Return on Investment (ROI) is one of the most popular performance measurement and evaluation metrics. ROI analysis (when applied correctly) is a powerful tool in comparing solutions and making informed decisions on the acquisitions of information systems. The purpose of this study is to provide a systematic research of the accuracy of the ROI evaluations in the context of information systems implementations. Measurements theory and error analysis, specifically propagation of uncertainties methods, were used to derive analytical expressions for ROI errors. Monte Carlo simulation methodology was used to design and deliver a quantitative experiment to model costs and returns estimating errors and calculate ROI accuracies. Spreadsheet simulation (Microsoft Excel spreadsheets enhanced with Visual Basic for Applications) was used to implement Monte Carlo simulations. The main contribution of the study is that this is the first systematic effort to evaluate ROI accuracy. Analytical expressions have been derived for estimating errors of the ROI evaluations. Results of the Monte Carlo simulation will help practitioners in making informed decisions based on explicitly stated factors influencing the ROI uncertainties.

Health Data collection is one of the major components of public health systems. Decision makers, policy makers, and medical service providers need accurate and timely data in order to improve the quality of health services. The rapid growth and use of mobile technologies has exerted pressure on the demand for mobile-based data collection solutions to bridge the information gaps in the health sector. We propose a prototype using open source data collection frameworks to test its feasibility in improving the vaccination data collection in Kenya. KenVACS, the proposed prototype, offers ways of collecting vaccination data through mobile phones and visualizes the collected data in a web application; the system also sends reminder short messages service (SMS) to remind parents on the date of the next vaccination. Early evaluation demonstrates the benefits of such a system in supporting and improving vaccination of children. Finally, we conducted a qualitative study to assess challenges in remote health data collection and evaluated usability and functionality of KenVACS.

Purpose: Innovation capability is a growing and significant area of academic research. However, there is little attempt to provide a cumulative overview of this phenomenon. The purpose of this systematic review is to synthesize peer reviewed articles published in the area to develop a conceptual framework and to aid future research. Design/Methodology/Approach: The paper adopted a systematic review of literature on innovation capability. The final screening generated 51 articles from 30 journals from 2000-2015. Findings: The examination and synthesis of the theoretical and the empirical articles show that (1) the authors applied narrow range of conceptual and theoretical foundations; (2) innovation capability is being investigated mostly at the firm level for about 90% of the articles, and marginally about 5% at network (supply) chain level; (3) the authors define innovation capability in different ways and use diverse set of dimensions to measure innovation capability; (4) there is potential for future research across firms in innovation management disciplines. Practical implications: The review contributes to theory development in organizational capability literature in general. Managers wishing to innovate need to examine critically and integrate some of the innovation capability dimensions proposed in this paper. Originality: The review is unique in the sense that it provides conceptualisation of innovation capability framework.

Recently ERP implementation has seen increasing significance in different sectors. The research related to the implementation issues of ERP has also increased during the past decade. Particularly the behavioral aspects of ERP implementations have been researched in terms of identifying appropriate frameworks, critical success factors, perception and attitude of users and managers, the role of change agents, leadership, etc. This conceptual review summarizes some of the studies done on the above aspects and suggests further research areas. It is suggested that leadership competencies, learning attitude of the users, and organizational power dynamics can be potential areas of research in the context of ERP implementation.

Aim/Purpose: The purpose of this study is to investigate the interrelationship among the quality measures of information system success, including system quality, information, quality, and service quality, that eventually influence accounting information systems effectiveness. Background: It is generally believed that investment in an information system offers opportunities to organizations for business process efficiency and effectiveness. Despite huge investments in accounting information systems, banks in Nigeria have not realized the full potential benefits of using these systems because of persistent failures. Few studies have been conducted to address the problem. Methodology: A survey research design was used to collect data, and a total of 287 questionnaires were retrieved from respondents in the Nigerian banking sector. Contribution: This study contributes to the understanding of the most important antecedent factors of the quality measures, the interrelationship among the quality measures, and the influence of these measures on the accounting information systems effectiveness. Findings: The result of the study revealed that security, ease of use, and efficiency are key features of system quality, while the information quality dimension includes accuracy, timeliness, and completeness. The result of the study further revealed that information quality and system quality have significant influences on accounting information systems effectiveness. Recommendations for Practitioners: This study provides practitioners with important measures for evaluation of AIS effectiveness in the context of Nigerian banks. Recommendation for Researchers: Future researchers may build on the findings of current study to conduct fur-ther research in the area of AIS effectiveness in different contexts. Future Research: This study examines only three quality measures of Delone and Mclean model and antecedents of information and system quality measures, neglecting contingency factor. Therefore, future study should include other factors to the AIS effectiveness model to help in developing more specific theory in AIS domain.

Aim/Purpose: The purpose of this paper is to examine the key research foci, methodologies, and theoretical perspectives adopted by researchers when studying E-government accessibility for persons with disabilities (PWDs), particularly in developing countries. The study aims to develop a conceptual framework for designing accessible E-government for PWDs in developing countries. Background: Studies on E-government accessibility for persons with disabilities in developing countries have been minimal. The few studies conducted until now have failed to integrate PWDs, a population already marginalized, into the digital society. Accessibility has been identified by researchers as a major hindrance to PWDs participating in E-government. It is imperative therefore to examine the manner in which researchers investigate and acquire knowledge about this phenomenon. Methodology : The study synthesizes literature from top IS journals following a systematic literature review approach. The data synthesis focuses on identifying key concepts relating to E-government accessibility for PWDs. Contribution: The study contributes to the field of E-government, with a focus on how E-government services can be made accessible to PWDs. The study calls on researchers to reflect on their epistemological and ontological paradigms when examining accessibility of E-government services in developing countries. Findings: The findings show that most researchers focus on the evaluation of E-government websites and predominantly adopt quantitative methods. The study also reveals that the use of technological determinism as a theoretical lens is high among researchers. Recommendations for Practitioners : The study recommends that E-government web developers and policy makers involve PWDs from design to evaluation in the development of E-government applications. Recommendation for Researchers: The study advocates the need to conduct studies on E-government accessibility by employing more qualitative and mixed approaches to gain in-depth and better understanding of the phenomenon. Impact on Society : This study creates greater awareness and points out inadequacies that society needs to address to make E-government more inclusive of and participatory for PWDs. Future Research: Further empirical work is required in order to refine the relevance and applicability of various constructs in EADM so as to arrive at a framework for addressing E-government accessibility for PWDs in developing countries.

Background/Aim/Purpose: A commonly implemented software process improvement framework is the capability maturity model integrated (CMMI). Existing literature indicates higher levels of CMMI maturity could result in a loss of agility due to its organizational focus. To maintain agility, research has focussed attention on agile maturity models. The objective of this paper is to find the common research themes and conclusions in agile maturity model research. Methodology: This research adopts a systematic approach to agile maturity model research, using Google Scholar, Science Direct, and IEEE Xplore as sources. In total 531 articles were initially found matching the search criteria, which was filtered to 39 articles by applying specific exclusion criteria. Contribution:: The article highlights the trends in agile maturity model research, specifically bringing to light the lack of research providing validation of such models. Findings: Two major themes emerge, being the coexistence of agile and CMMI and the development of agile principle based maturity models. The research trend indicates an increase in agile maturity model articles, particularly in the latter half of the last decade, with concentrations of research coinciding with version updates of CMMI. While there is general consensus around higher CMMI maturity levels being incompatible with true agility, there is evidence of the two coexisting when agile is introduced into already highly matured environments. Future Research: Future research direction for this topic should include how to attain higher levels of CMMI maturity using only agile methods, how governance is addressed in agile environments, and whether existing agile maturity models relate to improved project success.

Aim/Purpose: This paper reports our implementation of a prototype system, namely PRATO (Proposals Reviewers Automated Taxonomy-based Organization), for automatic assignment of proposals to reviewers based on categorized tracks and partial matching of reviewers’ profiles of research interests against proposal keywords. Background: The process of assigning reviewers to proposals tends to be a complicated task as it involves inspecting the matching between a given proposal and a reviewer based on different criteria. The situation becomes worse if one tries to automate this process, especially if a reviewer partially matches the domain of the paper at hand. Hence, a new controlled approach is required to facilitate the matching process. Methodology: Proposals and reviewers are organized into categorized tracks as defined by a tree of hierarchical research domains which correspond to the university’s colleges and departments. In addition, reviewers create their profiles of research interests (keywords) at the time of registration. Initial assignment is based on the matching of categorized sub-tracks of proposal and reviewer. Where the proposal and a reviewer fall under different categories (sub-tracks), assignment is done based on partial matching of proposal content against re-viewers’ research interests. Jaccard similarity coefficient scores are calculated of proposal keywords and reviewers’ profiles of research interest, and the reviewer with highest score is chosen. The system was used to automate the process of proposal-reviewer assignment at the Umm Al-Qura University during the 2017-2018 funding cycle. The list of proposal-reviewer assignments generated by the system was sent to human experts for voting and subsequently to make final assignments accordingly. With expert votes and final decisions as evaluation criteria, data system-expert agreements (in terms of “accept” or “reject”) were collected and analyzed by tallying frequencies and calculating rejection/acceptance ratios to assess the system’s performance. Contribution: This work helped the Deanship of Scientific Research (DSR), a funding agency at Umm Al-Qura University, in managing the process of reviewing proposals submitted for funding. We believe the work can also benefit any organizations or conferences to automate the assignment of papers to the most appropriate reviewers. Findings: Our developed prototype, PRATO, showed a considerable impact on the entire process of reviewing proposals at DSR. It automated the assignment of proposals to reviewers and resulted in 56.7% correct assignments overall. This indicates that PRATO performed considerably well at this early stage of its development. Recommendations for Practitioners: It is important for funding agencies and publishers to automate reviewing process to obtain better reviewing quality in a timely manner. Recommendation for Researchers: This work highlighted a new methodology to tackle the proposal-reviewer assignment task in an automated manner. More evaluation might be needed with consideration of different categories, especially for partially matched candidates. Impact on Society: The new methodology and knowledge about factors influencing the implementation of automated proposal-reviewing systems will help funding agencies and publishers to improve the quality of their internal processes. Future Research: In the future, we plan to examine PRATO’s performance on different classification schemes where specialty areas can be represented in graphs rather than trees. With graph representation, the scope for reviewer selection can be widened to include more general fields of specialty. Moreover, we will try to record the reasons for rejection to identify accurately whether the rejection was due to improper assignment or other reasons.

Aim/Purpose: In this research, we examined the influence of the information system (IS) quality dimensions proposed by Wixom and Todd on reading satisfaction of online newspaper readers in Bangladesh, especially the readers’ intention to revisit and recommendations through electronic word-of-mouth (eWOM). Background: We identified the top 50 most visited websites, of which 13 were online newspapers, although their ranking among Bangladesh online newspapers varies from month to month. The literature illustrates that, despite the wide availability of online news portals and the fluctuations in frequency of visits, little is known about the factors that affect the satisfaction, word-of-mouth, and frequency of visits of readers. An understanding of reader satisfaction will help to gain richer insights into the phenomenon of readers’ intention to revisit and recommendation by eWOM. Stakeholders of online newspapers can then focus on those factors to increase visits to their websites, which will help them attract online advertisements from different organisations. Methodology: Data were collected using a structured questionnaire, from 217 people who responded to the survey. We used SmartPLS 3 to analyze the data collected, as it is based on second-generation analysis, which in turn is based on structural equation modeling (SEM). Contribution: This research explores the impacts of technological dimensions on readers’ satisfaction, as most of the previous research has focused on cultural or social dimensions. Findings: The results supported all of the hypothesized relationships between technological dimensions and reader satisfaction with online newspapers, except for one. The first, information, was predicted with accuracy and completeness, while the second object-based belief, system quality, was predicted by its accessibility, flexibility, reliability, and timeliness. Overall, quality factors influencing readers’ satisfaction were shown to lead to word-of-mouth revisit intentions. Our proposed model was empirically tested and has contributed to a nascent body of knowledge about readers’ revisit intentions and eWOM recommendations regarding online newspapers. It was also shown that strong satisfaction leads to higher revisit intention and eWOM. Recommendations for Practitioners: To keep the users satisfied, online newspapers need to focus on improving information quality (IQ) and system quality (SQ). If they do this well, they will be rewarded with higher revisit intention and recommendations by eWOM. Recommendation for Researchers: This study extends Oh’s customer loyalty model by integrating the Wixom-Todd model. This study reinforces an alternative rationale of the construct satisfaction. Future Research: We ignored negative stimulus like technostress, which can have an impact on satisfaction. In future, we will test the relationship between technostress and its impact on online newspaper reading.

Aim/Purpose: The aim of this paper is to analyze various text classification techniques employed in practice, their strengths and weaknesses, to provide an improved awareness regarding various knowledge extraction possibilities in the field of data mining. Background: Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. However, in spite of the growth and spread of AI in all fields of research, its role with respect to text mining is not well understood yet. Methodology: For this study, various articles written between 2010 and 2017 on “text classification techniques in AI”, selected from leading journals of computer science, were analyzed. Each article was completely read. The research problems related to text classification techniques in the field of AI were identified and techniques were grouped according to the algorithms involved. These algorithms were divided based on the learning procedure used. Finally, the findings were plotted as a tree structure for visualizing the relationship between learning procedures and algorithms. Contribution: This paper identifies the strengths, limitations, and current research trends in text classification in an advanced field like AI. This knowledge is crucial for data scientists. They could utilize the findings of this study to devise customized data models. It also helps the industry to understand the operational efficiency of text mining techniques. It further contributes to reducing the cost of the projects and supports effective decision making. Findings: It has been found more important to study and understand the nature of data before proceeding into mining. The automation of text classification process is required, with the increasing amount of data and need for accuracy. Another interesting research opportunity lies in building intricate text data models with deep learning systems. It has the ability to execute complex Natural Language Processing (NLP) tasks with semantic requirements. Recommendations for Practitioners: Frame analysis, deception detection, narrative science where data expresses a story, healthcare applications to diagnose illnesses and conversation analysis are some of the recommendations suggested for practitioners. Recommendation for Researchers: Developing simpler algorithms in terms of coding and implementation, better approaches for knowledge distillation, multilingual text refining, domain knowledge integration, subjectivity detection, and contrastive viewpoint summarization are some of the areas that could be explored by researchers. Impact on Society: Text classification forms the base of data analytics and acts as the engine behind knowledge discovery. It supports state-of-the-art decision making, for example, predicting an event before it actually occurs, classifying a transaction as ‘Fraudulent’ etc. The results of this study could be used for developing applications dedicated to assisting decision making processes. These informed decisions will help to optimize resources and maximize benefits to the mankind. Future Research: In the future, better methods for parameter optimization will be identified by selecting better parameters that reflects effective knowledge discovery. The role of streaming data processing is still rarely explored when it comes to text classification.

Aim/Purpose: The wide use of online banking and technological advancement has attracted the interest of malicious and criminal users with a more sophisticated form of attacks. Background: Therefore, banks need to adapt their security systems to effectively stem threats posed by imposters and hackers and to also provide higher security standards that assure customers of a secured environment to perform their financial transactions. Methodology : The use of authentication techniques that include the mutual secure socket layer authentication embedded with some specific features. Contribution: An approach was made through this paper towards providing a more reliable and complete solution for implementing multi-level user authentication in a banking environment. Findings: The use of soft token as the final stage of authentication provides ease of management with no additional hardware requirement. Recommendations for Practitioners : This work is an approach made towards providing a more reliable and complete solution for implementing multi-level user authentication in a banking environment to stem cybercrime. Recommendation for Researchers: With this approach, a reliable system of authentication is being suggested to stem the growing rate of hacking activities in the information technology sector. Impact on Society :This work if adopted will give the entire populace confidence in carrying out online banking without fear of any compromise. Future Research: This work can be adopted to model a real-life scenario.

Aim/Purpose: The current study investigates the relationship between mobile and email marketing and consumer impulse buying tendencies in Pakistan. Background: Technology has become a primary driver for all business operations, which has dramatically transformed the wireless communications marketing paradigm. However, researchers have claimed that further inquiry is still needed to explore the role that distinct and emerging global technologies have on marketing communication strategies. This study explores the linkage of mobile and email marketing on consumers’ impulse buying behavior in Pakistan. Methodology: Primary data were collected through the distribution of 1000 questionnaires among students of different universities within two provinces of Pakistan: Punjab and Khyber Pakhton Khan (KPK). The study was conducted between November 2016 and March 2017. The authors received back 950 surveys, which is a very significant rate of return (95%). Of those submitted, 900 surveys were deemed eligible for analysis after improper documents were eliminated. Structure equation modeling (SEM) was utilized to test the study’s hypotheses. Contribution: This study assists organizations in improving marketing campaigns by focusing more on mobile devices (m-devices) and email medium to better comprehend consumers’ assessment processes at a lower budgetary cost. Such digital considerations could provide innovative possibilities for marketers in approaching their target market by adopting novel methods for information sharing. Findings: The findings revealed a positive association between mobile and email marketing on consumers’ impulse buying tendencies. The comprehensive analysis affirmed; however, there is a higher positive relationship of mobile marketing results compared to email marketing outcomes. There are favorable benefits in considering such emerging methods in marketing communications as promotional strategies are considered by organizations. Recommendations for Practitioners: Marketers are encouraged to evaluate the potential of using both emerging mediums to take advantage of consumer impulse buying habits where m-devices and emails approaches are utilized. Future Research: Future inquiries might examine the global influence of m-devices and email technology toward other buying tendencies of consumers: exploratory, online, variety seeking, habitual, and other emerging complex on-demand buying behavior.

Aim/Purpose: The aim of this study is to examine empirically the effects of certain key Critical Success Factors (CSFs) for the implementation of Enterprise Resource Planning (ERP) Systems on the comprehensive achievement of the crucial roles of Computer-Based Information Systems (CBISs) Background: The effects of the CSFSs were examined in the higher education sector in the Kingdom of Saudi Arabia (KSA) using a case study of the ERP adoption in Prince Sattam Bin Abdulaziz University. Methodology: A theoretical model was proposed based on the literature written on the CSFs and the roles of CBISs in business. The model encompasses six key CSFs and their associations with the realization of the crucial roles of CBISs. To test the proposed model, a questionnaire was developed by considering the most frequently used measurements items in the ERP’s literature. The data were collect-ed from 219 key stakeholders. Contribution: This study acts as one of the few empirical studies in assessing the effects of the important CSFs for ERP implementation upon its successful implementation. Its outcomes provide more insights and clarifications about the effects of six key CSFs on the comprehensive achievement of the crucial CBIS’s roles. Particularly, the uniqueness of this study lies in addressing the effects of these CSFs on the achievement of the vital CBIS’s roles collectively rather than the achievement of each role individually. Moreover, the study examined these effects in the higher education environment, which is characterized by its own special business processes and services. Findings: The results reveal that the six key CSFs have a positive relationship with the comprehensive achievement of the crucial roles of CBISs. These findings are consistent with many previous studies on the effects of the CSFs on the realization of the expected benefits of the enterprise systems. Recommendations for Practitioners: The managers and other key stakeholders should carefully manage the vital aspects of the CSFs in order to realize the promised ERP’s benefits, including the CBIS’s roles. Future Research: Additional empirical examinations are needed to investigate the effects of the rest of the CSFs on realizing the roles of information systems.

Aim/Purpose: This study explores how women-led SMMEs in developing countries, specifically in the Mozambican context, institutionalise e-commerce by focusing on the ongoing interaction between the SMME, its context, and process of e-commerce institutionalization. Background: It is believed that institutionalization of e-commerce provides significant benefits of unlimited access to new markets, and access to new, improved, inexpensive and convenient operational methods of transacting. Although prior studies have examined the adoption of e-commerce and the enabling and constraining factors, few have examined e-commerce (i) institutionalization (that is, post-adoption), and (ii) from a gender perspective. This study aims to respond to this paucity in the literature by exploring how women-led SMMEs in developing countries, specifically in the Mozambican context, institutionalise e-commerce. Methodology: The study follows a qualitative inquiry approach for both data collection and analysis. Semi-structured interviews were adopted for data collection and thematic analysis implemented on the data. SMMEs were purposively sampled to allow for the selection of information-rich SMMEs for study and specifically those that have gone through the experience of adoption and in some cases have institutionalized e-commerce. Contribution: The empirical findings explain how the institutionalization process from interactive e-commerce to transactive e-commerce unfolds in the Mozambican context. Findings: Transition from interactive to transactive e-commerce is firstly influenced by (i) the type of business the SMME is engaged in; and (ii) customer and trading partner’s readiness for e-commerce. Secondly, the transition process is influenced by the internal factors of (i) manager’s demographic factors; (ii) mimetic behaviour arising from exposure to (foreign) organizations in the same industry that have mature forms of e-commerce; (iii) the business networks developed with some of these organizations that have mature forms of e-commerce; (iv) access to financial resources; and (v) social media technologies. Thirdly, the process is influenced by external contextual factors of (i) limited government intervention towards e-commerce endeavors; (ii) limited to lack of financial institutions readiness for e-commerce; (iii) lack of local available IT expertise; (iv) consumer’s low purchasing power due to economic recessions; (vi) international competitive pressure; and (vii) sociocultural practices. Recommendations for Practitioners: The study provides SMME managers, practitioners, and other stakeholders concerned with women’s development with a better understanding of the process in order to develop appropriate policies and interventions that are suitable for the reality of women-led SMMEs in Mozambique and other developing countries with similar contextual characteristics. Recommendation for Researchers: The study contributes to the existing debate of e-commerce and the use of ICT for development in developing countries by providing a distinct contribution of the institutionalization process and how the contextual structures influence this process. Impact on Society: Women-led SMME managers can learn from the different experiences, and compare their e-commerce efforts with SMMEs that were able to institutionalize and make strategies for improvements within their organizations. Future Research: The manner in which women-led SMMEs employ e-commerce requires further investigation to understand how issues related to gender, the cultural context, and different regions or countries impact this process.

Aim/Purpose: This study aims to identify the main factors influencing consumers’ adoption of social commerce (s-commerce). Based on the socio-technical theory, the study suggests a research model that investigates the key social and technical factors driving consumers’ decision to purchase from social commerce websites. In addition, the research model explores the interactive relationship among these factors. Background: The phenomenon of social commerce (s-commerce) has emerged due to the increased penetration of social media and the rapid development of Web 2.0 technologies. Electronic commerce (e-commerce) companies have made significant efforts to shift their operations to s-commerce. Therefore, to facilitate their efforts to transform, various research has been conducted to investigate the main factor influencing the adoption of s-commerce. Most of these studies have emphasised the social aspects related to s-commerce design features to understand how the use of advanced web technologies influence how customers interact with each other in s-commerce environments. However, s-commerce is viewed as a socio-technical system that requires the investigation of both social and technical factors to help in the design of effective s-commerce platforms. Methodology: To validate the proposed research model, 418 paper-based and online questionnaires were collected from online shoppers in Jordan. The Structure Equation Modelling (SEM) approach was used to test the proposed hypotheses. Contribution: This study offers a research model that serves as a theoretical framework for investigating customers’ behaviour in s-commerce environment. It represents a strong context-specific model that includes both the technical and social facilitators of s-commerce. The research model participates in gaining an improved understanding of how customers’ intention, actual purchase and post-purchase experience are formed in the s-commerce environment. Findings: The results of Structure Equation Modelling (SEM) reveal that s-commerce constructs, familiarity and user experience have a positive influence on the perceived usefulness and perceived ease of use of s-commerce. In addition, perceptions of its usefulness and ease of use have a positive influence on trust, which in turn influences the purchase intention and the actual purchase. Finally, the post-purchase experience significantly influences both trust and purchase intention. Recommendations for Practitioners: This study shows that social commerce constructs strengthen customers’ perceptions of usefulness. S-commerce service providers are required to provide their customers with various channels to seek social support. Both familiarity and user experience are key enablers of customers’ perceived ease of use. S-commerce service providers consider the variation in customers’ familiarity and experience with s-commerce websites because this has a significant influence on purchase intentions and behaviour. Consequently, system designers should offer useful and sufficient information and tutorials that effectively guide customers in their searching, decision-making and purchasing activities throughout the shopping process. S-commerce service providers should understand the importance of providing secure payment systems and make their privacy policies clear to customers. Post-purchase experience has an influential role in reinforcing customers’ trust and purchase intention. The findings confirm the important role of post-purchase experience in retaining customers by improving their trust and repurchase intention. Therefore, making a customer’s post-purchase experience pleasant should be a key priority for s-commerce service providers because it has a significant influence on customers’ trust and repurchase intentions. Recommendation for Researchers: This study offers a unidimensional conceptualisation of the design features of s-commerce. These features include three main forms: recommendations and referrals, communities and forums, and reviews and ratings. Such conceptualisation provides additional insights and an understanding of the activities of information sharing in s-commerce. The significance of the technical side of s-commerce is highlighted and empirical proof is provided that social interactions guided by social technologies enhance customers’ perceived usefulness of an s-commerce website, thus increasing their trust and intention to purchase which leads to an actual purchase. This offers insights into the various types of s-commerce characteristics that contribute to facilitating customers’ purchase behaviour on s-commerce websites. Impact on Society: The findings offer insights which have important implications for research and practice to help facilitate the adoption of s-commerce. Future Research: This study considered the s-commerce websites as a homogenous online environment. Additional research could collect data from diverse online communities, such as professional groups, to provide a comprehensive understanding of how a wider variety of user behaviour is affected. Second, this was a quantitative study based on data collected in a questionnaire. Further studies may consider using qualitative or mixed methodologies (i.e. focus groups and interviews) to explore other technical and social factors that influence the use of s-commerce.

Aim/Purpose: This study seeks to investigate if participation in business association’s programs through the traditional and new media platforms influences family businesses in South Eastern Nigeria to diversify into similar or different businesses. Background: Before the advances in information and communication technology, businesses were carried on via the traditional media. The application of these advances has changed the way business communications and transactions are conducted globally in both family and non-family businesses. Businesses are adapting to today’s turbulent environment by opening similar or different businesses in the same or different locations that are hinged on the traditional and new media platforms. Nigerians are largely involved in social network through the traditional (face-to-face contact) and new media (e.g., Facebook, WhatsApp, Twitter, YouTube and Instagram). Moreover, in spite of the commonplaceness of family businesses in Nigeria, these businesses still experience weak diversification, bankruptcy and loss of socio-emotional wealth. Consequent upon the foregoing, this paper specifically investigates if involvement in social network via the traditional media (i.e., participation in business association’s meetings, workshops, seminars) and the new media (i.e., participation in the business association’s interactive sessions on trending business issues through the association’s online social platform like WhatsApp, Twitter), influence family businesses in South Eastern Nigeria to diversify into similar or different businesses. Methodology: The study adopted a qualitative methodology. The qualitative data were generated via interview involving 30 purposively selected businesses from South Eastern Nigeria. This comprises 15 family businesses each that have respectively adopted related and unrelated diversification strategies. Two respondents (i.e., the business owner and a top level manager) each were drawn from the selected businesses. In all, 60 respondents were interviewed. Since the unit of analysis is the family business, the interview transcriptions from all the respondents were subjected to thematic content analysis on the basis of the family businesses. Contribution: Active involvement and participation in all the meetings, discussions, workshops and seminars of the social network via the traditional and new media platforms facilitates the adoption of related or unrelated diversification in family businesses. Moreover, the adoption of similar social network platforms like WhatsApp and Twitter in all the relationships among and between employees and managers, and the transactions of the businesses is one of the key factors for achieving successful related or unrelated diversification in family businesses. Findings: In spite of the risky nature of the business environment, the adoption of related diversification strategies is significantly influenced by resources such as business consultancy services garnered through the traditional and new media platforms of the social network. Also, family businesses that are actively involved in a social network where the actors interact through the traditional and new media are influenced by the resources acquired to consider adopting unrelated diversification. These resources include: better understanding of the nature of business challenges, environments and experiences; and different lines of businesses. Thus, the traditional and new media platforms are complementary in their roles. Recommendations for Practitioners: Family business owner-managers could use the findings to develop related or unrelated strategies for diversifying into existing or new markets. This can be through the localization of manufacturing plant, improvement of product packaging, sitting of sales outlet closer to the consumers, introduction of lower prices for products/services, introduction of new and better ways of service delivery, or development of more compelling promotion strategies. Recommendation for Researchers: As a veritable guide, this study could guide future researchers in the formulation of their objectives, selection of instrument for data collection and respondents, and adoption of method of data analysis. Impact on Society: Successful diversification suggests the establishment of new or more businesses. Consequently, these new or more family businesses are expected to translate to more employment opportunities and by extension reduction in unemployment and poverty rates in the society. Future Research: Further studies should be carried out to enhance the development of family businesses, contribute to the existing literature and ensure the generalization of the findings.

Aim/Purpose: This study investigates the relationship between knowledge sharing process and innovation success with specific emphasis on tacit knowledge. Based on the literature review, we hypothesised that knowledge donating and collecting have a positive relationship with innovation success. Methodology: The hypotheses were empirically tested using the partial least square path modelling with data collected from twelve state-owned public organisations operating in Southern Nigeria. Contribution: The research made distinct empirical contributions to the burgeoning literature on knowledge sharing and innovation from the public sector and developing country context. Findings: Knowledge donating and collecting contribute to innovation success positively and significantly. Knowledge donating effect on innovation success was found to be more significantly positive than the effect of knowledge collecting on innovation success. Recommendations for Practitioners: Public organisations should promote a supportive culture to spur innovation through the frequent share of experiences, information and skills among the various knowledge actors. Public managers should convey the importance of knowledge sharing and its value to knowledge users in clear terms and attend to creating conditions or contexts that encourage people to share knowledge freely and willingly with others. It is apt to improve organisational commitment and support for knowledge sharing activities such as mentorship programs, workshops, conferences, seminars and other related training and development programs in order to provide opportunities for employees to develop innovation competencies from the transfer of tacit knowledge developed over time from experience. To optimise innovation outcomes from knowledge sharing practices, knowledge sharing should be in tandem with the industry or global best practices. Future Research: Future studies should add interviews to provide depth in terms of insights and substance to the questionnaire, and may extend to public organisation with different ownership structure.

Aim/Purpose: This study examines the influence of ambidextrous knowledge sharing in industrial clusters on innovation performance from the perspective of knowledge-based dynamic capabilities. Background: The key factor to improving innovation performance in an enterprise is to share knowledge with other enterprises in the same cluster and use dynamic capabilities to absorb, integrate, and create knowledge. However, the relationships among these concepts remain unclear. Based on the dynamic capability theory, this study empirically reveals how enterprises drive innovation performance through knowledge sharing. Methodology: Survey data from 238 cluster enterprises were used in this study. The sample was collected from industrial clusters in China’s Fujian province that belong to the automobile, optoelectronic, and microwave communications industries. Through structural equation modeling, this study assessed the relationships among ambidextrous knowledge sharing, dynamic capabilities, and innovation performance. Contribution: This study contributes to the burgeoning literature on knowledge management in China, an important emerging economy. It also enriches the exploration of innovation performance in the cluster context and expands research on the dynamic mechanism from a knowledge perspective. Findings: Significant relationships are found between ambidextrous knowledge sharing and innovation performance. First, ambidextrous knowledge sharing positively influences the innovation performance of cluster enterprises. Further, knowledge absorption and knowledge generation capabilities play a mediating role in this relationship, which confirms that dynamic capabilities are a partial mediator in the relationship between ambidextrous knowledge sharing and innovation performance. Recommendations for Practitioners: The results highlight the crucial role of knowledge management in contributing to cluster innovation and management practices. They indicate that cluster enterprises should consider the importance of knowledge sharing and dynamic capabilities for improving innovation performance and establish a multi-agent knowledge sharing platform. Recommendation for Researchers: Researchers could further explore the role of other mediating variables (e.g., organizational agility, industry growth) as well as moderating variables (e.g., environmental uncertainty, learning orientation). Impact on Society: This study provides a reference for enterprises in industrial clusters to use knowledge-based capabilities to enhance their competitive advantage. Future Research: Future research could collect data from various countries and regions to test the research model and conduct a comparative analysis of industrial clusters.

Aim/Purpose: This research aims to examine the role of Knowledge Management (KM) infrastructure (technological, structural, and cultural) in enhancing job satisfaction in the context of developing countries, as exemplified by Jordan. Background: Despite the presence of job satisfaction studies conducted in educational institutions across the world, knowledge management issues have not been taken into consideration as influencing factors. Methodology: A total of 168 responses to a questionnaire survey were collected from the academic staff at Zarqa University in Jordan. Multiple regression analysis was conducted to test the research hypotheses. Contribution: This study offers deeper understanding about the role that knowledge management infrastructure plays in enhancing job satisfaction from a developing country perspective. The proposed model is tested the first time in Jordan. Findings: Results of the current study revealed that there are significant positive impacts of technological and cultural KM infrastructures on job satisfaction, whereas structural KM infrastructure does not have a significant impact on job satisfaction. Also, the results revealed significant gender difference in perception of the impact of knowledge management infrastructure on job satisfaction. On the other hand, an ANOVA test found no significant difference in the impact of knowledge management infrastructure on job satisfaction among groups by age, experience, and academic rank. Recommendation for Researchers: Our findings can be used as a base of knowledge for further studies about knowledge management infrastructure and job satisfaction following different criteria and research procedures. Future Research: The current model can be applied and assessed further in other sectors, including public universities and other services sectors in developed and developing countries.

Aim/Purpose: Based on business-IT alignment, this study addresses the understudied practice of DevOps. Background: Although organizations continue to implement DevOps practices, few studies explore connections with prior theory. This study contributes to this need by developing the DevOps strategic IT alignment model. Methodology: The sample included 57 firms from the current Forbes Global 2000 and the Fortune 500 lists. The authors employed partial least squares structural equation modeling (PLS-SEM) to evaluate the DevOps IT alignment model. Contribution: The proposed model builds a foundation for further investigation into the influence of theory on DevOps using quantitative research methods. It also contributes to a reliable and valid DevOps instrument for future exploration. Findings: Continuous integration of software and knowledge sharing increases the level of IT subunit alignment in large organizations that foster DevOps. Furthermore, practicing DevOps positively influences the level of business-IT alignment. Recommendations for Practitioners: Organizations that cultivate DevOps experience greater levels of business-IT alignment through stronger knowledge sharing and continuous integration of applications. Thus, managers should identify how to develop closer bonds between subunits with dissimilar skillsets in their organizations. Recommendation for Researchers: Researchers should explore how theories interact, help, and/or do not support blossoming practices like DevOps. Impact on Society: Stronger bonds increase knowledge sharing between interdepartmental colleagues. Lower hierarchical levels of an organization as well as higher managerial levels benefit from cross-domain IT knowledge. Future Research: It is important to explore how different types of knowledge in diverse disciplines requires unique cross-discipline bonds to form and whether these relationships have connections with the contingency theory and quality management.

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.

Aim/Purpose: This study investigates social media use and its effect on knowledge sharing. Based on the review of related literature, we hypothesised that social media use has a significant effect on outward and inward knowledge sharing. Background: While the notion of social media use in work organisations has been progressively developed, empirical studies linking social media to the context of knowledge sharing have only begun to emerge. Even so, literature on social media use and its impact on public organisation is still tentative and remains a developing area. Methodology: The partial least square method was utilised in testing of hypotheses with data collected from 103 employees, who by virtue of their position and job function(s) interface with the public for the purpose of sharing knowledge via the social media space. Contribution: The study made contributions to the social knowledge management literature in two ways. First, the study developed a research model that links social media use to the two distinct dimensions of knowledge sharing. Second, the study provides a quantitative approach, where statistical techniques were applied to validate the social media use and knowledge sharing link. Findings: Statistically, the public organisations utilise social media partly for knowledge sharing, with its effect being significant on outward knowledge sharing and insignificant on inward knowledge sharing. This indicates that social media were deployed mainly for information dissemination “outward knowledge sharing” and not for stakeholders’ feedback and interaction “inward knowledge sharing”. Recommendations for Practitioners: Public organisations should develop a policy framework and guidelines for social media use to encourage the full use of this technology to inform and interact with stakeholders. It is important for this policy document to adopt best practices regarding interactive spaces so that both knowledge sharing dimensions manifest themselves in social media communications. Second, it is necessary to carry out staff training for the professional use of this technology for knowledge sharing. Recommendation for Researchers: Future studies should extend to more populations in different contexts to validate findings Impact on Society: This paper intends to influence practices adopted by organisations in the public sector to improve the knowledge sharing dimensions via the social media space. Future Research: Future studies may extend to public organisations in other geographical locations around Nigeria. It will be useful for studies to provide an international perspective by sampling public organisations from different countries or by comparing and contrasting the findings of other studies, specifically those from other countries. A longitudinal study should be encouraged to detect advancement or development with regards to the subject matter over a period of time.

Aim/Purpose: The purpose of this study is to investigate the mechanism of the front/back-office structure affecting new service development (NSD) performance and examine the role of knowledge transfer in the relationship between front/back-office structure and NSD. Background: The separation of front and back-office has become the prevailing trend of the organizational transformation of modern service enterprises in the digital era. Yet, the influence of front and back-office separation dealing with new service development has not been widely researched. Methodology: Building on the internal social capital perspective, a multivariate regression analysis was conducted to investigate the impact of front/back-office structure on the NSD performance through knowledge transfer as an intermediate variable. The data was collected through a survey questionnaire from 198 project-level officers in the commercial banking industry of China. Contribution: This study advances the understanding of front/back-office structure’s influence mechanism on new service development activity. It reveals that knowledge transfer plays a critical role in bridging the impact of front and back-office separation to NSD performance under the trend of digitalization of service organizations. Findings: This study verified the positive effects of front/back-office social capital on NSD performance. Moreover, knowledge transfer predicted the variation in NSD performance and fully mediated the effect of front/back-office social capital on NSD performance. Recommendations for Practitioners: Service organizations should optimize knowledge transfer by promoting the social capital between front and back-office to overcome the negative effect organizational separation brings to NSD. Service and other organizations could explore developing an internal social network management platform, by which the internal social network could be visualized and dynamically managed. Recommendation for Researchers: The introduction of information and communications technology not only divides the organization into front and back-office, but also reduces the face-to-face customer contact. The impacts of new forms of customer contact to new service development and knowledge transfer between customer and service organizations call for further research. Along with the digital servitization, some manufacturing organizations also separate front and back-offices. The current model can be applied and assessed further in manufacturing and other service sectors. Impact on Society: The conclusion of this study guides us to pay attention to the construction of social capital inside organizations with front/back-office structure and implicates introducing and developing sociotechnical theory in front/back-office issue undergoing technological revolution. Future Research: As this study is based on the retail banking industry, similar studies are called upon in other service sectors to identify differences and draw more general conclusions. In addition, as the front and back-offices are being replaced increasingly by information technology such as artificial intelligence (AI), it is necessary to advance the research on front/back-office research with a new theoretical perspective, such as sociotechnical theory.

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 main goal of this systematic literature review was to look for studies that provide information relevant to business intelligence’s (BI) framework development and implementation in the tourism sector. This paper tries to classify the tourism sectors where BI is implemented, group various BI functionalities, and identify common problems encountered by previous research. Background: There has been an increased need for BI implementation to support decision-making in the tourism sector. Tourism stakeholders such as management of destination, accommodation, transportation, and public administration need a guideline to understand functional requirements before implementation. This paper addresses the problem by comprehensively reviewing the functionalities and issues that need to be considered based on previous business intelligence framework development and implementation in tourism sectors. Methodology: We have conducted a systematic literature review using the Preferred Reporting Items for Systematic Reviews and Guidelines for Meta-Analysis (PRISMA) method. The search is conducted using online academic database platforms, resulting in 543 initial articles published from 2002 to 2022. Contribution: The paper could be of interest to relevant stakeholders in the tourism industry because it provides an overview of the capabilities and limitations of business intelligence for tourism. To our knowledge, this is the first study to identify and classify the BI functionalities needed for tourism sectors and implementation issues related to organizations, people, and technologies that need to be considered. Findings: BI functionalities identified in this study include basic functions such as data analysis, reports, dashboards, data visualization, performance metrics, and key performance indicator, and advanced functions such as predictive analytics, trend indicators, strategic planning tools, profitability analysis, benchmarking, budgeting, and forecasting. When implementing BI, the issues that need to be considered include organizational, people and process, and technological issues. Recommendations for Practitioners: As data is a major issue in BI implementation, tourism stakeholders, especially in developing countries, may need to build a tourism data center or centralized coordination regulated by the government. They can implement basic functions first before implementing more advanced features later. Recommendation for Researchers: We recommend further studying the BI implementation barriers by employing a perspective of an adoption framework such as the technology, organization, and environment (TOE) framework. Impact on Society: This research has a potential impact on improving the tourism industry’s performance by providing insight to stakeholders about what is needed to help them make more accurate decisions using business intelligence. Future Research: Future research may involve collaboration between practitioners and academics in developing various BI architectures specific to each tourism industry, such as destination management, hospitality, or transportation.

Aim/Purpose: This study examines the impact of decision-making, crisis management, and decision-making on sustainability through the mediation of open innovation in the energy sector. Background: Public companies study high-performance practices, requiring overcoming basic obstacles such as financial crises that prevent the adoption and development of sustainability programs. Methodology: Due to the COVID-19 pandemic, which has led to the closure of businesses in Iraq, a survey was distributed. To facilitate responses, free consultations were offered to help complete the questionnaire quickly. Of the 435 questionnaires answered, 397 were used for further analysis. Contribution: The impact of crises that impede the energy sector from adopting sustainable environmental regulations is investigated in this study. Its identification of specific constraints to open innovation leads to the effectiveness of adopting environmentally friendly policies and reaching high levels of sustainable performance. Findings: The impacts of risk-taking, crisis management, and decision-making on sustainability have been explored. Results show that open innovation fully mediates the relationship between the factors of risk-taking, crisis management, decision-making, and sustainability. Recommendations for Practitioners: The proposed model can be used by practitioners to develop and improve sustainable innovation practices and achieve superior performance. Recommendation for Researchers: Researchers are recommended to conduct in-depth studies of the phenomenon based on theoretical and empirical foundations, especially in light of the relationship between crisis management, decision-making, and risk-taking and their impact on sustainability based on linear and non-compensatory relationships. Impact on Society: This study provides a reference for organizations with similar cultural backgrounds in adopting sustainable practices to minimize pollution in the Iraqi context. Future Research: A more in-depth study can be performed using a larger sample, which not only includes the energy industry but also other industries.

Aim/Purpose: Systematic literature reviews seek to locate all studies that contain material of relevance to a research question and to synthesize the relevant outcomes of those studies. The primary aim of this paper was to synthesize both research and practice reports on micro-credentials (MCRs). Background: There has been an increase in reports and research on the plausibility of MCRs to support dynamic human skills development for an increasingly impatient and rapidly changing digital world. The integration of fast-paced emerging technologies and digitalization necessitate alternative learning paradigms. MCRs offer time, financial, and space flexibility and can be stacked into a larger qualification, thereby allowing for a broader range of transdisciplinary competencies within a qualification. However, MCRs often lack the academic rigor required for accreditation within existing disciplines. Methodology: The study followed the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta Analyses), which offers a rigorous method to enhance reporting quality. The study used both academic research and practice reports. Contribution: The paper makes a theoretical contribution to the discourse about the need for innovation within existing educational paradigms for continued relevance in a changing world. It also contributes to the debate on the role of MCRs in bridging the gap between practice and academia despite the growing difference between their interests, and the role that MCRs play in the social-economic plans of countries. Findings: The key findings are that investments in MCRs are mainly in the Science, Technology, Engineering and Mathematics (STEM) and Education sectors, and have taken place mainly in high-income countries and regions – contexts that particularly value practice-accredited MCRs. Low-income countries, by contrast, remain traditional and insist on MCRs that are formally accredited by a recognized academic institution. This contributes to a widening skills gap between low- and high-income countries or regions, which results in greater global disparities. There is also a growing divide between academia and practice concerning their interest in MCRs (a reflection of the rigor versus relevance debate), which partially explains why many global and larger organizations have gone on to create their own learning institutions. Recommendations for Practitioners: We recommend that educational mechanisms consider the critical importance of MCRs as part of innovative efforts for life-wide (different sectors) and life-long (same sector) learning, especially in low-income countries. MCRs provide dynamic mechanisms to fill skills gaps in an increasing ruthless international battle for talent. Recommendation for Researchers: We recommend focused research into skills and career pathways using MCRs while at the same time remaining responsive to transdisciplinary efforts and sensitive to global and local changes within any sector. Impact on Society: Work and society have transformed over time, and more so in the new digital age, yet academia has been slow in adapting to the changes, forcing organizations to create their own learning institutions or to use MCRs to fill the skills gap. The purpose of education goes beyond preparing individuals for work, extending further to creating an environment where individuals and governments seek their own social and economic outcomes. MCRs provide a flexible means for co-creation between individuals, education, organizations, and government that could stem global rising unemployment, social exclusion, and redundancy. Future Research: Future research should focus on the co-creation of MCRs between practitioners and academia.

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: In this study, we aim to investigate the impact of an important characteristic of textual reviews – the diversity of the review content on review helpfulness. Background: Consumer-generated reviews are an essential format of online Word-of-Month that help customers reduce uncertainty and information asymmetry. However, not all reviews are equally helpful as reflected by the varying number of helpfulness votes received by reviews. From consumers’ perspective, what kind of content is more effective and useful for making purchase decisions is unclear. Methodology: We use a data set consisting of consumer reviews for laptop products on Amazon from 2014 to 2018. A topic modeling technique is implemented to unveil the hidden topics embedded in the reviews. Based on the extracted topics, we compute the text diversity score of each review. The diversity score measures how diverse the content in a review is compared to other reviews. Contribution: In the literature, studies have examined various factors that can influence review helpfulness. However, studies that emphasized the information value of textual reviews are limited. Our study contributes to the extant literature of online word-of-mouth by establishing the connection between the diversity of the review content and consumer perceived helpfulness. Findings: Empirical results show that text diversity plays an important role in consumers’ evaluation of whether the review is helpful. Reviews that contain more diverse content tend to be more helpful to consumers. Moreover, we find a negative interaction effect between text diversity and the text depth. This result suggests that text depth and text diversity have a substitution effect. When a review contains more in-depth content, the impact of text diversity is weakened. Recommendations for Practitioners: For consumers to quickly find the informative reviews, platforms should incorporate measures such as text diversity in the ranking algorithms to rank consumer reviews. Future Research: Future study can extend the current research by examine the impact of text diversity for experienced goods and compare the results with search goods.

Aim/Purpose: The purpose of this study is to identify the key determinants hindering Knowledge Transfer (KT) practices for Information Technology Project Managers (ITPMs) Background: The failure rate of IT projects remains unacceptably high worldwide, and KT between project managers and team members has been recognized as a significant issue affecting project success. Therefore, this study tries to identify the determinants of KT within the context of IT projects for ITPMs. Methodology: A systematic review of the literature (SLR) was employed in the investigation. The SLR found 28 primary studies on KT for ITPMs that were published in Scopus and Web of Science databases between 2010 and 2023. Contribution: Social Cognitive Theory (SCT) was used to build a theoretical framework where the determinants were categorized into Personal factors, Environmental (Project organizational) factors, and other factors, such as Technological factors influencing ITPMs (Behavioral factors), to implement in KT practices. Findings: The review identified 11 key determinants categorized into three broad categories: Personal factors (i.e., motivation, absorptive capability, trust, time urgency), Project Organizational factors (i.e., team structure, leadership style, reward system, organizational culture, communication), and Technological factors (i.e., project task collaboration tool and IT infrastructure and support) that influence implementing KT for ITPMs Recommendations for Practitioners: The proposed framework in this paper can be used by project managers as a guide to adopt KT practices within their project organization. Recommendation for Researchers: The review showed that some determinants, such as Technological factors, have not been adequately explored in the existing KT model in the IT projects context and can be integrated with other relevant theories to understand how a project manager’s knowledge can be transferred and retained in the organization using technology in future research. Impact on Society: This study emphasizes the role of individual actions and project organizational and technological matters in shaping the efficacy of KT within project organizations. It offers insight that could steer business owners or executives within project organizations to closely observe the behavior of project managers, thereby securing successful project outcomes. Future Research: The determinant list provided in this paper is acquired from extensive SLR and, therefore, further research should aim to expand and deepen the investigation by validating these determinants from experts in the field of IT and project management. Future studies can also add other external technological determinants to provide a more comprehensive KT implementation framework. Similarly, this research does not include determinants identified directly from the industry, as it relies solely on determinants found in the existing literature. Although a comprehensive attempt has been made to encompass all relevant papers, there remains a potential for overlooking some research in this process.

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: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

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: Blockchain technology (BCT) has emerged as a potential catalyst for transforming government institutions and services, yet the adoption of blockchain in governments faces various challenges, for which previous studies have yet to provide practical solutions. Background: This study aims to identify and analyse barriers, potential solutions, and their relations in implementing BC for governments through a systematic literature review (SLR). The authors grouped the challenges based on the Technology-Organisation-Environment (TOE) framework while exercising a thematic grouping for the solutions, followed by a comprehensive mapping to unveil the relationship between challenges and solutions. Methodology: This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, combined with the tollgate method, to improve the quality of selected articles. The authors further administer a three-level approach (open coding, axial coding, and selective coding) to analyse the challenges and solutions from the articles. Contribution: The authors argue that this study enriches the existing literature on BC adoption, particularly in the government context, by providing a comprehensive framework to analyse and address the unique challenges and solutions, thus contributing to the development of new theories and models for future research in BC adoption in government settings and fostering deeper exploration in the field. Findings: The authors have unveiled 40 adoption challenges categorised using the TOE framework. The most prevalent technological challenges include security concerns and integration & interoperability, while cultural resistance, lack of support and involvement, and employees’ capability hinder adoption at the organisational level. Notably, the environmental dimension lacks legal and standard frameworks. The study further unveils 28 potential solutions, encompassing legal frameworks, security and privacy measures, collaboration and governance, technological readiness and infrastructure, and strategic planning and adoption. The authors of the study have further mapped the solutions to the identified challenges, revealing that the establishment of legal frameworks stands out as the most comprehensive solution. Recommendations for Practitioners: Our findings provide a big picture regarding BC adoption for governments around the globe. This study charts the problems commonly encountered by government agencies and presents proven solutions in their wake. The authors endeavour practitioners, particularly those in governments, to embrace our findings as the cornerstone of BC/BCT adoption. These insights can aid practitioners in identifying existing or potential obstacles in adopting BC, pinpointing success factors, and formulating strategies tailored to their organisations. Recommendation for Researchers: Researchers could extend this study by making an in-depth analysis of challenges or solutions in specific types of countries, such as developed and developing countries, as the authors believe this approach would yield more insights. Researchers could also test, validate, and verify the mapping in this study to improve the quality of the study further and thus can be a great aid for governments to adopt BC/BCT fully. Impact on Society: This study provides a comprehensive exploration of BC adoption in the government context, offering detailed explanations and valuable insights that hold significant value for government policymakers and decision-makers, serving as a bedrock for successful implementation by addressing roadblocks and emphasising the importance of establishing a supportive culture and structure, engaging stakeholders, and addressing security and privacy concerns, ultimately enhancing the efficiency and effectiveness of BC adoption in government institutions and services. Future Research: Future research should address the limitations identified in this study by expanding the scope of the literature search to include previously inaccessible sources and exploring alternative frameworks to capture dynamic changes and contextual factors in BC adoption. Additionally, rigorous scrutiny, review, and testing are essential to establish the practical and theoretical validity of the identified solutions, while in-depth analyses of country-specific and regional challenges will provide valuable insights into the unique considerations faced by different governments.

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: 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 rise of modern artificial intelligence (AI), in particular, machine learning (ML), has provided new opportunities and directions for knowledge management (KM). A central question for the future of KM is whether it will be dominated by an automation strategy that replaces knowledge work or whether it will support a knowledge-enablement strategy that enhances knowledge work and uplifts knowledge workers. This paper addresses this question by re-examining and updating a critical argument against KM by the sociologist of science Steve Fuller (2002), who held that KM was extractive and exploitative from its origins. Background: This paper re-examines Fuller’s argument in light of current developments in artificial intelligence and knowledge management technologies. It reviews Fuller’s arguments in its original context wherein expert systems and knowledge engineering were influential paradigms in KM, and it then considers how the arguments put forward are given new life in light of current developments in AI and efforts to incorporate AI in the KM technical stack. The paper shows that conceptions of tacit knowledge play a key role in answering the question of whether an automating or enabling strategy will dominate. It shows that a better understanding of tacit knowledge, as reflected in more recent literature, supports an enabling vision. Methodology: The paper uses a conceptual analysis methodology grounded in epistemology and knowledge studies. It reviews a set of historically important works in the field of knowledge management and identifies and analyzes their core concepts and conceptual structure. Contribution: The paper shows that KM has had a faulty conception of tacit knowledge from its origins and that this conception lends credibility to an extractive vision supportive of replacement automation strategies. The paper then shows that recent scholarship on tacit knowledge and related forms of reasoning, in particular, abduction, provide a more theoretically robust conception of tacit knowledge that supports the centrality of human knowledge and knowledge workers against replacement automation strategies. The paper provides new insights into tacit knowledge and human reasoning vis-à-vis knowledge work. It lays the foundation for KM as a field with an independent, ethically defensible approach to technology-based business strategies that can leverage AI without becoming a merely supporting field for AI. Findings: Fuller’s argument is forceful when updated with examples from current AI technologies such as deep learning (DL) (e.g., image recognition algorithms) and large language models (LLMs) such as ChatGPT. Fuller’s view that KM presupposed a specific epistemology in which knowledge can be extracted into embodied (computerized) but disembedded (decontextualized) information applies to current forms of AI, such as machine learning, as much as it does to expert systems. Fuller’s concept of expertise is narrower than necessary for the context of KM but can be expanded to other forms of knowledge work. His account of the social dynamics of expertise as professionalism can be expanded as well and fits more plausibly in corporate contexts. The concept of tacit knowledge that has dominated the KM literature from its origins is overly simplistic and outdated. As such, it supports an extractive view of KM. More recent scholarship on tacit knowledge shows it is a complex and variegated concept. In particular, current work on tacit knowledge is developing a more theoretically robust and detailed conception of human knowledge that shows its centrality in organizations as a driver of innovation and higher-order thinking. These new understandings of tacit knowledge support a non-extractive, human enabling view of KM in relation to AI. Recommendations for Practitioners: Practitioners can use the findings of the paper to consider ways to implement KM technologies in ways that do not neglect the importance of tacit knowledge in automation projects (which neglect often leads to failure). They should also consider how to enhance and fully leverage tacit knowledge through AI technologies and augment human knowledge. Recommendation for Researchers: Researchers can use these findings as a conceptual framework in research concerning the impact of AI on knowledge work. In particular, the distinction between replacement and enabling technologies, and the analysis of tacit knowledge as a structural concept, can be used to categorize and analyze AI technologies relative to KM research objectives. Impact on Society: The potential of AI on employment in the knowledge economy is a major issue in the ethics of AI literature and is widely recognized in the popular press as one of the pressing societal risks created by AI and specific types such as generative AI. This paper shows that KM, as a field of research and practice, does not need to and should not add to the risks created by automation-replacement strategies. Rather, KM has the conceptual resources to pursue a (human) knowledge enablement approach that can stand as a viable alternative to the automation-replacement vision. Future Research: The findings of the paper suggest a number of research trajectories. They include: Further study of tacit knowledge and its underlying cognitive mechanisms and structures in relation to knowledge work and KM objectives. Research into different types of knowledge work and knowledge processes and the role that tacit and explicit knowledge play. Research into the relation between KM and automation in terms of KM’s history and current technical developments. Research into how AI arguments knowledge works and how KM can provide an enabling framework.

Aim/Purpose: This conceptual review analyzes the designs of global health virtual communities of practice (VCoPs) programming reported in the empirical literature and proposes a new typology of their functioning. The purpose of this review is to provide clarity on VCoP learning stages of (co)evolution and insight into VCoP (re)development efforts to best meet member, organization, and network needs against an ever-evolving landscape of complexity in global health. Background: Since the COVID-19 pandemic, the field of global health has seen an uptick in the use of VCoPs to support continuous learning and improve health outcomes. However, evidence of how different combinations of programmatic designs impact opportunities for learning and development is lacking, and how VCoPs evolve as learning networks has yet to be explored. Methodology: Following an extensive search for literature in six databases, thematic analysis was conducted on 13 articles meeting the inclusion criteria. This led to the development and discussion of a new typology of VCoP phases of learning (co)evolution. Contribution: Knowledge gained from this review and the new categorization of VCoPs can support the functioning and evaluation of global health training programs. It can also provide a foundation for future research on how VCoPs influence the culture of learning organizations and networks. Findings: Synthesis of findings resulted in the categorization of global health VCoPs into five stages (slightly evolving, somewhat revolving, moderately revolving, highly revolving, and coevolving) across four design domains (network development, general member engagement before/after sessions, general member engagement during sessions, and session leadership). All global health VCoPs reviewed showed signs of adaptation and recommended future evolution. Recommendations for Practitioners: VCoP practitioners should pay close attention to how the structured flexibility of partnerships, design, and relationship development/accountability may promote or hinder VcoP’s continued evolution. Practitioners should shift perspective from short to mid- and long-term VCoP planning. Recommendation for Researchers: The new typology can stimulate further research to strengthen the clarity of language and findings related to VCoP functioning. Impact on Society: VCoPs are utilized by academic institutions, the private sector, non-profit organizations, the government, and other entities to fill gaps in adult learning at scale. The contextual implementation of findings from this study may impact VCoP design and drive improvements in opportunities for learning, global health, and well-being. Future Research: Moving forward, future research could explore how VCoP evaluations relate to different stages of learning, consider evaluation stages across the totality of VCoP programming design, and explore how best to capture VCoP (long-term) impact attributed to health outcomes and the culture of learning organizations and networks.

Aim/Purpose: Accurate detection of vacant parking spaces is crucial for autonomous parking. Deep learning, particularly Graph Neural Networks (GNNs), holds promise for addressing the challenges of diverse parking lot appearances and complex visual environments. Our GNN-based approach leverages the spatial layout of detected marking points in around-view images to learn robust feature representations that are resilient to occlusions and lighting variations. We demonstrate significant accuracy improvements on benchmark datasets compared to existing methods, showcasing the effectiveness of our GNN-based solution. Further research is needed to explore the scalability and generalizability of this approach in real-world scenarios and to consider the potential ethical implications of autonomous parking technologies. Background: GNNs offer a number of advantages over traditional parking spot detection methods. Unlike methods that treat objects as discrete entities, GNNs may leverage the inherent connections among parking markers (lines, dots) inside an image. This ability to exploit spatial connections leads to more accurate parking space detection, even in challenging scenarios with shifting illumination. Real-time applications are another area where GNNs exhibit promise, which is critical for autonomous vehicles. Their ability to intuitively understand linkages across marking sites may further simplify the process compared to traditional deep-learning approaches that need complex feature development. Furthermore, the proposed GNN model streamlines parking space recognition by potentially combining slot inference and marking point recognition in a single step. All things considered, GNNs present a viable method for obtaining stronger and more precise parking slot recognition, opening the door for autonomous car self-parking technology developments. Methodology: The proposed research introduces a novel, end-to-end trainable method for parking slot detection using bird’s-eye images and GNNs. The approach involves a two-stage process. First, a marking-point detector network is employed to identify potential parking markers, extracting features such as confidence scores and positions. After refining these detections, a marking-point encoder network extracts and embeds location and appearance information. The enhanced data is then loaded into a fully linked network, with each node representing a marker. An attentional GNN is then utilized to leverage the spatial relationships between neighbors, allowing for selective information aggregation and capturing intricate interactions. Finally, a dedicated entrance line discriminator network, trained on GNN outputs, classifies pairs of markers as potential entry lines based on learned node attributes. This multi-stage approach, evaluated on benchmark datasets, aims to achieve robust and accurate parking slot detection even in diverse and challenging environments. Contribution: The present study makes a significant contribution to the parking slot detection domain by introducing an attentional GNN-based approach that capitalizes on the spatial relationships between marking points for enhanced robustness. Additionally, the paper offers a fully trainable end-to-end model that eliminates the need for manual post-processing, thereby streamlining the process. Furthermore, the study reduces training costs by dispensing with the need for detailed annotations of marking point properties, thereby making it more accessible and cost-effective. Findings: The goal of this research is to present a unique approach to parking space recognition using GNNs and bird’s-eye photos. The study’s findings demonstrated significant improvements over earlier algorithms, with accuracy on par with the state-of-the-art DMPR-PS method. Moreover, the suggested method provides a fully trainable solution with less reliance on manually specified rules and more economical training needs. One crucial component of this approach is the GNN’s performance. By making use of the spatial correlations between marking locations, the GNN delivers greater accuracy and recall than a completely linked baseline. The GNN successfully learns discriminative features by separating paired marking points (creating parking spots) from unpaired ones, according to further analysis using cosine similarity. There are restrictions, though, especially where there are unclear markings. Successful parking slot identification in various circumstances proves the recommended method’s usefulness, with occasional failures in poor visibility conditions. Future work addresses these limitations and explores adapting the model to different image formats (e.g., side-view) and scenarios without relying on prior entry line information. An ablation study is conducted to investigate the impact of different backbone architectures on image feature extraction. The results reveal that VGG16 is optimal for balancing accuracy and real-time processing requirements. Recommendations for Practitioners: Developers of parking systems are encouraged to incorporate GNN-based techniques into their autonomous parking systems, as these methods exhibit enhanced accuracy and robustness when handling a wide range of parking scenarios. Furthermore, attention mechanisms within deep learning models can provide significant advantages for tasks that involve spatial relationships and contextual information in other vision-based applications. Recommendation for Researchers: Further research is necessary to assess the effectiveness of GNN-based methods in real-world situations. To obtain accurate results, it is important to employ large-scale datasets that include diverse lighting conditions, parking layouts, and vehicle types. Incorporating semantic information such as parking signs and lane markings into GNN models can enhance their ability to interpret and understand context. Moreover, it is crucial to address ethical concerns, including privacy, potential biases, and responsible deployment, in the development of autonomous parking technologies. Impact on Society: Optimized utilization of parking spaces can help cities manage parking resources efficiently, thereby reducing traffic congestion and fuel consumption. Automating parking processes can also enhance accessibility and provide safer and more convenient parking experiences, especially for individuals with disabilities. The development of dependable parking capabilities for autonomous vehicles can also contribute to smoother traffic flow, potentially reducing accidents and positively impacting society. Future Research: Developing and optimizing graph neural network-based models for real-time deployment in autonomous vehicles with limited resources is a critical objective. Investigating the integration of GNNs with other deep learning techniques for multi-modal parking slot detection, radar, and other sensors is essential for enhancing the understanding of the environment. Lastly, it is crucial to develop explainable AI methods to elucidate the decision-making processes of GNN models in parking slot detection, ensuring fairness, transparency, and responsible utilization of this technology.

Aim/Purpose: This study aims to develop a solution for personalized tourism recommendations that addresses information overload, data sparsity, and the cold-start problem. It focuses on enabling tourists to choose the most suitable tourism-related facilities, such as restaurants and hotels, that match their individual needs and preferences. Background: The tourism industry is experiencing a significant shift towards digitalization due to the increasing use of online platforms and the abundance of user data. Travelers now heavily rely on online resources to explore destinations and associated options like hotels, restaurants, attractions, transportation, and events. In this dynamic landscape, personalized recommendation systems play a crucial role in enhancing user experience and ensuring customer satisfaction. However, existing recommendation systems encounter major challenges in precisely understanding the complexities of user preferences within the tourism domain. Traditional approaches often rely solely on user ratings, neglecting the complex nature of travel choices. Data sparsity further complicates the issue, as users might have limited interactions with the system or incomplete preference profiles. This sparsity can hinder the effectiveness of these systems, leading to inaccurate or irrelevant recommendations. The cold-start problem presents another challenge, particularly with new users who lack a substantial interaction history within the system, thereby complicating the task of recommending relevant options. These limitations can greatly hinder the performance of recommendation systems and ultimately reduce user satisfaction with the overall experience. Methodology: The proposed User-based Multi-Criteria Trust-aware Collaborative Filtering (UMCTCF) approach exploits two key aspects to enhance both the accuracy and coverage of recommendations within tourism recommender systems: multi-criteria user preferences and implicit trust networks. Multi-criteria ratings capture the various factors that influence user preferences for specific tourism items, such as restaurants or hotels. These factors surpass a simple one-star rating and take into account the complex nature of travel choices. Implicit trust relationships refer to connections between users that are established through shared interests and past interactions without the need for explicit trust declarations. By integrating these elements, UMCTCF aims to provide more accurate and reliable recommendations, especially when data sparsity limits the ability to accurately predict user preferences, particularly for new users. Furthermore, the approach employs a switch hybridization scheme, which combines predictions from different components within UMCTCF. This scheme leads to a more robust recommendation strategy by leveraging diverse sources of information. Extensive experiments were conducted using real-world tourism datasets encompassing restaurants and hotels to evaluate the effectiveness of UMCTCF. The performance of UMCTCF was then compared against baseline methods to assess its prediction accuracy and coverage. Contribution: This study introduces a novel and effective recommendation approach, UMCTCF, which addresses the limitations of existing methods in personalized tourism recommendations by offering several key contributions. First, it transcends simple item preferences by incorporating multi-criteria user preferences. This allows UMCTCF to consider the various factors that users prioritize when making tourism decisions, leading to a more comprehensive understanding of user choices and, ultimately, more accurate recommendations. Second, UMCTCF leverages the collective wisdom of users by incorporating an implicit trust network into the recommendation process. By incorporating these trust relationships into the recommendation process, UMCTCF enhances its effectiveness, particularly in scenarios with data sparsity or new users with limited interaction history. Finally, UMCTCF demonstrates robustness towards data sparsity and the cold-start problem. This resilience in situations with limited data or incomplete user profiles makes UMCTCF particularly suitable for real-world applications in the tourism domain. Findings: The results consistently demonstrated UMCTCF’s superiority in key metrics, effectively addressing the challenges of data sparsity and new users while enhancing both prediction accuracy and coverage. In terms of prediction accuracy, UMCTCF yielded significantly more accurate predictions of user preferences for tourism items compared to baseline methods. Furthermore, UMCTCF achieved superior coverage compared to baseline methods, signifying its ability to recommend a wider range of tourism items, particularly for new users who might have limited interaction history within the system. This increased coverage has the potential to enhance user satisfaction by offering a more diverse and enriching set of recommendations. These findings collectively highlight the effectiveness of UMCTCF in addressing the challenges of personalized tourism recommendations, paving the way for improved user satisfaction and decision-making within the tourism domain. Recommendations for Practitioners: The proposed UMCTCF approach offers a potential opportunity for tourism recommendation systems, enabling practitioners to create solutions that prioritize the needs and preferences of users. By incorporating UMCTCF into online tourism platforms, tourists can utilize its capabilities to make well-informed decisions when selecting tourism-related facilities. Furthermore, UMCTCF’s robust design allows it to function effectively even in scenarios with data sparsity or new users with limited interaction history. This characteristic makes UMCTCF particularly valuable for real-world applications, especially in scenarios where these limitations are common obstacles. Recommendation for Researchers: The success of UMCTCF can open up new avenues in personalized recommendation research. One promising direction lies in exploring the integration of additional contextual information, such as temporal (time-based) or location-based information. By incorporating these elements, the model could be further improved, allowing for even more personalized recommendations. Furthermore, exploring the potential of UMCTCF in domains other than tourism has considerable significance. By exploring its effectiveness in other e-commerce domains, researchers can broaden the impact of UMCTCF and contribute to the advancement of personalized recommendation systems across various industries. Impact on Society: UMCTCF has the potential to make a positive impact on society in various ways. By delivering accurate and diverse recommendations that are tailored to individual user preferences, UMCTCF fosters a more positive and rewarding user experience with tourism recommendation systems. This can lead to increased user engagement with tourism platforms, ultimately enhancing overall satisfaction with travel planning. Furthermore, UMCTCF enables users to make more informed decisions through broader and more accurate recommendations, potentially reducing planning stress and leading to more fulfilling travel experiences. Future Research: Expanding upon the success of UMCTCF, future research activities can explore several promising paths. Enriching UMCTCF with various contextual data, such as spatial or location-based data, to enhance recommendation accuracy and relevance. Leveraging user-generated content, like reviews and social media posts, could provide deeper insights into user preferences and sentiments, improving personalization. Additionally, applying UMCTCF in various e-commerce domains beyond tourism, such as online shopping, entertainment, and healthcare, could yield valuable insights and enhance recommendation systems. Finally, exploring the integration of optimization algorithms could improve both recommendation accuracy and efficiency.

Aim/Purpose: This research aims to develop a smart agricultural knowledge management framework to empower emergent farmers and extension officers (advisors to farmers) in developing countries as part of a smart farming lab (SFL). The framework utilizes knowledge objects (KOs) to capture information and knowledge of different forms, including indigenous knowledge. It builds upon a foundation of established agricultural knowledge management (AKM) models and serves as the cornerstone for an envisioned SFL. This framework facilitates optimal decision support by fostering linkages between these KOs and relevant organizations, knowledge holders, and knowledge seekers within the SFL environment. Background: Emergent farmers and extension officers encounter numerous obstacles in their knowledge operations and decision-making. This includes limited access to agricultural information and difficulties in applying it effectively. Many lack reliable sources of support, and even when information is available, understanding and applying it to specific situations can be challenging. Additionally, extension offices struggle with operational decisions and knowledge management due to agricultural organizations operating isolated in silos, hindering their access to necessary knowledge. This research introduces an SFL with a proposed AKM process model aimed at transforming emergent farmers into smart, innovative entities by addressing these challenges. Methodology: This study is presented as a theory-concept paper and utilizes a literature review to evaluate and synthesize three distinct AKM models using several approaches. The results of the analysis are used to design a new AKM process model. Contribution: This research culminates in a new AKM process framework that incorporates the strengths of various existing AKM models and supports emergent farmers and extension officers to become smart, innovative entities. One main difference between the three models analyzed, and the one proposed in this research, is the deployment and use of knowledge assets in the form of KOs. The proposed framework also incorporates metadata and annotations to enhance knowledge discoverability and enable AI-powered applications to leverage captured knowledge effectively. In practical terms, it contributes by further motivating the use of KOs to enable the transfer and the capturing of organizational knowledge. Findings: A model for an SFL that incorporates the proposed agricultural knowledge management framework is presented. This model is part of a larger knowledge factory (KF). It includes feedback loops, KOs, and mechanisms to facilitate intelligent decision-making. The significance of fostering interconnected communities is emphasized through the creation of linkages. These communities consist of knowledge seekers and bearers, with information disseminated through social media and other communication integration platforms. Recommendations for Practitioners: Practitioners and other scholars should consider implementing the proposed AKM process model as part of a larger SFL to support emergent farmers and extension officers in making operational decisions and applying knowledge management strategies. Recommendation for Researchers: The AKM process model is only presented in conceptual form. Therefore, researchers can practically test and assess the new framework in an agricultural setting. They can also further explore the potential of social media integration platforms to connect knowledge seekers with knowledge holders. Impact on Society: The proposed AKM process model has the potential to support emergent farmers and extension officers in becoming smart, innovative entities, leading to improved agricultural practices and potentially contributing to food security. Future Research: This paper discusses the AKM process model in an agrarian setting, but it can also be applied in other domains, such as education and the healthcare sector. Future research can evaluate the model’s effectiveness and explore and further investigate the semantic web and social media integration.

Aim/Purpose: This study aims to identify the characteristics of YouTube video reviews that consumers utilize to evaluate review helpfulness and explores how they process such information. This study aims to investigate the effect of argument quality, review popularity, number of likes, and source credibility on consumers’ perception of YouTube’s video review helpfulness. Background: Video reviews posted on YouTube are an emerging form of online reviews, which have the potential to be more helpful than textual reviews due to their visual and audible cues that deliver more vivid information about product features and specifications. With the availability of an enormous number of video reviews with unpredictable quality, it becomes challenging for consumers to find helpful reviews without consuming significant time and effort. In addition, YouTube does not provide a specific feature that indicates a review helpfulness similar to the one found on e-commerce websites. Consequently, consumers have to examine the characteristics of video reviews that are readily available on YouTube, evaluate them, and form a perception of whether a review is helpful or not. Despite the increasing popularity of YouTube’s video reviews, video reviews’ helpfulness received inadequate attention in the literature. The antecedents of the helpfulness of online video reviews are still underinvestigated, and more research is needed to identify the characteristics that consumers depend upon to assess video review helpfulness. Furthermore, it is important to understand how consumers process the information they gain from these characteristics to form a perception of their helpfulness. Methodology: Following an extended investigation of the relevant literature, we identified four key video characteristics that consumers presumably utilize to evaluate review helpfulness on YouTube (i.e., review popularity, number of likes, source credibility, and argument quality). By employing the Elaboration Likelihood Model (ELM), we classified these characteristics along the central and peripheral routes. The central route characteristics require a high cognitive effort by consumers to process the review’s message and reach a logical decision. In contrast, the peripheral route assumes that consumers judge the review’s message based on superficial qualities without substantial cognitive effort. A research model is introduced to investigate the effect of central and peripheral cues and their corresponding video review characteristics on review helpfulness. Accordingly, argument quality is proposed in the central route of the model, while review popularity, number of likes, and source credibility are proposed in the peripheral route. Furthermore, the study investigates how consumers process the information they obtain from these routes jointly or independently. To empirically test the proposed model, a convenient sample of 361 YouTube users was obtained through an online survey. The partial least squares method was used to investigate the effect of the proposed characteristics on video review helpfulness. Contribution: This study contributes to the literature in several ways. First, it is one of the few studies that investigate online video reviews’ helpfulness. Second, this study identifies several unique characteristics of YouTube’s video reviews that span peripheral and central routes, which potentially contribute to review helpfulness. Third, this study proposes a conceptual model based on the ELM to explore the effect of central and peripheral cues and their corresponding review characteristics on review helpfulness. Fourth, the research findings provide implications for research and practice that advance the theoretical understanding of video reviews’ helpfulness and serve as guidelines to create more helpful video reviews by better understanding the consumer’s cognitive processes. Findings: The results show that among the four characteristics proposed in the research model, argument quality in the central route is the strongest determinant factor affecting video review helpfulness. Results also show that review popularity, source credibility, and the number of likes in the peripheral route have significant effects on video review helpfulness. Altogether, our results show that the effect of the peripheral route adds up to 0.463 compared to 0.430, which is the impact magnitude of the argument quality construct in the central route. Based on the comparable effect magnitude of the central and peripheral routes of the model on video review helpfulness, our results indicate that both peripheral and central cues significantly affect consumers’ perception of video review helpfulness. The two routes are not mutually exclusive, and their cues can be processed in parallel or consecutive ways. Recommendations for Practitioners: The study recommends creating a dedicated category for reviews on YouTube with a specific feature for consumers to indicate the helpfulness of a video review, similar to the helpful vote button in textual reviews. The study also recommends that reviewers deliver more appealing and convincing argument quality, work toward improving their credibility, and understand the factors that contribute to video popularity. Impact on Society: Identifying the characteristics that affect video review helpfulness on YouTube helps consumers access helpful reviews more efficiently and improves their purchase decisions. Future Research: Future research could look into different types of data that could be extracted from YouTube to investigate the helpfulness of online video reviews. Future studies could employ machine learning and sentiment analysis techniques to reach more insights. Future research could also investigate the effect of product types in the context of online video reviews.

Aim/Purpose: The objective of this study is to summarize the challenges of Agile Requirements Engineering (Agile RE) in the public sector in republican and constitutional monarchy nations. Additionally, it offers recommendations to address these challenges. Background: Failure of IT projects in the public sector results in financial losses for the state and loss of public trust, often attributed to issues in requirements engineering such as prioritization of user needs and excessive scope of requirements. IT projects can have a higher success rate with Agile RE, but there are also drawbacks. Therefore, this study holds significance by presenting a thorough framework designed to pinpoint and overcome the challenges associated with Agile RE to increase the success rate of IT projects. Methodology: This study employs a Systematic Literature Review (SLR) protocol in the field of software engineering or related domains, which consists of three main phases: planning the review, conducting the review with a snowballing approach, and reporting the review. Furthermore, the authors perform open coding to categorize challenges based on the Agile methodologies adoption factor model and axial coding to map potential solutions. Contribution: The authors assert that this research enriches the existing literature on Agile RE, specifically within the public sector context, by mapping out challenges and possible solutions that contribute to creating a foundation for future studies to conduct a more in-depth analysis of Agile adoption in the public sector. Furthermore, it compares the barriers of Agile RE in the public sector with the general context, leading to the discovery of new theories specifically for this field. Findings: Most challenges related to Agile RE in the public sector are found in the people and process aspects. Project and organizational-related are subsequent aspects. Therefore, handling people and processes proficiently is imperative within Agile RE to prevent project failure. Recommendations for Practitioners: Our findings offer a comprehensive view of Agile RE in the public sector in republican and constitutional monarchy nations. This study maps the challenges encountered by the public sector and provides potential solutions. The authors encourage practitioners to consider our findings as a foundation for adopting Agile methodology in the public sector. Furthermore, this study can assist practitioners in identifying existing barriers related to Agile RE, pinpointing elements that contribute to overcoming those challenges, and developing strategies based on the specific needs of the organizations. Recommendation for Researchers: Researchers have the potential to expand the scope of this study by conducting research in other countries, especially African countries, as this study has not yet encompassed this geographic region. Additionally, they can strengthen the evidence linking Agile RE challenges to the risk of Agile project failure by performing empirical validation in a specific country. Impact on Society: This research conducts a comprehensive exploration of Agile RE within the public sector, serving as a foundation for the successful adoption of Agile methodology by overcoming obstacles related to Agile RE. This study highlights the importance of managing people, processes, projects, and organizational elements to increase the success of Agile adoption in the public sector. Future Research: In the future, researchers should work towards resolving the limitations identified in this study. This study has not provided a clear prioritization of challenges and solutions according to their significance. Therefore, future researchers can perform a Fuzzy Analytical Hierarchical Process (F-AHP) to prioritize the proposed solutions.

Aim/Purpose: This study examined the use of Social Media Applications (SMAs) for accessing health-related information within a heterogeneous population in Jordan. The objective of this study was therefore threefold: (i) to investigate the usage of SMAs, including WhatsApp, Twitter, YouTube, Snapchat, Instagram, and Facebook, for accessing health-related information; (ii) to examine potential variations in the use of SMAs based on demographic and behavioral characteristics; and (iii) to identify the factors that can predict the use of SMAs. Background: There has been limited focus on investigating the behavior of laypeople in Jordan when it comes to seeking health information from SMAs. Methodology: A cross-sectional study was conducted among the general population in Jordan using an online questionnaire administered to 207 users. A purposive sampling technique was employed, wherein all the participants actively sought online health information. Descriptive statistics, t-tests, and regression analyses were utilized to analyze the collected data. Contribution: This study adds to the existing body of research on health information seeking from SMAs in developing countries, with a specific focus on Jordan. Moreover, laypeople, often disregarded by researchers and health information providers, are the most vulnerable individuals who warrant greater attention. Findings: The findings indicated that individuals often utilized YouTube as a platform to acquire health-related information, whereas their usage of Facebook for this purpose was less frequent. Participants rarely utilized Instagram and WhatsApp to obtain health information, while Twitter and Snapchat were very seldom used for this purpose. The variable of sex demonstrated a notable positive correlation with the utilization of YouTube and Twitter for the purpose of finding health-related information. Conversely, the variable of nationality exhibited a substantial positive correlation with the utilization of Facebook, Instagram, and Twitter. Consulting medical professionals regarding information obtained from the Internet was a strong indicator of using Instagram to search for health-related information. Recommendations for Practitioners: Based on the empirical results, this study provides feasible recommendations for the government, healthcare providers, and developers of SMAs. Recommendation for Researchers: Researchers should conduct separate investigations for each application specifically pertaining to the acquisition of health-related information. Additionally, it is advisable to investigate additional variables that may serve as predictors for the utilization of SMAs. Impact on Society: The objective of this study is to enhance the inclination of the general public in Jordan to utilize SMAs for health-related information while also maximizing the societal benefits of these applications. Future Research: Additional research is required to examine social media’s usability (regarding ease of use) and utility (comparing advantages to risks) in facilitating effective positive change and impact in healthcare.

In the context of asthma severity prediction, this study delves into the feature importance of various symptoms and demographic attributes. Leveraging a comprehensive dataset encompassing symptom occurrences across varying severity levels, this investigation employs visualisation techniques, such as stacked bar plots, to illustrate the distribution of symptomatology within different severity categories. Additionally, correlation coefficient analysis is applied to quantify the relationships between individual attributes and severity levels. Moreover, the study harnesses the power of random forest and the Gini importance methodology, essential tools in feature importance analytics, to discern the most influential predictors in asthma severity prediction. The experimental results bring to light compelling associations between certain symptoms, notably 'runny-nose' and 'nasal-congestion', and specific severity levels, elucidating their potential significance as pivotal predictive indicators. Conversely, demographic factors, encompassing age groups and gender, exhibit comparatively weaker correlations with symptomatology. These findings underscore the pivotal role of individual symptoms in characterising asthma severity, reinforcing the potential for feature importance analysis to enhance predictive models in the realm of asthma management and bioinformatics.

Recently, interest in the correlation between 'contract' in transaction governance and 'trust' in relational governance mechanisms has been growing. This study focuses on issues related to the evolution of contract and inter-organisational trust dynamics in transaction governance and uses mixed research method to investigate sectors related to transaction governance in Taiwan's electronics industry. The study finds higher flexibility in contract implementation to be a promoter of trust between two parties in a relationship, thereby promoting project execution efficiency in the case of Taiwanese firms. Organisational management differs between the East and West; therefore, Western firms should understand how various contractual provisions can be used to accommodate different transactions when cooperating with Taiwanese electronics companies.

The world's economies are attempting to transform themselves to have a greater focus on developing knowledge as a commodity through innovation. Innovation starts with a creative activity that yields an invention but is augmented through a systematic value driven knowledge management system to yield new knowledge that can create a competitive advantage. To succeed in such an economy, organisations must have or develop the talent that can produce and use information effectively, they must have an ambidextrous organisational structure that allows them to innovate and produce simultaneously, and they must have an innovation management system to sustain effective innovation. In this paper we show how to augment existing university courses to simultaneously develop subject matter and innovation skills in students. We also suggest the incorporation of the new Innovations Management System Standard Series ISO 56000 into business curricula to better prepare students to function in the knowledge economy.

In the context of US universities where student evaluations of teaching play an important role in the retention and promotion of faculty, it is important to understand what a student expects in the classroom. This study took the perspective of Perry's cognitive development scheme with the following research question: what is the Perry level of cognitive development of business students? An established survey was used at two different universities. It was found that the median was position 3, and that there was large variation in three dimensions. First is the variation across program levels. Second, there was variation across universities. This becomes an issue when instructors move to a different university and questions the possibility to transfer 'best practices'. Third, variation was found within a specific program level. This means that instructors are faced with students who, from a cognitive perspective, have different demands which are unlikely to be simultaneously met.

Large graphs or automata have their data that cannot fit in a single machine, or may take unreasonable time to be processed. We implement with MapReduce and Giraph two algorithms for intersecting and minimising large and distributed automata. We provide some comparative analysis, and the experiment results are depicted in figures. Our work experimentally validates our propositions as long as it shows that our choice, in comparison with MapReduce one, is not only more suitable for graph-oriented algorithms, but also speeds the executions up. This work is one of the first steps of a long-term goal that consists in a high level distributed graph processing language.

This study investigates cross-border commercial air passenger traffic in Africa, focusing on the development of the 15 busiest country pairs during the period 1989 to 2015. It explores dimensions not previously studied by using ICAO's 'Traffic by Flight Stage' (TFS) and data from the CEPII Gravity Dataset. The spatial results show on an uneven geographical distribution of country pairs with the centre of gravity to South, East and North-East Africa, with one long-distance corridor between Egypt and South Africa. Countries in North and West Africa have rather few linkages, except for Egypt. Central African countries are not represented among the 15 country pairs. Although the number of passengers and the rank among the countries have shifted, South Africa and Egypt stand out, as having most country pair connections. Factors such as changing economic, diplomatic and political relations have had an influence on changing country pair connections throughout the period. A number of variables were selected to investigate how they correlated with Africa's commercial passenger traffic. Of the seven variables selected, five did show on a correlation and two did partly so. In that view, Africa's air traffic follows rather typical patterns.