm the theoretical framework’s validity and structural equation modelling to test hypotheses. The data are obtained from 115 halal food firms in China (with a response rate of 82.7%). Contribution: This study’s findings contribute to the Social Capital Theory by presenting the impacts of different supply chain practices on company performance. The findings also suggest the impact of intangible resources on enhancing company performance, contributing to the Resource-based View Theory. These results are a crucial contribution to both academicians and corporate managers working in the Halal food industry. Managers can apply these findings to discover and adopt knowledge management capacity with practical anticipation that these concepts will align with their company strategies. Also, the research motivates managers to concentrate their knowledge management on enhancing companies’ supply chain practices to achieve improved company performance. Findings: This study is an initial effort that provides empirical evidence regarding the relationships among supply chain, knowledge management, and company performance from the perspective of China’s halal food industry. The results prove that knowledge management capacity is the supply chains’ primary success determinant and influencer. Besides, knowledge management capacity positively influences company performance, and supply chain practices directly influence company performance. Recommendations for Practitioners: Managers can apply these study findings to determine and increase knowledge management capacity with practical anticipation that these concepts will align with their company strategies. Also, the research motivates managers to concentrate their knowledge management on enhancing companies’ supply chain practices to achieve improved company performance. Recommendation for Researchers: The study presents a new theoretical framework and empirical evidence for surveying halal food businesses in China. Impact on Society: These results are a significant contribution to the research field and industry focusing on halal foods. Future Research: First, this research focuses only on halal food businesses in China; thus, it is essential to re-examine the hypothesized relations between the constructs in other Chinese business segments and regions. Next, the effect of variables and practices on the theorized framework should be taken into account and examined in other industries and nations.

Aim/Purpose: This study aims to propose and empirically validate a model and investigates the factors influencing acceptance and use of Software as a Services cloud computing services (SaaS) from individuals’ perspectives utilizing an integrative model of Theory of Planned Behavior (TPB) and Technology Acceptance Model (TAM) with modifications to suit the objective of the study. Background: Even though SaaS cloud computing services has gained the acceptance in its educational and technical aspects, it is still expanding constantly with emerging cloud technologies. Moreover, the individual as an end-user of this technology has not been given the ample attention pertaining to SaaS acceptance and adoption (AUSaaS). Additionally, the higher education sector needs to be probed regarding AUSaaS perception, not only from a managerial stance, but also the individual. Hence, further investigation in all aspects, including the human factor, deserves deeper inspection. Methodology: A quantitative approach with probability multi-stage sampling procedure conducted utilizing survey instrument distributed among students from three public Malaysian universities. The valid collected responses were 289 Bachelor’s degree students. The survey included the demographic part as well as the items to measure the constructs relationships hypothesized. Contribution: The empirical results disclosed the appropriateness of the integrated model in explaining the individual’s attitude (R2 = 57%), the behavior intention (R2 = 64%), and AUSaaS at the university settings (R2 = 50%). Also, the study offers valuable findings and examines new relationships that considered a theoretical contribution with proven empirical results. That is, the subjective norms effect on attitude and AUSaaS is adding empirical evidence of the model hypothesized. Knowing the significance of social effect is important in utilizing it to promote university products and SaaS applications – developed inside the university – through social media networks. Also, the direct effect of perceived usefulness on AUSaaS is another important theoretical contribution the SaaS service providers/higher education institutes should consider in promoting the usefulness of their products/services developed or offered to students/end-users. Additionally, the research contributes to the knowledge of the literature and is considered one of the leading studies on accepting SaaS services and applications as proliferation of studies focus on the general and broad concept of cloud computing. Furthermore, by integrating two theories (i.e., TPB and TAM), the study employed different factors in studying the perceptions towards the acceptance of SaaS services and applications: social factors (i.e., subjective norms), personal capabilities and capacities (i.e., perceived behavioral control), technological factors (i.e., perceived usefulness and perceived ease of use), and attitudinal factors. These factors are the strength of both theories and utilizing them is articulated to unveil the salient factors affecting the acceptance of SaaS services and applications. Findings: A statistically positive significant influence of the main TPB constructs with AUSaaS was revealed. Furthermore, subjective norms (SN) and perceived usefulness (PU) demonstrated prediction ability on AUSaaS. Also, SN proved a statically significant effect on attitude (ATT). Specifically, the main contributors of intention are PU, perceived ease of use, ATT, and perceived behavioral control. Also, the proposed framework is validated empirically and statistically. Recommendation for Researchers: The proposed model is highly recommended to be tested in different settings and cultures. Also, recruiting different respondents with different roles, occupations, and cultures would likely draw more insights of the results obtained in the current research and its generalizability Future Research: Participants from private universities or other educational institutes suggested in future work as the sample here focused only on public sector universities. The model included limited number of variables suggesting that it can be extended in future works with other constructs such as trialability, compatibility, security, risk, privacy, and self-efficacy. Comparison of different ethnic groups, ages, genders, or fields of study in future research would be invaluable to enhance the findings or reveal new insights. Replication of the study in different settings is encouraged.

Aim/Purpose: The aim of this study is to develop a research model derived from relevant literature to guide empirical efforts. Background: Companies struggle to innovate, which is essential for improving their performance, surviving in competition, and growing. A number of studies have discussed company innovation capability, stating that innovation capability is influenced by several variables such as cooperation and knowledge management. Therefore, further research is necessary to identify factors playing a role in enhancing innovation capability. Methodology: This study is based on systematic literature review. The stages are: (1) research scope review, (2) comprehensive online research, (3) journal quality assessment, (4) data extraction from journals, (5) journal synthesis, and (6) comprehensive report. The online research used Google Scholar database, by browsing titles, abstracts, and keywords to locate empirical research studies in peer-reviewed journals published in 2010-2020. Furthermore, 62 related articles were found, of which 38 articles were excluded from further analysis and 24 articles were selected because they were more related to the topic. Contribution: The results of this study enrich the research in the field of knowledge management, cooperation, and innovation capability by developing a conceptual framework of innovation capability. The proposed theoretical model may be fundamental in addressing the need of a research model to guide further empirical efforts. Findings: This study provides a research model derived from systematically reviewing relevant literature. The proposed theoretical model was done by incorporating the aspects of knowledge management, cooperation, and innovation capability. The model shows that knowledge management and cooperation are essential aspects of innovation capability. Furthermore, this study also provides the dimensions and sub dimensions of each variable that was established after synthesizing the literature review. Recommendations for Practitioners: Business practitioners can use the identified predictors of innovation capability and the dimensions of each variable to explore their company’s innovation capability. They can also take the relevant variables into consideration when making policies regarding innovation. Recommendation for Researchers: The theoretical model proposed in this study needs validation with further empirical investigation. Impact on Society: Readers of this paper can obtain an understanding that knowledge management and cooperation are essential aspects to consider in enhancing innovation capability. Future Research: Future studies should explore other dimensions of knowledge management and cooperation through alternative approaches and perspectives.

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

Aim/Purpose: In this work, the authors propose an augmented model for human-centered Unified Communications & Collaboration (UC&C) product design and evaluation, which is supported by previous theoretical work. Background: Although the goal of implementing UC&C in an organization is to promote and mediate group dynamics, increasing overall productivity and collaboration; it does not seem to provide a solution for effective communication. It is clear that there is still a lack of consideration for human communication processes in the development of such products. Methodology: This paper is sustained by existing research to propose and test the application of an augmented model capable of supporting the design, development and evaluation of UC&C services that can be driven by the human communication process. To test the application of the augmented model in UC&C service development, a proof-of-concept mobile prototype was elaborated upon and evaluated, making use of User Experience (UX) and user-centred methods and techniques. A total of nine testing sessions were carried out in an organizational communication setup and recorded with eye tracking technology. Contribution: The authors argue that UC&C services should look at the user’s (human) natural processes to improve effective infocommunication and thus enhance collaboration. Authors believe this augmented version of the model will pave the way improving the research and development of useful and practical infocommunication products, capable of truly serving users’ needs. Findings: On evaluation of the prototype, qualitative data analysis uncovered structural problems in the proposed prototype which hindered the augmented model’s elements and subsequently, the user experience. Five out of eighteen identified interaction issues are highlighted in this paper to demonstrate the proposed augmented model’s validity, applied in UC&C services evaluation. Recommendations for Practitioners: Considering and respecting the user’s natural communication processes, practitioners should be able to propose and develop innovative solutions that truly enable and empower effective organizational collaboration. UC&C functionalities should be designed, taking the augmented model’s proposed elements and their pertinence in representing the human interpersonal communication phenomena into consideration, namely: Social Presence; Immediacy of Communication; Concurrency and Synchronicity. Recommendation for Researchers: This paper intends to demonstrate that the adoption and use of UTAUT technology characteristics, in conjunction with Synchronicity proposition, can be considered as a reference for human-centric design and the evaluation of UC&C systems. Impact on Society: To highlight the need to develop further research on this important topic of human collaboration mediated by technology inside organizations. Future Research: This research focused its attention on communication functionalities. However, collaboration can potentially be affected by other services that may be included in a UC&C system, such as scheduling, meetings or task management. Future research could consider employing this augmented model to evaluate such systems or proof-of-concept prototypes.

Aim/Purpose: The need to explore leaders’ role as a mediating factor between knowledge sharing and firms’ competitiveness was the focus of this paper. Further, gaps related to knowledge sharing influence on firms’ competitiveness from an emerging economy perspective was a major driver of this study. Background: The relevance of knowledge sharing is today crucial for firms that seek to harness internal resource innovation towards ensuring increased competitiveness. The link between the actions of leaders and outcomes from sharing knowledge towards increased competitiveness would further advance theory on knowledge sharing and provide managerial implication that is instrumental for an improved organisational outcome. Methodology: The study sample was 282 participants and Partial least square structural equation model was used for the analysis of the data obtained through a questionnaire survey with the aid of SmartPLSv3.9. Contribution: The study contributes to knowledge management literature through advancing leadership as a mediating factor that accounts for the link between knowledge sharing and firms’ competitiveness, most especially from an emerging economy perspective. Findings: Knowledge sharing was found to have a positive effect on firms’ competitiveness. The study found that leadership behaviour mediates the relationship between knowledge sharing and a firm’s competitiveness. Recommendations for Practitioners: The study recommends that, when supported with the right attitude from leaders in the organisation, knowledge sharing will be beneficial towards the firm gaining competitiveness most especially. Future Research: Future studies should be carried out in other sectors aside from the manufacturing sector using the same measures used to measure knowledge sharing. Also, a comparative analysis of knowledge sharing and firms’ competitiveness using leaders’ behaviour as a mediator should be researched in other developing economies.

Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.

Table of Contents for Volume 16, 2021, of the Interdisciplinary Journal of Information, Knowledge, and Management

Aim/Purpose: This study investigated a model of mobile esports acceptance among urban citizens based on an extended Technology Acceptance Model (TAM). Background: Currently, esports are increasingly popular and in demand by the public. Supported by the widespread development of mobile devices, it has become an interactive market trend to play games in a new model, mobile esports. Methodology: This study collected data from 400 respondents and analyzed it using partial least squares-structural equation modeling (PLS-SEM). Contribution: This study addresses two research gaps. The first gap is limited esports information systems studies, particularly in mobile esports acceptance studies. The second gap is limited exploration of external variables in online gaming acceptance studies. Thus, this study proposed a TAM extended model by integrating the TAM native variables with other external variables such as visual appeal, enjoyment, social interaction, and competition to explore mobile esports acceptance by urban citizens. Findings: Nine hypotheses were accepted, and four were rejected. The visual appeal did not affect the acceptance. Meanwhile, social interaction and enjoyment significantly affected both perceived ease of use and usefulness. However, perceived ease of use surprisingly had an insignificant effect on attitude toward using mobile esports. Moreover, competition significantly affected the acceptance, particularly on perceived usefulness. Recommendations for Practitioners: Fresh and innovative features, such as new game items or themes, should be frequently introduced to enhance players’ continued enjoyment. Moreover, mobile esports providers should offer a solid platform to excite players’ interactions to increase the likelihood that users feel content. On the other hand, the national sports ministry/agency or responsible authorities should organize many esports competitions, big or small, to search for new talents. Recommendation for Researchers: Visual appeal in this study did not influence the perceived ease of use or usefulness. However, it could affect enjoyment. Thus, it would be worth revisiting the relationship between visual appeal and enjoyment. At the same time, perceived ease of use is a strong driver for the continued use of most online games, but not in this study. It could indicate significant differences between mobile esports and typical online games, one of which is the different purposes. Users might play online games for recreational intention, but players would use mobile esports to compete, win, or even get monetary rewards. Therefore, although users might find mobile esports challenging and hard to use, they tend to keep playing it. Thus, monetary rewards could be considered a determinant of the continuation of use. Impact on Society: Nowadays, users are being paid for playing games. It also would be an excel-lent job if they become professional esports athletes. This study investigated factors that could affect the continued use of mobile esports. Like other jobs, playing games professionally in the long term could make the players tedious and tired. Therefore, responsible parties, like mobile esports providers or governments, could use the recommendations of this study to promote positive behavior among the players. They will not feel like working and still con-sider playing mobile esports a hobby if they happily do the job. In the long run, the players could also make a nation’s society proud if they can be a champion in prestigious competitions. Future Research: A larger sample size will be needed to generalize the results, such as for a nation. It is also preferable if the sample is randomized systematically. Future works should also investigate whether the same results are acquired in other mobile esports. Furthermore, to extend our knowledge and deepen our understanding of the variables that influence mobile esports adoption, the subsequent research could look at other mobile esports acceptability based on characteristics of system functionality and moderator effects. Finally, longitudinal data-collecting approaches are suggested for future studies since behavior can change over time.

Aim/Purpose: This study focuses on the connection between IT-producing firms’ digital service capabilities and the digital service performance of IT-consuming firms, especially online shop operators. Background: The acquisition and integration of knowledge regarding digital service capabilities and performance can increase the level at which employees assimilate information, organize with IT-consuming firms, and cooperate with them to develop the delivery of services and customize services to fill their needs. Exploring capabilities that may enable this process is a prerequisite for all businesses offering digital services and, thus, an engrossing and ongoing interest of practitioners and scholars. However, there is a lack of research on the relationship between IT-producing firms’ digital service capabilities and the digital service performance of IT-consuming firms in the business-to-business (B2B) context. Methodology: The study builds on a survey conducted among small firms that have an online shop in use and are located in Finland. Contribution: The study offers empirical evidence for the capabilities valued by IT-consuming firms, providing a model for IT-producing firms to use when deciding on a future focus. The study was executed in a B2B setting from the viewpoint of online shop operators, presenting a novel understanding of influential digital service capabilities. Findings: Adaptability, determined by capabilities related to utilizing information gained via the integration of a digital product into other digital tools (e.g., marketing, personalization, and analytics), statistically significantly affects all three aspects of an IT-consuming firm’s digital service performance (financial, operational, and sales). Another product capability, availability, which includes aspects such as security, different aspects of functioning, and mobile adaptation, affects one aspect of digital performance, namely operational. The results also suggest that the role of service process-related capabilities in determining service comprehensiveness significantly influences two aspects of IT-consuming firms’ digital service performance: financial (negative effect) and operational (positive effect). The results show that the capabilities associated with the relationship between the producing firm and the consuming firm do not affect IT-consuming firms’ performance to the same extent. Recommendations for Practitioners: The study results suggest that IT-producing firms should concentrate on leveraging service comprehensiveness, as there has been a shift in the B2B context from merely selling a digital product and associated services. It seems that usability-related issues are now taken for granted, and the emphasis is on features that support the use of information to create value. Recommendation for Researchers: The results contribute to the capabilities literature by showing that the shift in focus from technical product-related capabilities to relationship-related capabilities is not yet evident among small online store operators. Impact on Society: In addition to offering tools with different integration possibilities, supporting IT-consuming firms in making the most of the possibilities would be very helpful. Future Research: The comprehension of the relationship between digital service capabilities and digital service performance would benefit from future research that takes into account additional control variables. The theoretical model of this study can be further studied by using other performance measures, such as market performance, as dependent variables.

Aim/Purpose: This study examined the relationship between critical success factors (CSFs), perceived benefits, and usage intention of Mobile Knowledge Management Systems (MKMS) via an integrated Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM). Background: This study investigates the CSFs (i.e., Strategic Leadership, Employee Training, System Quality, and Information Quality) that impact the usage intention of KMS in mobile contexts which have been neglected. Since users normally consider the usefulness belief in a system before usage, this study examines the role of perceived benefits as a mediator between the CSFs and usage intention. Methodology: A survey-based research approach in the Malaysian semiconductor industry was employed via an integrated model of TAM and ISSM. At a response rate of 59.52%, the findings of this study were based on 375 usable responses. The data collected was analyzed using the Partial Least Squares with SmartPLS 3.0. Contribution: This study contributes to the body of knowledge in the areas of mobile technology acceptance and knowledge management. Specifically, it helps to validate the integrated model of TAM and ISSM with the CSFs from knowledge management and information system. In addition, it provides the would-be adopters of MKMS with valuable guidelines and insights to consider before embarking on the adoption stage. Findings: The findings suggest that Employee Training and Information Quality have a positive significant relationship with Perceived MKMS Benefits. On the contrary, Strategic Leadership, System Quality, and Perceived User-friendliness showed an insignificant relationship with Perceived MKMS Benefits. Additionally, Employee Training and Information Quality have an indirect relationship with MKMS Usage Intention which is mediated by Perceived MKMS Benefits. Recommendations for Practitioners: The findings are valuable for managers, engineers, KM practitioners, KM consultants, MKMS developers, and mobile device producers to enhance MKMS usage intention. Recommendation for Researchers: Researchers would be able to conduct more inter-disciplinary studies to better understand the relevant issues concerning both fields – knowledge management and mobile computing disciplines. Additionally, the mediation effect of TAM via Perceived Usefulness (i.e., perceived MKMS benefits) on usage intention of MKMS should be further investigated with other CSFs. Future Research: Future studies could perhaps include other critical factors from both KM and IS as part of the external variables. Furthermore, Perceived Ease of Use (i.e., Perceived User-friendly) should be tested as a mediator in the future, together with Perceived Usefulness (i.e., perceived MKMS Benefits) to compare which would be a more powerful predictor of usage intention. Moreover, it may prove interesting to find out how the research framework would fit into other industries to verify the findings of this study for better accuracy and generalizability.

Aim/Purpose: Currently, the world faces unprecedented challenges due to COVID-19, particularly concerning individuals’ health and livelihood and organizations and industrial performance. Indeed, the pandemic has caused rapid intensifying socio-economic effects. For instance, organizations are shifting from traditional working patterns toward telecommuting. By adopting remote working, organizations might mitigate the impact of COVID-19 on their workforce, explicitly concerning their safety, wellbeing, mobility, work-life balance, and self-efficiency. From this perceptive, this study examines the factors that influence employees’ behavioral intention to adopt telecommuting in the banking industry. Background: The study’s relevance stems from the fact that telecommuting and its benefits have been assumed rather than demonstrated in the banking sector. However, the pandemic has driven the implementation of remote working, thereby revealing possible advantages of working from home in the banking industry. The study investigated the effect of COVID-19 in driving organizations to shift from traditional working patterns toward telecommuting. Thereby, the study investigates the banking sector employees’ behavioral intention to adopt telecommuting. Methodology: The study employed a survey-based questionnaire, which entails gathering data from employees of twelve banks in Jordan, as the banking sector in Jordan was the first to transform from traditional working to telecommuting. The sample for this research was 675 respondents; convenience sampling was employed as a sampling technique. Subsequently, the data were analyzed with the partial least square structural equation modeling (PLS-SEM) to statistically test the research model. Contribution: Firstly, this study provides a deep examination and understanding of facilitators of telecommuting in a single comprehensive model. Secondly, the study pro-vides a deeper insight into the factors affecting behavioral intention towards telecommuting from the employees’ perspective in the banking sector. Finally, this study is the first to examine telecommuting in the emerging market of Jordan. Thereby, this study provides critical recommendations for managers to facilitate the implementation of telecommuting. Findings: Using the Technology Acceptance Model (TAM), this study highlights significant relationships between telecommuting systems, quality, organizational support, and the perceived usefulness and ease of use in telecommuting. Employees who perceive telecommuting systems to be easy and receive supervision and training for using these systems are likely to adopt this work scheme. The results present critical theoretical and managerial implications regarding employees’ behavioral intentions toward telecommuting. Recommendations for Practitioners: This study suggests the importance of work-life balance for employees when telecommuting. Working from home while managing household duties can create complications for employees, particularly parents. Therefore, flexibility in terms of working hours is needed to increase employees’ acceptance of telecommuting as they will have more control over their life. These increase employees’ perceived self-efficacy with telecommuting, which smooths the transition toward remote working in the future. In addition, training will allow employees to solve technical issues that can arise from using online systems. Recommendation for Researchers: This study focused on the context of the banking sector. The sensitivity of data and transactions in this sector may influence employers’ and employees’ willingness to work remotely. In addition, the job descriptions of employees in banks moderate specific factors outlined in this model, including work-life balance. For instance, executive managers may have a higher overload in banks in contrast to front-line employees. Thus, future studies should explore different contexts, including manufacturing and consultation, to understand the industry’s effect on remote working. Similarly, future research should concentrate on the influence of job descriptions on employees’ intentions toward telecommuting. Impact on Society: The COVID-19 pandemic created a sudden shift towards telecommuting, which made employees struggle to adopt new work schemes. Therefore, managers had to provide training for their employees to be well prepared and increase their acceptance of telecommuting. Furthermore, telecommuting has a positive effect on work-life balance, it provides employees with the flexibility to organize their daily schedule into more activities. Along the same line, the study highlighted the correlation between work-life balance and telecommuting. Such a relationship provides further evidence for the need to understand employees’ lifestyles in facilitating the adoption of telecommuting. Moreover, the study extends the stream of literature by outlining critical factors affecting employees’ acceptance of telecommuting. Future Research: Future studies should explore different contexts, including manufacturing and consultation, to understand the industry’s effect on remote working. Similarly, future research should concentrate on the influence of job descriptions on employees’ intentions toward telecommuting. Furthermore, the research team conducted the study by surveying 12 banks. Future research recommends surveying the whole banking industry to add more validation to the model.

Aim/Purpose: This study identifies the elements that influence intentions to purchase from the most popular Arabic online classifieds platform, OpenSooq.com. Background: Online purchasing has become popular among consumers in the past two decades, with perceived risk and trust playing key roles in consumers’ intention to purchase online. Methodology: A questionnaire survey was conducted of Internet users from three Jordanian districts to investigate how they used the OpenSooq platform in their e-commerce activities. In total, 202 usable responses were collected, and the data were analyzed with PLS-SEM for hypothesis testing and model validation. Contribution: Though online trading is increasingly popular, the factors that impact the behavior of consumers when purchasing high-value products have not been adequately investigated. Therefore, this study examined the factors affecting perceived risk, and the potential impact of privacy concerns on the perceived risk of online smartphone buyers. The study framework can help explore online behavior in various situations to ascertain similarities and differences and probe other aspects of online buying. Findings: Perceived risk negatively correlates with online purchasing behavior and trust. However, privacy concern and perceived risk, transaction security and trust, and trust and online purchasing behavior exhibited positive correlations. Recommendations for Practitioners: Customers can complete and retain online purchases in a range of settings illuminated in this study’s methods and procedures. Moreover, businesses can manage their IT arrangements to make Internet shopping more convenient and build processes for online shopping that allow for engagement, training, and ease of use, thus improving their customers’ online purchasing behavior. Recommendation for Researchers: Given the insight into the understanding and integration of variables including perceived risk, privacy issues, trust, transaction security, and online purchasing behavior, academics can build on the groundwork of this research paradigm to investigate underdeveloped countries, particularly Jordan, further. Impact on Society: Understanding the characteristics that influence online purchasing behavior can help countries realize the full potential of online shopping, particularly the benefits of safe, fast, and low-cost financial transactions without the need for an intermediary. Future Research: Future research can examine the link between online purchase intent, perceived risk, privacy concerns, trust, and transaction security to see if the findings of this study in Jordan can be applied to a broader context in other countries.

Aim/Purpose: This study aims to examine the roles of key traits of m-university services and their users in promoting two crucial post-adoption outcomes of these services; namely, continuance usage intention and perceived value. Background: M-university (i.e., a university providing services via mobile technologies) has gained a great interest in the higher education sector as a driver of new business models and innovative service offerings. However, its assessment has been greatly overlooked, especially in evaluating the factors that drive the stakeholders’ continuance intention to use it and the determinants of its post-adoption perceived value. Consequently, research efforts undertaking such assessment facets empirically are highly required. Methodology: An integrated research model that enables such assessment was developed and evaluated using a quantitative research methodology. Accordingly, data were collected using a formulated closed-ended survey questionnaire. The target population consisted of the academic staff of a Saudi public university that has witnessed an extensive adoption of m-university services. The obtained data (i.e., 207 fully completed responses) were evaluated using the structural equation modeling approach. Contribution: To the best of our knowledge, this is the first study that gains the chance to provide the research community and m-service providers with new knowledge and understanding about the predictors that drive the continuance usage intention and value of m-university services. Findings: The findings showed that all of the examined traits of m-university services and their users (i.e., reliability, usability, customization, self-efficacy, and involvement) are having positive roles in promoting the continuance intention to use these services, while only two traits (i.e., reliability and involvement) contribute significantly to augmenting the perceived value. Recommendations for Practitioners: The study recommends developing effective design and implementation specifications that strengthen the contributions of the examined traits in the post-adoption stage of m-university services. Recommendation for Researchers: Further studies should be devoted to addressing the notable need to assess the factors influencing the adoption of m-university services, as well as to explore which ones are having significant roles in the attainment of post-adoption outcomes. Impact on Society: The empirical insights provided by the present study are essential for both university stakeholders and mobile service providers in their endeavors to improve the key aspects of the anticipated post-adoption outcomes of the provided services. Future Research: Further empirical investigations are needed to examine the roles of more m-university services and user traits in achieving a broad range of post-adoption outcomes of such services.

Aim/Purpose: Mobile commerce adoption is low in developing countries; hence, public servants may not consider mobile commerce and mobile payments. Understanding the factors that influence mobile commerce and mobile payments in their context will aid in promoting those services. Background: The study investigates the factors that influence public servants’ mobile commerce and mobile payments in Ghana. Hence, it provides some understanding of the various aspects of mobile commerce and mobile payments adoption, such as acceptance, use, and eventual adoption into the user’s daily life, and how that affects their behaviour. Methodology: The research was conducted by surveying the factors influencing public servants’ adoption of mobile commerce and payments in Ghana. A cross-sectional survey was undertaken to put the research model to the test to measure the constructs and their relationships. Contribution: The study confirmed previous findings and created a new conceptual model for mobile commerce and mobile payment adoption and usage in the Ghanaian context. Findings: The variables of performance expectancy, trust, and facilitating conditions have a significant positive influence on behavioural intention. The factors of effort expectation and social influence have a significant negative impact. Price value and perceived reliability are latent variables that do not affect behavioural intention. Behavioural intention and facilitating conditions significantly influence the actual use behaviour of mobile commerce and mobile payment users. Recommendations for Practitioners: Mobile commerce is emerging as a new mode of transactions, with firms providing enabling platforms for users. Mobile commerce could become the most acceptable application for the next generation of mobile platform applications. This study offers insights into the fluidity of the mobile environment, with implications that spell out what will be effective mobile commerce services that will continue to be relevant. Mobile applications are attractive to people because they provide a better user experience. These mobile applications have been optimised to provide a fast, easy and delightful experience. Mobile commerce and mobile payment service providers can attract and retain more users if attention is paid to performance expectancy, trust, and facilitating conditions since they influence individuals’ decisions to adopt. Mobile technology is almost ubiquitous, influencing both online sales and in-store sales. With the right mobile commerce platform and features, businesses can expect to increase in-store and online sales, catering to a more extensive clientele. Mobile devices are the primary means that most customers use to look up information about products they see in stores, such as product reviews and pricing options. This study indicates that mobile commerce service providers can achieve a more extensive customer base by promoting performance expectancy, trust, and behavioural intentions. Recommendation for Researchers: Despite the numerous studies in the mobile commerce literature, few have used integrated models of perceived reliability, trust, and price value or methods to evaluate these factors in the emerging mobile commerce industry. Also, it combines mobile commerce and mobile payments, which very few that we know of have done. Impact on Society: Ghana is already in a cash-lite economy. Thus, the study is appropriate with the result of trust being a significant factor. It implies that people will begin using mobile commerce and mobile payments with a bit of drive to bring about this drive quickly. Future Research: Future research could further test the adapted model with moderating factors of age, gender, and education to delve deeper into the complexities of mobile commerce and mobile payments.

Aim/Purpose: This study aimed to explore the impact of environmental drivers and trust on consumers’ adoption of Fintech services in the Jordanian context. It had also evaluated the mediating role of trust on the relation between environmental drivers and consumers adoption of Fintech services. Background: The reviewed studies on Fintech adoption demonstrated a lack of focus on the role of external or environmental drivers on consumers’ intentions to use and continue to use of Fintech services. Amongst the analyzed studies, the majority had examined the role of consumers perception of services usefulness and ease of use while few had included some environmental variables within the investigated variables such as social influence and government support. Furthermore, shortage of Fintech adoption related research in the developing countries, especially the Jordanian context was noted. Methodology: The study conceptual model was derived from Social Cognitive Theory (SCT) and Technological Personal Environmental (TPE) framework. This study was a quantitative one that employed survey method to empirically address its research questions and test the proposed hypotheses. Jordanian residents over the age of 18 who are familiar with Fintech were targeted, and convenience sampling was applied to get representative sample. Data was assembled from 323 respondents using an online questionnaire. Partial Least Squares Structure Equation Modeling (PLS-SEM) was applied to analyze the gathered data through SMART-PLS software. Contribution: This article adds to the existing literature on multiple stands, as it adds to literature related to Fintech adoption, as well as the interaction between consumer environment and their level of adoption. It also enriches the limited literature on the influence of COVID-19 to drive consumer usage of innovative services. Moreover, it supplements the scarce literature on Fintech adoption in the Jordanian settings. Findings: The main findings revealed the positive influence of both environmental drivers and trust as predictors of consumer intention to use Fintech services. It had also asserted the positive mediating effect of trust on the relationship amongst environmental drivers and consumer usage intent. Recommendations for Practitioners: By understanding the importance of consumer environment and trust on encouraging consumer to adopt Fintech services, governments, policy makers and practitioners can utilize this knowledge to adopt their offered services. They need to work on enhancing the technological infrastructure, as well as establishing general technological knowledge. They also need to highlight the role of Fintech service in fighting Covid-19, by adhering to the social distancing rules. Moreover, they need to guarantee the security and reliability of the developed services to increase their level of trust in the offered services. Recommendation for Researchers: This research has confirmed the positive influence of consumer environment represented by social influence, government support, technological readiness, and COVID-19 on their adoption of Fintech services. It has also established the mediating influence of consumer trust on the relation between environmental drivers and consumer intent to use Fintech services. This area is unexplored and needs more validation. Impact on Society: By understanding the factors affecting the Jordanian society in adopting Fintech services, this research provides set of recommendation to the Jordanian government and policy makers that can lead for more adoption of the developed Fintech services, which in turn would lead to better services provided to the society as well as increasing the financial inclusion level in the Jordanian society. Future Research: Future research can explore other environmental variables that were not included in the current research. Future research can also investigate the moderating effect of personal attributes such as consumer’s demographics, or more personal attributes such as self-efficacy, inherit innovativeness or risk aversion. It can also examine the moderating effect of financial literacy and/ or technological background.

Aim/Purpose: This paper identified and explored the factors influencing Bitcoin adoption and use in South Africa. Background: Since its introduction in 2008, the value and popularity of Bitcoin has risen exponentially. Captivating the eyes of the world, from regulators to economists, Bitcoin promises to revolutionize the digital currency space. Despite being over 10 years old, the concept of cryptocurrency is fairly new in South Africa, a developing country. South African’s interest in Bitcoin continues to grow with the country constantly ranking within the top 10 in online searches for “Bitcoin” and “cryptocurrency” on Google. The primary objective of this research was to identify adoption factors amongst South African citizens, an area that has not received much research focus in the past. In addition to this, the study aimed to identify how Bitcoin is primarily used in South Africa. Methodology: A survey-based questionnaire was utilized to obtain responses from adopters of Bitcoin in South Africa. The quantitative survey was completed by 204 respondents. Contribution: This research contributes to the body of knowledge relating to Bitcoin adoption, specifically from a developing country. Adoption factors are identified that can be utilized by businesses that intend to adopt cryptocurrency, to strategically prepare for the potential risks or opportunities brought about by Bitcoin and cryptocurrency in general. Findings: The findings of this study indicate that while perceived usefulness, perceived ease of use, subjective norms, and facilitating conditions positively influence intention to adopt Bitcoin, trust was the only construct that is statistically significant and hence is the greatest driver of adoption in South Africa. In terms of its primary use in South Africa, the study revealed that Bitcoin is used as a speculative instrument for short-term trading in South Africa followed by being used as a long-term investment in the crypto-asset class. No respondent indicated that they utilize Bitcoin as a payment method in South Africa. Recommendations for Practitioners: When developing crypto-based investment products, custodians of assets must ensure that a minimum-security protocol is followed to safeguard these assets. This will enhance the trust that potential investors and customers have in their systems and products. Recommendation for Researchers: This study focused on adoption factors for South African citizens. Future studies should be conducted to identify adoption factors by businesses in South Africa. Impact on Society: Bitcoin offers an alternate trading instrument and investment option, with the possibility of large gains over a relatively short period. Bitcoin also presents the possibility of cross-border transactions at a significantly lower cost compared to traditional cross-border transfers of funds. Future Research: Studies should be conducted to explore the factors influencing the adoption of altcoins to determine if the technological differences influence the adoption of one currency over the other. Research should also be conducted comparing the taxation of cryptocurrency in various countries around the world.

Aim/Purpose: This paper aims to analyze how artisans in Ghana are incorporating mobile commerce into their everyday business and how perceived usefulness, perceived ease of use, subjective norms, age, gender, expertise, and educational level affected the adoption and usage of m-commerce. Background: This study integrates well-established theoretical models to create a new conceptual model that ensures a comprehensive mobile commerce adoption survey. Methodology: A cross-sectional survey was conducted to measure the constructs and their relations to test the research model. Contribution: The study’s findings confirmed previous results and produced a new conceptual model for mobile commerce adoption and usage. Findings: Except for gender, perceived ease of use, and subjective norms that did not have specific effects on mobile commerce adoption, age, educational level, perceived usefulness, expertise, attitude, and behavioral intention showed significant effects. Recommendations for Practitioners: First of all, mobile commerce service providers should strategically pay critical attention to customer-centered factors that positively affect the adoption of mobile commerce innovations than focusing exclusively on technology-related issues. Mobile service providers can attract more users if they carefully consider promoting elements like perceived usefulness and perceived ease of use which directly or indirectly affect the individuals’ decision to adopt information technology from consumer perspectives. Second, mobile commerce service providers should strategically focus more on younger individuals since, per the research findings, they are more likely to adopt mobile commerce innovations than the older folks in Ghana. Third, service providers should also devise strategies to retain actual users of m-commerce by promoting elements like behavioral intentions and attitude, which according to the research findings, have a higher predictive power on actual usage of m-commerce. Recommendation for Researchers: The conceptual model developed can be employed by researchers worldwide to analyze technology acceptance research. Impact on Society: The study’s findings suggested that mobile commerce adoption could promote a cashless society that is convenient for making buying things quicker and easier. Future Research: The research sample size could be increased, and also the study could all sixteen regions in Ghana or any other country for a broader representation.

Aim/Purpose: This paper aims to analyze the availability and pricing of perishable farm produce before and during the lockdown restrictions imposed due to Covid-19. This paper also proposes machine learning and deep learning models to help the farmers decide on an appropriate market to sell their farm produce and get a fair price for their product. Background: Developing countries like India have regulated agricultural markets governed by country-specific protective laws like the Essential Commodities Act and the Agricultural Produce Market Committee (APMC) Act. These regulations restrict the sale of agricultural produce to a predefined set of local markets. Covid-19 pandemic led to a lockdown during the first half of 2020 which resulted in supply disruption and demand-supply mismatch of agricultural commodities at these local markets. These demand-supply dynamics led to disruptions in the pricing of the farm produce leading to a lower price realization for farmers. Hence it is essential to analyze the impact of this disruption on the pricing of farm produce at a granular level. Moreover, the farmers need a tool that guides them with the most suitable market/city/town to sell their farm produce to get a fair price. Methodology: One hundred and fifty thousand samples from the agricultural dataset, released by the Government of India, were used to perform statistical analysis and identify the supply disruptions as well as price disruptions of perishable agricultural produce. In addition, more than seventeen thousand samples were used to implement and train machine learning and deep learning models that can predict and guide the farmers about the appropriate market to sell their farm produce. In essence, the paper uses descriptive analytics to analyze the impact of COVID-19 on agricultural produce pricing. The paper explores the usage of prescriptive analytics to recommend an appropriate market to sell agricultural produce. Contribution: Five machine learning models based on Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Random Forest, and Gradient Boosting, and three deep learning models based on Artificial Neural Networks were implemented. The performance of these models was compared using metrics like Precision, Recall, Accuracy, and F1-Score. Findings: Among the five classification models, the Gradient Boosting classifier was the optimal classifier that achieved precision, recall, accuracy, and F1 score of 99%. Out of the three deep learning models, the Adam optimizer-based deep neural network achieved precision, recall, accuracy, and F1 score of 99%. Recommendations for Practitioners: Gradient boosting technique and Adam-based deep learning model should be the preferred choice for analyzing agricultural pricing-related problems. Recommendation for Researchers: Ensemble learning techniques like Random Forest and Gradient boosting perform better than non-Ensemble classification techniques. Hyperparameter tuning is an essential step in developing these models and it improves the performance of the model. Impact on Society: Statistical analysis of the data revealed the true nature of demand and supply and price disruption. This analysis helps to assess the revenue impact borne by the farmers due to Covid-19. The machine learning and deep learning models help the farmers to get a better price for their crops. Though the da-taset used in this paper is related to India, the outcome of this research work applies to many developing countries that have similar regulated markets. Hence farmers from developing countries across the world can benefit from the outcome of this research work. Future Research: The machine learning and deep learning models were implemented and tested for markets in and around Bangalore. The model can be expanded to cover other markets within India.

Table of Contents for Volume 17, 2022, of the Interdisciplinary Journal of Information, Knowledge, and Management

Aim/Purpose: This paper illustrates the potential of health recommender systems (HRS) to support and enhance maternal care. The study aims to explore the recent implementations of maternal HRS and to discover the challenges of the implementations. Background: The sustainable development goals (SDG) aim to reduce maternal mortality to less than 70 per 100,000 live births by 2030. However, progress is uneven between countries, with primary causes being severe bleeding, infections, high blood pressure, and failed abortions. Regular antenatal care (ANC) visits are crucial for detecting and managing complications, such as hypertensive illnesses, anemia, and gestational diabetes mellitus. Utilizing maternal evaluations during ANC visits can help identify and treat problems early, lowering morbidity and death rates for both mothers and fetuses. Technology-enabled daily health recording can help monitor pregnancy by providing actionable guides to patients and health workers based on patient status. Methodology: A systematic literature review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify maternal HRS reported in studies between November 2022 and December 2022. Information was subsequently extracted to understand the potential benefits of maternal HRS. Titles and abstracts of 1,851 studies were screened for the full-text screening, in which two reviewers independently selected articles and systematically extracted data using a predefined extraction form. Contribution: This study adds to the explorations of the challenges of implementing HRS for maternal care. This study also emphasizes the significance of explainability, data-driven methodologies, automation, and the necessity for integration and interoperability in the creation and deployment of health recommendation systems for maternity care. Findings: The majority of maternal HRS use a knowledge-based (constraint-based) ap-proach with more than half of the studies generating recommendations based on rules defined by experts or available guidelines. We also derived four types of interfaces that can be used for delivering recommendations. Moreover, patient health records as data sources can hold data from patients’ or health workers’ input or directly from the measurement devices. Finally, the number of studies in the pilot or demonstration stage is twice that in the sustained stages. We also discovered crucial challenges where the explainability of the methods was needed to ensure trustworthiness, comprehensibility, and effective enhancement of the decision-making process. Automatic data collection was also required to avoid complexity and reduce workload. Other obstacles were also identified where data integration between systems should be established and decent connectivity must be provided so that complete services can be admin-istered. Lastly, sustainable operations would depend on the availability of standards for integration and interoperability as well as sufficient financial sup-port. Recommendations for Practitioners: Developers of maternal HRS should consider including the system in the main healthcare system, providing connectivity, and automation to deliver better service and prevent maternal risks. Regulations should also be established to support the scale-up. Recommendation for Researchers: Further research is needed to do a thorough comparison of the recommendation techniques used in maternal HRS. Researchers are also recommended to explore more on this topic by adding more research questions. Impact on Society: This study highlights the lack of sustainability studies, the potential for scaling up, and the necessity for a comprehensive strategy to integrate the maternal recommender system into the larger maternal healthcare system. Researchers can enhance and improve health recommendation systems for maternity care by focusing on these areas, which will ultimately increase their efficacy and facilitate clinical practice integration. Future Research: Additional research can concentrate on creating and assessing methods to increase the explainability and interpretability of data-driven health recommender systems and integrating automatic measurement into the traditional health recommender system to enhance the anticipated outcome of antenatal care. Comparative research can also be done to assess how well various models or algorithms utilized in these systems function. Future research can also examine creative solutions to address resource, infrastructure, and technological constraints, such as connectivity and automation to help address the shortage of medical personnel in remote areas, as well as define tactics for long-term sustainability and integration into current healthcare systems.

Aim/Purpose: Acquisitions play a pivotal role in the growth strategy of a firm. Extensive resources and time are dedicated by a firm toward the identification of prospective acquisition candidates. The Indian manufacturing sector is currently experiencing significant growth, organically and inorganically, through acquisitions. The principal aim of this study is to explore models that can predict acquisitions and compare their performance in the Indian manufacturing sector. Background: Mergers and Acquisitions (M&A) have been integral to a firm’s growth strategy. Over the years, academic research has investigated multiple models for predicting acquisitions. In the context of the Indian manufacturing industry, the research is limited to prediction models. This research paper explores three models, namely Logistic Regression, Decision Tree, and Multilayer Perceptron, to predict acquisitions. Methodology: The methodology includes defining the accounting variables to be used in the model which have been selected based on strong theoretical foundations. The Indian manufacturing industry was selected as the focus, specifically, data for firms listed in the Bombay Stock Exchange (BSE) between 2010 and 2022 from the Prowess database. There were multiple techniques, such as data transformation and data scrubbing, that were used to mitigate bias and enhance the data reliability. The dataset was split into 70% training and 30% test data. The performance of the three models was compared using standard metrics. Contribution: The research contributes to the existing body of knowledge in multiple dimensions. First, a prediction model customized to the Indian manufacturing sector has been developed. Second, there are accounting variables identified specific to the Indian manufacturing sector. Third, the paper contributes to prediction modeling in the Indian manufacturing sector where there is limited research. Findings: The study found significant supporting evidence for four of the proposed hypotheses indicating that accounting variables can be used to predict acquisitions. It has been ascertained that statistically significant variables influence acquisition likelihood: Quick Ratio, Equity Turnover, Pretax Margin, and Total Sales. These variables are intrinsically linked with the theories of liquidity, growth-resource mismatch, profitability, and firm size. Furthermore, comparing performance metrics reveals that the Decision Tree model exhibits the highest accuracy rate of 62.3%, specificity rate of 66.4%, and the lowest false positive ratio of 33.6%. In contrast, the Multilayer Perceptron model exhibits the highest precision rate of 61.4% and recall rate of 64.3%. Recommendations for Practitioners: The study findings can help practitioners build custom prediction models for their firms. The model can be developed as a live reference model, which is continually updated based on a firm’s results. In addition, there is an opportunity for industry practitioners to establish a benchmark score that provides a reference for acquisitions. Recommendation for Researchers: Researchers can expand the scope of research by including additional classification modeling techniques. The data quality can be enhanced by cross-validation with other databases. Textual commentary about the target firms, including management and analyst quotes, provides additional insight that can enhance the predictive power of the models. Impact on Society: The research provides insights into leveraging emerging technologies to predict acquisitions. The theoretical basis and modeling attributes provide a foundation that can be further expanded to suit specific industries and firms. Future Research: There are opportunities to expand the scope of research in various dimensions by comparing acquisition prediction models across industries and cross-border and domestic acquisitions. Additionally, it is plausible to explore further research by incorporating non-financial data, such as management commentary, to augment the acquisition prediction model.

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

Aim/Purpose: The aim of this research paper is to suggest a comprehensive model that incorporates the technology acceptance model with the task-technology fit model, information quality, security, trust, and managerial support to investigate the intended usage of big data analytics (BDA) in banks and insurance companies. Background: The emergence of the concept of “big data,” prompted by the widespread use of connected devices and social media, has been pointed out by many professionals and financial institutions in particular, which makes it necessary to assess the determinants that have an impact on behavioral intention to use big data analytics in banks and insurance companies. Methodology: The integrated model was empirically assessed using self-administered questionnaires from 181 prospective big data analytics users in Moroccan banks and insurance firms and examined using partial least square (PLS) structural equation modeling. The results cover sample characteristics, an analysis of the validity and reliability of measurement models’ variables, an evaluation of the proposed hypotheses, and a discussion of the findings. Contribution: The paper makes a noteworthy contribution to the BDA adoption literature within the finance sector. It stands out by ingeniously amalgamating the Technology Acceptance Model (TAM) with Task-Technology Fit (TTF) while underscoring the critical significance of information quality, trust, and managerial support, due to their profound relevance and importance in the finance domain. Thus showing BDA has potential applications beyond the finance sector. Findings: The findings showed that TTF and trust’s impact on the intention to use is considerable. Information quality positively impacted perceived usefulness and ease of use, which in turn affected the intention to use. Moreover, managerial support moderates the correlation between perceived usefulness and the intention to use, whereas security did not affect the intention to use and managerial support did not moderate the influence of perceived ease of use. Recommendations for Practitioners: The results suggest that financial institutions can improve their adoption decisions for big data analytics (BDA) by understanding how users perceive it. Users are predisposed to use BDA if they presume it fits well with their tasks and is easy to use. The research also emphasizes the importance of relevant information quality, managerial support, and collaboration across departments to fully leverage the potential of BDA. Recommendation for Researchers: Further study may be done on other business sectors to confirm its generalizability and the same research design can be employed to assess BDA adoption in organizations that are in the advanced stage of big data utilization. Impact on Society: The study’s findings can enable stakeholders of financial institutions that are at the primary stage of big data exploitation to understand how users perceive BDA technologies and the way their perception can influence their intention toward their use. Future Research: Future research is expected to conduct a comparison of the moderating effect of managerial support on users with technical expertise versus those without; in addition, international studies across developed countries are required to build a solid understanding of users’ perceptions towards BDA.

Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between review consistency and review helpfulness. Overall, the findings reveal the following: (1) online reviews with textual content that correctly explains the associated rating tend to be more helpful; (2) reviews with extreme ratings are more likely to be consistent with their textual content; and (3) comparatively, review consistency more strongly affects the helpfulness of reviews with short textual content, positive polarity textual content, and lower rating scores and variance. Recommendations for Practitioners: E-commerce systems should incorporate a review consistency measure to rank consumer reviews and provide customers with quick and accurate access to the most helpful reviews. Impact on Society: Incorporating a score of review consistency for online reviews can help consumers access the best reviews and make better purchase decisions, and e-commerce systems improve their business, ultimately leading to more effective e-commerce. Future Research: Additional research should be conducted to test the impact of review consistency on helpfulness in different datasets, product types, and different moderating variables.

Aim/Purpose: This study aims to evaluate the success of ERP post-implementation and the factors that affect the overall success of the ERP system by integrating the Task Technology Fit (TTF) model into the Information System Success Model (ISSM). Background: Not all ERP implementations provide the expected benefits, as post-implementation challenges can include inflexible ERP systems and ongoing costs. Therefore, it is necessary to evaluate the success after ERP implementation, and this research integrates the Task Technology Fit (TTF) model into the Information System Success Model (ISSM). Methodology: For data analysis and the proposed model, the authors used SmartPLS 3 by applying the PLS-SEM test and one-tailed bootstrapping. The researchers distributed questionnaires online to 115 ERP users at a construction company in Indonesia and successfully got responses from 95 ERP users. Contribution: The results obtained will be helpful and essential for future researchers and Information System practitioners – considering the high failure rate in the use of ERP in a company, as well as the inability of organizations and companies to exploit the benefits and potential that ERP can provide fully. Findings: The results show that Perceived Usefulness, User Satisfaction, and Task-Technology Fit positively affect the Organizational Impact of ERP implementation. Recommendations for Practitioners: The findings can help policymakers and CEOs of businesses in Indonesia’s construction sector create better business strategies and use limited resources more effectively and efficiently to provide a considerably higher probability of ERP deployment. The findings of this study were also beneficial for ERP vendors and consultants. The construction of the industry has specific characteristics that ERP vendors should consider. Construction is a highly fragmented sector, with specialized segments demanding specialist technologies. Several projects also influence it. They can use them to identify and establish several alternative strategies to deal with challenges and obstacles that can arise during the installation of ERP in a firm. Vendors and consultants can supply solutions, architecture, or customization support by the standard operating criteria, implement the ERP system and train critical users. The ERP system vendors and consultants can also collaborate with experts from the construction sector to develop customized alternatives for construction companies. That would be the most outstanding solution for implementing ERP in this industry. Recommendation for Researchers: Future researchers can use this combined model to study ERP post-implementation success on organizational impact with ERP systems in other company information systems fields, especially the construction sector. Future integration of different models can be used to improve the proposed model. Integration with models that assess the level of Information System acceptance, such as Technology Acceptance Model 3 (TAM3) or Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), can be used in future research to deepen the exploration of factors that influence ERP post-implementation success in an organization. Impact on Society: This study can guide companies, particularly in the construction sector, to maintain ERP performance, conduct training for new users, and regularly survey user satisfaction to ensure the ERP system’s reliability, security, and performance are maintained and measurable. Future Research: It is increasing the sample size with a larger population at other loci (private and state-owned) that use ERP to see the factors influencing ERP post-implementation success and using mixed methods to produce a better understanding. With varied modes, it is possible to get better results by adding unique factors to the research, and future integration of other models can be used to improve the proposed model.

Aim/Purpose: This study aims to devise a personalized solution for online healthcare platforms that can alleviate problems arising from information overload and data sparsity by providing personalized healthcare services to patients. The primary focus of this paper is to develop an effective medicine recommendation approach for recommending suitable medications to patients based on their specific medical conditions. Background: With a growing number of people becoming more conscious about their health, there has been a notable increase in the use of online healthcare platforms and e-services as a means of diagnosis. As the internet continues to evolve, these platforms and e-services are expected to play an even more significant role in the future of healthcare. For instance, WebMD and similar platforms offer valuable tools and information to help manage patients’ health, such as searching for medicines based on their medical conditions. Nonetheless, patients often find it arduous and time-consuming to sort through all the available medications to find the ones that match their specific medical conditions. To address this problem, personalized recommender systems have emerged as a practical solution for mitigating the burden of information overload and data sparsity-related issues that are frequently encountered on online healthcare platforms. Methodology: The study utilized a dataset of MC ratings obtained from WebMD, a popular healthcare website. Patients on this website can rate medications based on three criteria, including medication effectiveness, ease of use, and satisfaction, using a scale of 1 to 5. The WebMD MC rating dataset used in this study contains a total of 32,054 ratings provided by 2,136 patients for 845 different medicines. The proposed HSMCCF approach consists of two primary modules: a semantic filtering module and a multi-criteria filtering module. The semantic filtering module is designed to address the issues of data sparsity and new item problems by utilizing a medicine taxonomy that sorts medicines according to medical conditions and makes use of semantic relationships between them. This module identifies the medicines that are most likely to be relevant to patients based on their current medical conditions. The multi-criteria filtering module, on the other hand, enhances the approach’s ability to capture the complexity of patient preferences by considering multiple criteria and preferences through a unique similarity metric that incorporates both distance and structural similarities. This module ensures that patients receive more accurate and personalized medication recommendations. Moreover, a medicine reputation score is employed to ensure that the approach remains effective even when dealing with limited ratings or new items. Overall, the combination of these modules makes the proposed approach more robust and effective in providing personalized medicine recommendations for patients. Contribution: This study addresses the medicine recommendation problem by proposing a novel approach called Hybrid Semantic-based Multi-Criteria Collaborative Filtering (HSMCCF). This approach effectively recommends medications for patients based on their medical conditions and is specifically designed to overcome issues related to data sparsity and new item recommendations that are commonly encountered on online healthcare platforms. The proposed approach addresses data sparsity and new item issues by incorporating a semantic filtering module and a multi-criteria filtering module. The semantic filtering module sorts medicines based on medical conditions and uses semantic relationships to identify relevant ones. The multi-criteria filtering module accurately captures patient preferences and provides precise recommendations using a novel similarity metric. Additionally, a medicine reputation score is also employed to further expand potential neighbors, improving predictive accuracy and coverage, particularly in sparse datasets or new items with few ratings. With the HSMCCF approach, patients can receive more personalized recommendations that are tailored to their unique medical needs and conditions. By leveraging a combination of semantic-based and multi-criteria filtering techniques, the proposed approach can effectively address the challenges associated with medicine recommendations on online healthcare platforms. Findings: The proposed HSMCCF approach demonstrated superior effectiveness compared to benchmark recommendation methods in multi-criteria rating datasets in terms of enhancing both prediction accuracy and coverage while effectively addressing data sparsity and new item challenges. Recommendations for Practitioners: By applying the proposed medicine recommendation approach, practitioners can develop a medicine recommendation system that can be integrated into online healthcare platforms. Patients can then utilize this system to make better-informed decisions regarding the medications that are most suitable for their specific medical conditions. This personalized approach to medication recommendations can ultimately lead to improved patient satisfaction. Recommendation for Researchers: Integrating patient medicine reviews is a promising way for researchers to elevate the proposed medicine recommendation approach. By leveraging patient reviews, the approach can gain a more comprehensive understanding of how certain medications perform for specific medical conditions. Additionally, exploring the relationship between MC-based ratings using an improved aggregation function can potentially enhance the accuracy of medication predictions. This involves analyzing the relationship between different criteria, such as medication effectiveness, ease of use, and satisfaction of the patients, and determining the optimal weighting for each criterion based on patient feedback. A more holistic approach that incorporates patient reviews and an improved aggregation function can enable the proposed medicine recommendation approach to provide more personalized and accurate recommendations to patients. Impact on Society: To mitigate the risk of infection during the COVID-19 pandemic, the promotion of online healthcare services was actively encouraged. This allowed patients to continue accessing care and receiving treatment while adhering to physical distancing guidelines and shielding measures where necessary. As a result, the implementation of personalized healthcare services for patients is expected to be a major disruptive force in healthcare in the coming years. This study proposes a personalized medicine recommendation approach that can effectively address this issue and aid patients in making informed decisions about the medications that are most suitable for their specific medical conditions. Future Research: One way that may enhance the proposed medicine recommendation approach is to incorporate patient medicine reviews. Furthermore, the analysis of MC-based ratings using an improved aggregation function can also potentially enhance the accuracy of medication predictions.

Aim/Purpose: Based on the stimulus-organism-response (SOR) model, the current study investigated social commerce functions as an innovative retailing technological support by selecting the three most appropriate features for the Chinese online shopping environment with respective value co-creation intentions. Background: Social commerce is the customers’ online shopping touchpoint in the latest retail era, which serves as a corporate technological tool to extend specific customer services. Although social commerce is a relatively novel platform, limited theoretical attention was provided to determine retailers’ approaches in employing relevant functions to improve consumer experience and value co-creation. Methodology: A questionnaire was distributed to Chinese customers, with 408 valid questionnaires being returned and analyzed through Structural Equation Modeling (SEM). Contribution: The current study investigated the new retail concept and value co-creation from the consumer’s perspective by developing a theoretical model encompassing new retail traits and consumer value, which contributed to an alternative theoretical understanding of value creation, marketing, and consumer behaviour in the new retail business model. Findings: The results demonstrated that value co-creation intention was determined by customer experience, hedonic experience, and trust. Simultaneously, the three factors were significantly influenced by interactivity, personalisation, and sociability features. Specifically, customers’ perceptions of the new retail idea and the consumer co-creation value were examined. Resultantly, this study constructed a model bridging new retail characteristics with consumer value. Recommendations for Practitioners: Nonetheless, past new retail management practice studies mainly focused on superficial happiness in the process of human-computer interaction, which engendered a computer system design solely satisfying consumers’ sensory stimulation and experience while neglecting consumers’ hidden value demands. As such, a shift from the subjective perspective to the realisation perspective is required to express and further understand the actual meaning and depth of consumer happiness. Recommendation for Researchers: New retailers could incorporate social characteristics on social commerce platforms to improve the effectiveness of marketing strategies while increasing user trust to generate higher profitability. Impact on Society: The new retail enterprises should prioritise consumers’ acquisition of happiness meaning and deep experience through self-realisation, cognitive improvement, identity identification, and other aspects of consumer experiences and purchase processes. By accurately revealing and matching consumers’ fundamental perspectives, new retailers could continuously satisfy consumer requirements in optimally obtaining happiness. Future Research: Future comparative studies could be conducted on diverse companies within the same industry for comprehensive findings. Moreover, other underlying factors with significant influences, such as social convenience, group cognitive ability, individual family environment, and other external stimuli were not included in the present study examinations.

Aim/Purpose: The major challenges for firms to initiate corporate social responsibility (CSR) arise from resource constraints, complexity, and uncertainty. Consuming considerable financial and human resources is the main difficulty for smaller firms or those operating in less profitable industries, and the lack of immediate outputs from CSR initiatives poses a challenge for firms in prioritizing and assessing their effectiveness. Background: To better integrate CSR management into overall business strategy and decision-making processes, Blockchain technology (BCT) could potentially offer a feasible and optimal alternative to CSR reports. Methodology: This study uses the fixed effects regression by way of the Least Squares Dummy Variable (LSDV) approach in STATA to analyze the direct effect of CSR management on business performance and the moderating effect of BCT adoption on this relationship with a panel data set of 5810 observations collected from the 874 listed companies in 2015 in Taiwan Stock Exchange through 2021. Contribution: This study contributes to the literature by shedding light on the organizational factors that influence BCT adoption. Findings: The findings show that firms with high levels of CSR management have better business performance. Additionally, the adoption of BCT strengthens the positive relationship between CSR management and business performance, but it cannot replace the fundamental principles of CSR. Finally, firm size does not significantly affect BCT adoption, indicating that companies of all sizes have an equal opportunity to adopt BCT, which can help to level the playing field in terms of resources available to different firms. Recommendations for Practitioners: This study suggests that firms managing CSR practices have better business performance, and the adoption of BCTs further enhances this positive relationship. However, BCT adoption does not have the same positive effect on business performance as CSR practices. Additionally, this research can help to inform public policy related to BCT adoption and diffusion. Recommendation for Researchers: By exploring the factors that influence BCT adoption, future researchers can provide insights into the key challenges and opportunities faced by organizations of different sizes and help to develop strategies for promoting the effective adoption of BCT. Impact on Society: Given the limitations of current CSR reporting, the understanding gained from BCT applications can provide companies with an alternative mechanism to foster progress in CSR implementation. Future Research: Firstly, while the fixed-effects model might have dampened the power of explanation because it only captures within-unit variation and ignores between-unit variation, the explanatory power is further limited due to only integrating two independent variables in this model. Because of limited data availability, this study only utilizes CSR_Report and firm_size as independent variables. Future studies can consider more key factors and may lead to different results. Additionally, panel data is collected from Taiwan and, therefore, may not be representative of the broader population. Future researchers integrating the Stock Exchange of different countries are recommended.

Aim/Purpose. COVID-19 was an unprecedented disruptive event that accelerated the shift to remote work and encouraged widespread adoption of digital tools in organizations. This empirical study was conducted from an organizational-strategic perspective, with the aim of examining how the COVID-19 pandemic outbreak affected employees’ use of organizational information systems (IS) as reflected in frequency. Background. To date, only a limited effort has been made, and a rather narrow perspective has been adopted, regarding the consequences of the adoption of new work environments following COVID-19. It seems that the literature is lacking in information regarding employee use of organizational IS since the outbreak of the pandemic. Specifically, this issue has not yet been examined in relation to employees’ perception about the organization’s digital efforts and technological maturity for remote work. The present study bridges this gap. Methodology. The public sector in Israel, which employs about a third of the Israeli work-force, was chosen as a case study of information-intensive organizations. During the first year of COVID-19, 716 questionnaires were completed by employees and managers belonging to four government ministries operating in Israel. The responses were statistically analyzed using a Chi-Square and Spearman’s Rho tests. Contribution. Given that the global pandemic is an ongoing phenomenon and not a passing episode, the findings provide important theoretical and practical contributions. The period prior to the COVID-19 pandemic and the period of the pandemic are compared with regard to organizational IS use. Specifically, the study sheds new light on the fact that employee perceptions motivated increased IS use during an emergency. The results contribute to the developing body of empirical knowledge in the IS field in the era of digital transformation (DT). Findings. More than half of the respondents who reported that they did not use IS before COVID-19 stated that the pandemic did not change this. We also found a significant positive correlation between the perception of the digital efforts made by organizations to enable connection to the IS for remote work and a change in frequency of IS use. This frequency was also found to have a significant positive correlation with the perception of the organization’s technological maturity to enable effective and continuous remote work. Recommendations for Practitioners. In an era of accelerating DT, this paper provides insights that may support chief information officers and chief digital officers in understanding how to promote the use of IS. The results can be useful for raising awareness of the importance of communicating managerial messages for employees regarding the organizational strategy and the resilience achieved through IS not only in routine, but also in particular in emergency situations. Recommendations for Researchers. Considering that the continual crisis has created challenges in IS research, it is appropriate to continue researching the adaptation and acclimation of organizations to the “new normal”. Impact on Society. The COVID-19 pandemic created a sudden change in employment models, which have become more flexible than ever. The research insights enrich the knowledge about the concrete consequences of this critical change. Future Research. We suggest that researchers investigate this core issue in other sectors and/or other countries, in order to be obtain new and complementary empirical insights on a comparative basis.

Aim/Purpose: This study investigates the relationship between dual network embedding, dual entrepreneurial bricolage, and knowledge-creation performance. Background: The importance of new ventures for innovation and economic growth has been fully endorsed. Establishing incubation organizations to help new startups overcome constraints and dilemmas has become the consensus of various countries. In particular, the number of Chinese makerspaces has rapidly increased. Startups in the makerspaces form a loosely coupled dual network to cooperate and share resources, especially knowledge. Methodology: By convenience sampling, 400 startups in the makerspaces in Fujian Province, China were selected for the questionnaire survey study. In total, 307 valid responses were collected, yielding a response rate of 76.8%. The survey data were analyzed for hypothesis testing, using the PL-SEM technique with the AMOS20.0 software. Contribution: At the theoretical level, this research supplements the exploration of the influencing factors of the entrepreneurial bricolage of startups at the network level. It deepens the research on the internal mechanism of the dual network embeddedness affecting the knowledge-creation performance. In practice, it provides a theoretical basis and management inspiration for startups in makerspaces to overcome the inherent disadvantage of being too small and weak to explore innovative paths. Findings: First, relational embedding of startups in makerspaces directly affects knowledge-creation performance. Second, dual entrepreneurial bricolage plays a mediating role in diversity. Selective entrepreneurial bricolage plays a partial mediating role between relationship embedding and knowledge-creation performance. Parallel entrepreneurial bricolage plays a complete intermediary role between structural embedding and knowledge-creation performance. Dual entrepreneurial bricolage plays a complete intermediary role between knowledge embedding and knowledge-creation performance. Recommendations for Practitioners: Enterprises in the makerspaces should make dynamic adjustments to the network embedded state and dual entrepreneurial bricolage to improve knowledge-creation performance. When startups conduct selective entrepreneurship bricolage, they should strengthen relational and knowledge embeddedness to improve their relationship strength and tacit knowledge acquisition. When startups conduct parallel entrepreneurship bricolage, structural and knowledge embedding should be strengthened to improve the position of enterprises in the network to acquire diversified knowledge to explore and discover new business opportunities and project resources. Recommendation for Researchers: The heterogeneity of industries and regions may impact the dual network embedding mechanism of startups. Researchers can choose a wider range of regions and industries for sampling. Impact on Society: This study provides a theoretical basis and management inspiration for startups to overcome the inherent disadvantage of being too small and weak to explore innovative paths. It provides a basis to support startups in unleashing innovation vitality and achieving healthy growth. Future Research: Previous studies have shown that network relationships and bricolage behavior have a certain relationship with the enterprise life cycle. Future research can adopt a longitudinal research design across time points, which will increase the explanatory power of research conclusions.

Aim/Purpose: This paper aims to empirically quantify the financial distress caused by the COVID-19 pandemic on companies listed on Amman Stock Exchange (ASE). The paper also aims to identify the most important predictors of financial distress pre- and mid-pandemic. Background: The COVID-19 pandemic has had a huge toll, not only on human lives but also on many businesses. This provided the impetus to assess the impact of the pandemic on the financial status of Jordanian companies. Methodology: The initial sample comprised 165 companies, which was cleansed and reduced to 84 companies as per data availability. Financial data pertaining to the 84 companies were collected over a two-year period, 2019 and 2020, to empirically quantify the impact of the pandemic on companies in the dataset. Two approaches were employed. The first approach involved using Multiple Discriminant Analysis (MDA) based on Altman’s (1968) model to obtain the Z-score of each company over the investigation period. The second approach involved developing models using Artificial Neural Networks (ANNs) with 15 standard financial ratios to find out the most important variables in predicting financial distress and create an accurate Financial Distress Prediction (FDP) model. Contribution: This research contributes by providing a better understanding of how financial distress predictors perform during dynamic and risky times. The research confirmed that in spite of the negative impact of COVID-19 on the financial health of companies, the main predictors of financial distress remained relatively steadfast. This indicates that standard financial distress predictors can be regarded as being impervious to extraneous financial and/or health calamities. Findings: Results using MDA indicated that more than 63% of companies in the dataset have a lower Z-score in 2020 when compared to 2019. There was also an 8% increase in distressed companies in 2020, and around 6% of companies came to be no longer healthy. As for the models built using ANNs, results show that the most important variable in predicting financial distress is the Return on Capital. The predictive accuracy for the 2019 and 2020 models measured using the area under the Receiver Operating Characteristic (ROC) graph was 87.5% and 97.6%, respectively. Recommendations for Practitioners: Decision makers and top management are encouraged to focus on the identified highly liquid ratios to make thoughtful decisions and initiate preemptive actions to avoid organizational failure. Recommendation for Researchers: This research can be considered a stepping stone to investigating the impact of COVID-19 on the financial status of companies. Researchers are recommended to replicate the methods used in this research across various business sectors to understand the financial dynamics of companies during uncertain times. Impact on Society: Stakeholders in Jordanian-listed companies should concentrate on the list of most important predictors of financial distress as presented in this study. Future Research: Future research may focus on expanding the scope of this study by including other geographical locations to check for the generalisability of the results. Future research may also include post-COVID-19 data to check for changes in results.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: This research study aimed to explore ecommerce fraud practitioners’ experiences and develop a grounded theory framework to help define an ecommerce fraud incident response process, roles and responsibilities, systems, stakeholders, and types of incidents. Background: With a surge in global ecommerce, online transactions have become increasingly fraudulent, complex, and borderless. There are undefined ecommerce fraud roles, responsibilities, processes, and systems that limit and hinder cyber incident response to fraudulent activities. Methodology: A constructivist grounded theory approach was used to investigate and develop a theoretical foundation of ecommerce fraud incident response based on fraud practitioners’ experiences and job descriptions. The study sample consisted of 8 interviews with ecommerce fraud experts. Contribution: This research contributes to the body of knowledge by helping define a novel framework that outlines an ecommerce fraud incident response process, roles and responsibilities, systems, stakeholders, and incident types. Findings: An ecommerce fraud incident response framework was developed from fraud experts’ perspectives. The framework helps define processes, roles, responsibilities, systems, incidents, and stakeholders. The first finding defined the ecommerce fraud incident response process. The process includes planning, identification, analysis, response, and improvement. The second finding was that the fraud incident response model did not include the containment phase. The next finding was that common roles and responsibilities included fraud prevention analysis, tool development, reporting, leadership, and collaboration. The fourth finding described practitioners utilizing hybrid tools and systems for fraud prevention and detection. The fifth finding was the identification of internal and external stakeholders for communication, collaboration, and information sharing. The sixth finding is that research participants experienced different organizational alignments. The seventh key finding was stakeholders do not have a holistic view of the data and information to make some connections about fraudulent behavior. The last finding was participants experienced complex fraud incidents. Recommendations for Practitioners: It is recommended to adopt the ecommerce fraud response framework to help ecommerce fraud and security professionals develop an awareness of cyber fraud activities and/or help mitigate cyber fraud activities. Future Research: Future research could entail conducting a quantitative analysis by surveying the industry on the different components such as processes, systems, and responsibilities of the ecommerce fraud incident response framework. Other areas to explore and evaluate are maturity models and organizational alignment, collaboration, information sharing, and stakeholders. Lastly, further research can be pursued on the nuances of ecommerce fraud incidents using frameworks such as attack graph generation, crime scripts, and attack trees to develop ecommerce fraud response playbooks, plans, and metrics.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Aim/Purpose: Using the Agile Adoption Framework (AAF), this study aims to examine the agile potential of software development companies in Nepal based on their agile maturity level. In addition, this study also examines the impact of various basic agile practices in determining the maturity level of the agile processes being implemented in the software industry of Nepal. Background: Even if most organizations in the software sector utilize agile development strategies, it is essential to evaluate their performance. Nepal’s software industry did not adopt agile techniques till 2014. The Nepalese industry must always adapt to new developments and discover ways to make software development more efficient and beneficial. The population of the study consists of 1,500 and 2,000 employees of software companies in Nepal implementing agile techniques. Methodology: The sample size considered was 150 employees working in software companies in Nepal. However, only 106 respondents responded after three follow-ups. The sample was collected with purposive sampling. A questionnaire was developed to gain information on Customer Adaptive, Customer Collaboration, Continuous Delivery, Human Centric, and Technical Excellence related to agile practices along with the Agile Maturity Level. Contribution: This research contributes to the understanding of agile practices adopted in software companies in developing countries like Nepal. It also reveals the determinants of the agility of software companies in developing countries. Findings: The results suggest that some of the basic principles of agile have a very significant role in Agile Maturity Level in the Nepali context. In the context of Nepal, human-centered practices have a very high level of correlation, which plays a vital role as a major predictor of the agile maturity level. In addition, Technical Excellence is the variable that has the highest level of association with the Agile Maturity Level, making it the most significant predictor of this quality. Recommendations for Practitioners: As Nepali software companies are mostly offshore or serve outsourcing companies, there is a very thin probability of Nepali developers being able to interact with actual clients and this might be one of the reasons for the Nepali industry not relying on Customer Adaptation and Collaboration as major factors of the Agile methodologies. Continuous Delivery, on the other hand, has a significant degree of correlation with Agile Maturity Level. Human-centric practices have a very high level of correlation as well as being a major predictor in determining the Agile Maturity Level in the context of Nepal. Technical Excellence is the most significant predictor and the variable which has the highest level of correlation with Agile Maturity Level. Practitioners should mainly focus on technical excellence as well as human-centric practices to achieve a higher level of Agile Maturity. Recommendation for Researchers: There has not been any such research in the Nepali context that anyone could rely on, to deep dive into their organizational concerns regarding agile strategies and plans. Researchers will need to focus on a more statistical approach with data-driven solutions to the issues related to people and processes. Researchers will need to cover freelancers as well as academics to get a different perspective on what can be the better practices to achieve a higher level of agile maturity. Impact on Society: This study on Agile work is accessible not only to the software industry but also to the general public. The Agile technique has had a huge impact on society’s project management. It has revolutionized how teams approach project planning, development, and execution. The paper’s findings will further information regarding the Agile methodology, which emphasizes collaboration and communication, fosters teamwork and higher quality work, and promotes the exchange of knowledge, ideas, and the pursuit of common goals. Future Research: Owing to the limitations of this study, it is necessary to analyze agile practices in the Nepalese software sector using additional factors that influence agile maturity. The conclusion that years of agile experience do not serve as a balancing factor for both agile practices and the Agile Maturity Level requires additional research. Whether a software outsourcing firm or not, the organization type had no bearing on the degree of maturity of agile methods; this leaves space for further research.

Aim/Purpose: The use of digital technology, such as an electronic records management system (ERMS), has prompted widespread changes across organizations. The organization needs to support its operations with an automation system to improve production performance. This study investigates ERMS’s potentiality to enhance organizational performance in the oil and gas industry. Background: Oil and gas organizations generate enormous electronic records that lead to difficulties in managing them without any system or digitalization procedure. The need to use a system to manage big data and records affects information security and creates several problems. This study supports decision-makers in oil and gas organizations to use ERMS to enhance organizational performance. Methodology: We used a quantitative method by integrating the typical partial least squares (SEM-PLS) approach, including measurement items, respondents’ demographics, sampling and collection of data, and data analysis. The SEM-PLS approach uses a measurement and structural model assessment to analyze data. Contribution: This study contributes significantly to theory and practice by providing advancements in identity theory in the context of big data management and electronic records management. This study is a foundation for further research on the role of ERMS in operations performance and Big Data Management (BDM). This research makes a theoretical contribution by studying a theory-driven framework that may serve as an essential lens to evaluate the role of ERMS in performance and increase its potentiality in the future. This research also evaluated the combined impacts of general technology acceptance theory elements and identity theory in the context of ERMS to support data management. Findings: This study provides an empirically tested model that helps organizations to adopt ERMS based on the influence of big data management. The current study’s findings looked at the concerns of oil and gas organizations about integrating new technologies to support organizational performance. The results demonstrated that individual characteristics of users in oil and gas organizations, in conjunction with administrative features, are robust predictors of ERMS. The results show that ERMS potentiality significantly influences the organizational performance of oil and gas organizations. The research results fit the big ideas about how big data management and ERMS affect respondents to adopt new technologies. Recommendations for Practitioners: This study contributes significantly to the theory and practice of ERMS potentiality and BDM by developing and validating a new framework for adopting ERMS to support the performance and production of oil and gas organizations. The current study adds a new framework to identity theory in the context of ERMS and BDM. It increases the perceived benefits of using ERMS in protecting the credibility and authenticity of electronic records in oil and gas organizations. Recommendation for Researchers: This study serves as a foundation for future research into the function and influence of big data management on ERMS that support the organizational performance. Researchers can examine the framework of this study in other nations in the future, and they will be able to analyze this research framework to compare various results in other countries and expand ERMS generalizability and efficacy. Impact on Society: ERMS and its impact on BDM is still a developing field, and readers of this article can assist in gaining a better understanding of the literature’s dissemination of ERMS adoption in the oil and gas industry. This study presents an experimentally validated model of ERMS adoption with the effect of BDM in the oil and gas industry. Future Research: In the future, researchers may be able to examine the impact of BDM and user technology fit as critical factors in adopting ERMS by using different theories or locations. Furthermore, researchers may include the moderating impact of demographical parameters such as age, gender, wealth, and experience into this study model to make it even more robust and comprehensive. In addition, future research may examine the significant direct correlations between human traits, organizational features, and individual perceptions of BDM that are directly related to ERMS potentiality and operational performance in the future.

Aim/Purpose: Social commerce is an emergent topic widely used for product and service sourcing. It helps companies to have frequent interaction with their customers and strive to achieve a competitive advantage. Yet there is only little empirical evidence focusing on social commerce and its adoption in SMEs to date. This study investigates the key factors affecting social commerce adoption in SMEs. This research designed a theoretical model using the Technology, Organization, and Environment (TOE) Model Background: Despite its rapid growth and usage, social commerce is still in its evolution phase and its current conception is vague and restricted. Therefore, considering the benefits of social commerce for consumers and businesses, it is important to explore the concept of social commerce. Methodology: The research floated a self-administered questionnaire and surveyed 218 Jordanian SME businesses. The data was analyzed using smart PLS and the results were drawn that covers the detail of the characteristics of respondents, study descriptive, results of regressions assumptions, e.g., data normality, reliability, validity, common method biases, and description of the measurement model, followed by the findings of hypothesis analysis. Contribution: This study has many significant contributions to the existing studies on firms’ adoption of social commerce. It indicates that organizational readiness from the organizational perspective and consumer pressure from the environmental dimension of the TOE model are significant influential elements in the adoption of social commerce in business, followed by high-level management support and trading partner pressure, respectively. This shows that organizational readiness to adopt social commerce and consumer pressure has a vital role in Jordanian SMEs’ adopting social commerce. Findings: The results were drawn from a survey of 218 Jordanian SMEs, indicating that organizational readiness from an organizational dimension and consumer pressure environmental perspective, followed by top management’s support and trading partner pressure, significantly predicts the adoption intentions of social commerce. However, perceived usefulness and security concerns from a technological context do not significantly impact behavioral intentions to utilize social commerce. Recommendations for Practitioners: Lack of awareness about new technology and its potential benefits are not well diffused in the Jordanian context. In short, both organizational and environmental dimensions of the TOE framework significantly influence the behavioral intentions for social commerce adoption in the Jordanian context whereas the third-dimension technological factors do not affect the behavioral intentions of SMEs to adopt social commerce. In the technological context, SMEs need to invest in technology and must spread awareness among Jordanian consumers about the potential benefits of technology and must encourage them to use social commerce platforms to interact because of the high significance of social commerce for businesses as it facilitates the quick completion of tasks, enhances the productivity, and improves the chances of high profitability. Recommendation for Researchers: First, the study is limited in scope as it discusses the direct links between the TOE framework, behavioral intentions to use social commerce, and the actual usage of social commerce in the Jordanian context rather than testing the mediation, and moderation. Future research may examine the mediators and moderators in the conceptual model. Second, the research examined the behavioral intentions of SMEs rather than consumers to adopt social commerce. Further research might consider the consumer perspective on social commerce. Impact on Society: This research aims to identify the key factor that impact the behavioral intentions of SME businesses to practice social commerce. The theoretical underpinning of the study lies in the TOE model, as using its basic assumptions the conceptual grounds and hypothesis of the study are developed. Future Research: The study findings are not generalizable in different contexts as it was specifically conducted by gathering data from the Jordanian population. However future studies may consider different contexts, sectors, cultures, or countries to examine the model. Lastly, the research collected data using convenience sampling from 218 SMEs in Jordan, which may create difficulty in the generalizability of the research, so needs to examine a larger sample in future studies.

Table of Contents for Volume 18, 2023, of the Interdisciplinary Journal of Information, Knowledge, and Management

Aim/Purpose: This study presents a novel deep learning-based framework designed to enhance spontaneous micro-expression recognition by effectively increasing the amount and variety of data and balancing the class distribution to improve recognition accuracy. Background: Micro-expression recognition using deep learning requires large amounts of data. Micro-expression datasets are relatively small, and their class distribution is not balanced. Methodology: This study developed a framework using a deep learning-based model to recognize spontaneous micro-expressions on a person’s face. The framework also includes several technical stages, including image and data preprocessing. In data preprocessing, data augmentation is carried out to increase the amount and variety of data and class balancing to balance the distribution of sample classes in the dataset. Contribution: This study’s essential contribution lies in enhancing the accuracy of micro-expression recognition and overcoming the limited amount of data and imbalanced class distribution that typically leads to overfitting. Findings: The results indicate that the proposed framework, with its data preprocessing stages and deep learning model, significantly increases the accuracy of micro-expression recognition by overcoming dataset limitations and producing a balanced class distribution. This leads to improved micro-expression recognition accuracy using deep learning techniques. Recommendations for Practitioners: Practitioners can utilize the model produced by the proposed framework, which was developed to recognize spontaneous micro-expressions on a person’s face, by implementing it as an emotional analysis application based on facial micro-expressions. Recommendation for Researchers: Researchers involved in the development of a spontaneous micro-expression recognition framework for analyzing hidden emotions from a person’s face are playing an essential role in advancing this field and continue to search for more innovative deep learning-based solutions that continue to explore techniques to increase the amount and variety of data and find solutions to balancing the number of sample classes in various micro-expression datasets. They can further improvise to develop deep learning model architectures that are more suitable and relevant according to the needs of recognition tasks and the various characteristics of different datasets. Impact on Society: The proposed framework could significantly impact society by providing a reliable model for recognizing spontaneous micro-expressions in real-world applications, ranging from security systems and criminal investigations to healthcare and emotional analysis. Future Research: Developing a spontaneous micro-expression recognition framework based on spatial and temporal flow requires the learning model to classify optimal features. Our future work will focus more on exploring micro-expression features by developing various alternative learning models and increasing the weights of spatial and temporal features.

Aim/Purpose: In this study, we developed a recommendation system model designed to support decision-makers in identifying consumers eligible for pay-later options via consensus-based decision-making. This approach was chosen due to the high and complex risks involved, such as delayed payments, challenges in reaching consumers, and issues of bad credit. Background: The “pay-later” option, which allows consumers to postpone payment for e-commerce purchases, offers convenience and flexibility but also introduces several challenges: (i) by enabling payment deferral, merchants face financial risks, including potential delays or defaults in payment, adversely affecting their cash flow and profitability; and (ii) this payment delay can also heighten the risk of fraud, including identity theft and unauthorized transactions. Methodology: This study initiated a risk analysis utilizing the ROAD process. Considering contemporary economic developments and advancements in neural networks, integrating these networks into risk assessment has become crucial. Consequently, model development involved the amalgamation of three deep learning methods – CNN (Convolutional Neural Networks), RNN (Recurrent Neural Networks), and LSTM (Long Short-Term Memory) – to address various risk alternatives and facilitate multi-stage decision-making recommendations. Contribution: Our primary contribution is threefold. First, our study identified potential consumers by prioritizing those with the smallest associated problem consequence values. Second, we achieved an optimal recall value using a candidate generator. Last, we categorized consumers to assess their eligibility for pay-later rights. Findings: The findings from this study indicate that our multi-stage recommendation model is effective in minimizing the risk associated with consumer debt repayment. This method of consumer selection empowers policymakers to make informed decisions regarding which consumers should be granted pay-later privileges. Recommendations for Practitioners: This recommendation system is proposed to several key parties involved in the development, implementation, and use of pay-later systems. These parties include E-commerce Executive Management for financial analysis and risk evaluation, the Risk Management Team to assess and manage risks related to users utilizing Pay-Later services, and Sales Managers to integrate Pay-Later services into sales strategies. Recommendation for Researchers: Advanced fraud detection mechanisms were implemented to prevent unauthorized transactions effectively. The goal was to cultivate user confidence in the safety of their financial data by ensuring secure payment processing. Impact on Society: Ensuring consumers understand the terms and conditions of pay-later arrangements, including interest rates, repayment schedules, and potential fees, is crucial. Providing clear and transparent information, along with educating consumers about their financial responsibilities, helps prevent misunderstandings and disputes. Future Research: Our future development plans involve the ongoing assessment of the system’s performance to enhance prediction accuracy. This includes updating models and criteria based on feedback and changes in economic or market conditions. Upholding compliance with security and data privacy regulations necessitates the implementation of protective measures to safeguard consumer information. The implementation of such a system requires careful consideration to ensure fairness and adherence to legal standards. Additionally, it is important to acknowledge that algorithms and models may evolve over time through the incorporation of additional data and continuous evaluations.