Aim/Purpose: Given the increasingly important role of knowledge and human resources for firms in developing and emerging countries to pursue innovation, this paper aims to study and explore the potential intermediating roles of knowledge donation and collection in linking high-involvement human resource management (HRM) practice and innovation capability. The paper also explores possible moderators of market turbulence in fostering the influences of knowledge-sharing (KS) behaviors on innovation competence in terms of incremental and radical innovation. Background: The fitness of HRM practice is critical for organizations to foster knowledge capital and internal resources for improving innovation and sustaining competitive advantage. Methodology: The study sample is 309 respondents and Structural Equation Model (SEM) was used for the analysis of the data obtained through a questionnaire survey with the aid of AMOS version 22. Contribution: This paper increases the understanding of the precursor role of high-involvement HRM practices, intermediating mechanism of KS activities, and the regulating influence of market turbulence in predicting and fostering innovation capability, thereby pushing forward the theory of HRM and innovation management. Findings: The empirical findings support the proposed hypotheses relating to the intermediating role of KS in the HRM practices-innovation relationship. It spotlights the crucial character of market turbulence in driving the domination of knowledge-sharing behaviors on incremental innovation. Recommendations for Practitioners: The proposed research model can be applied by leaders and directors to foster their organizational innovation competence. Recommendation for Researchers: Researchers are recommended to explore the influence of different models of HRM practices on innovation to identify the most effective pathway leading to innovation for firms in developing and emerging nations. Impact on Society: This paper provides valuable initiatives for firms in developing and emerging markets on how to leverage the strategic and internal resources of an organization for enhancing innovation. Future Research: Future studies should investigate the influence of HRM practices and knowledge resources to promote frugal innovation models for dealing with resource scarcity.

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

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

The proposed work aims to discover the international technology competition and development of human-artificial intelligence (AI) collaboration on content creation in the film and animation industries to support the strategic planning, decision-making of R&D, and soft innovation. The study demonstrates a hybrid approach that combines technology life cycle (TLC) and latent Dirichlet allocation (LDA) topic modelling. We analyse 1,982 patents of AI collaborating on creating film and animation in the primary patent application countries (i.e., patents applied to the intellectual property offices of the USA, China, Korea, Japan, and European Patent Office, EPO) from 2010 to 2020. The TLC results show growing trends in the international technology competition. The major topic trends corresponding to TLC phases denote strong potential or future stagnation signals in different countries. The study provides the future R&D signals and suggests stimulating soft innovation with human-AI collaboration to face growing competition.

With the continuous growth and importance of data, the need for strong data protection becomes crucial. Encryption plays a vital role in preserving the confidentiality of data, and attribute-based encryption (ABE) offers a meticulous access control system based on attributes. This study investigates the integration of Fernet encryption with initialisation vector (IV) and ABE, resulting in a hybrid encryption approach that enhances both security and flexibility. By combining the advantages of Fernet encryption and IV-based encryption, the hybrid encryption scheme establishes an effective and robust mechanism for safeguarding data. Fernet encryption, renowned for its simplicity and efficiency, provides authenticated encryption, guaranteeing both the confidentiality and integrity of the data. The incorporation of an initialisation vector (IV) introduces an element of randomness into the encryption process, thereby strengthening the overall security measures. This research paper discusses the advantages and drawbacks of the hybrid encryption of Fernet and IV with ABE.

Campus discovery is an important feature of a university student induction process. Approaches towards campus discovery differ from course to course and can comprise guided tours that are often lengthy and uninspiring, or self-guided tours that run the risk of students failing to complete them. This paper describes a campus self-discovery induction game (Geospatial Crypto Reconnaissance) which aims to make students aware of campus resources and facilities, whilst at the same time allowing students to make friends and complete the game in an enthusing and exciting way. In this paper we describe the game construct, which comprises of a location, message, and artefact, and also the gameplay. Geospatial Crypto Reconnaissance requires students to identify a series of photographs from around the campus, to capture the GPS coordinates of the location of the photograph, to decipher a ciphered message and then to return both the GPS coordinates and the message for each photograph, proving that the student has attended the location. The game had a very high satisfaction score and we present an analysis of student feedback on the game and also provide guidance on how the game can be adopted for less technical cohorts of students.

Creative computing is one of the rapidly growing educational trends around the world. Previous studies have shown that creative computing can empower disadvantaged children and youth. At-risk youth tend to hold a negative view of self and perceive their abilities as inferior compared to “normative” pupils. The Implicit Theories of Intelligence approach (ITI; Dweck, 1999, 2008) suggests a way of changing beliefs regarding one’s abilities. This paper reports findings from an experiment that explores the impact of a short intervention among at-risk youth and “normative” high-school students on (1) changing ITI from being perceived as fixed (entity view of intelligence) to more flexible (incremental view of intelligence) and (2) the quality of digital self-representations programmed though a creative computing app. The participants were 117 Israeli youth aged 14-17, half of whom were at-risk youth. The participants were randomly assigned to the experimental and control conditions. The experimental group watched a video of a lecture regarding brain plasticity that emphasized flexibility and the potential of human intelligence to be cultivated. The control group watched a neutral lecture about brain-functioning and creativity. Following the intervention, all of the participants watched screencasts of basic training for the Scratch programming app, designed artifacts that digitally represented themselves five years later and reported their ITI. The results showed more incremental ITI in the experimental group compared to the control group and among normative students compared to at-risk youth. In contrast to the research hypothesis, the Scratch projects of the at-risk youth, especially in the experimental condition, were rated by neutral judges as being more creative, more aesthetically designed, and more clearly conveying their message. The results suggest that creative computing combined with the ITI intervention is a way of developing creativity, especially among at-risk youth. Increasing the number of youths who hold incremental views of intelligence and developing computational thinking may contribute to their empowerment and well-being, improve learning and promote creativity.

Aim/Purpose: Students face many challenges improving their soft skills such as critical thinking. This paper offers one possible solution to this problem. Background: This paper considers one method of enhancing critical thinking through a problem-solving game called the Coffee Shop. Problem-solving is a key component to critical thinking, and game-playing is one method of enhancing this through an interactive teaching method. Methodology: Three classes of Vietnamese high school students engaged in the Coffee Shop game. The method seeks outcome measurements through the use of analysis of multiple surveys to assess and interpret if critical thinking may have been improved. Contribution: The study may help to understand the importance of problem-solving in the context of an entrepreneurial setting and add to the variation of methods used to deliver the lesson to students in the classroom. Findings: The findings show that practicing problem-solving scenarios with a focus on critical thinking in a time limited setting results in a measured improvement of this skill. Recommendations for Practitioners: The findings suggest that educators could use games more as tools for problem-solving to contribute to their students’ learning outcomes around developing critical thinking. Recommendation for Researchers: More research could be devoted to developing problem-solving and critical thinking skills through game-play models. Impact on Society: Improved critical thinking skills in individuals could make a greater contribution to society. Future Research A comparative study between different high school grades and genders as well as between different countries or cultures.

Aim/Purpose: This study examines differences in credit and noncredit users’ learning and usage of the Plant Sciences E-Library (PASSEL, http://passel.unl.edu), a large international, open-source multidisciplinary learning object repository. Background: Advances in online education are helping educators to meet the needs of formal academic credit students, as well as informal noncredit learners. Since online learning attracts learners with a wide variety of backgrounds and intentions, it is important understand learner behavior so that instructional resources can be designed to meet the diversity of learner motivations and needs. Methodology: This research uses both descriptive statistics and cluster analysis. The descriptive statistics address the research question of how credit learners differ from noncredit learners in using an international e-library of learning objects. Cluster analysis identifies high and low credit/noncredit students based on their quiz scores and follow-up descriptive statistics to (a) differentiate their usage patterns and (b) help describe possible learning approaches (deep, surface, and strategic). Contribution: This research is unique in its use of objective, web-tracking data and its novel use of clustering and descriptive analytic approaches to compare credit and noncredit learners’ online behavior of the same educational materials. It is also one of the first to begin to identify learning approaches of the noncredit learner. Findings: Results showed that credit users scored higher on quizzes and spent more time on the online quizzes and lessons than did noncredit learners, suggesting their academic orientation. Similarly, high credit scorers spent more time on individual lessons and quizzes than did the low scorers. The most striking difference among noncredit learners was in session times, with the low scorers spending more time in a session, suggesting more browsing behavior. Results were used to develop learner profiles for the four groups (high/low quiz scorers x credit/noncredit). Recommendations for Practitioners: These results provide preliminary insight for instructors or instructional designers. For example, low scoring credit students are spending a reasonable amount of time on a lesson but still score low on the quiz. Results suggest that they may need more online scaffolding or auto-generated guidance, such as the availability of relevant animations or the need to review certain parts of a lesson based on questions missed. Recommendation for Researchers: The study showed the value of objective, web-tracking data and novel use of clustering and descriptive analytic approaches to compare different types of learners. One conclusion of the study was that this web-tracking data be combined with student self-report data to provide more validation of results. Another conclusion was that demographic data from noncredit learners could be instrumental in further refining learning approaches for noncredit learners. Impact on Society: Learning object repositories, online courses, blended courses, and MOOCs often provide learners the option of moving freely among educational content, choosing not only topics of interest but also formats of material they feel will advance their learning. Since online learning is becoming more prolific and attracts learners with a wide variety of backgrounds and intentions, these results show the importance of understanding learner behavior so that e-learning instructional resources can be designed to meet the diversity of learner motivations and needs. Future Research: Future research should combine web-tracking data with student self-report to provide more validation of results. In addition, collection of demographic data and disaggregation of noncredit student usage motivations would help further refining learning approaches for this growing population of online users.

Aim/Purpose: To examine how positive/negative message framing – based on peripheral cues (regarding popularity, source, visuals, and hyperlink) – affects perceptions of credibility of scientific information posted on social networking sites (in this case, Facebook), while exploring the mechanisms of viewing the different components. Background: Credibility assessment of information is a key skill in today's information society. However, it is a demanding cognitive task, which is impossible to perform for every piece of online information. Additionally, message framing — that is, the context and approach used to construct information— may impact perceptions of credibility. In practice, people rely on various cues and cognitive heuristics to determine whether they think a piece of content is true or not. In social networking sites, content is usually enriched by additional information (e.g., popularity), which may impact the users' perceived credibility of the content. Methodology: A quantitative controlled experiment was designed (N=19 undergraduate students), collecting fine grained data with an eye tracking camera, while analyzing it using transition graphs. Contribution: The findings on the mechanisms of that process, enabled by the use of eye tracking data, point to the different roles of specific peripheral cues, when the message is overall peripherally positive or negative. It also contributes to the theoretical literature on framing effects in science communication, as it highlights the peripheral cues that make a strong frame. Findings: The positively framed status was perceived, as expected from the Elaboration Likelihood Model, more credible than the negatively framed status, demonstrating the effects of the visual framing. Differences in participants' mechanisms of assessing credibility between the two scenarios were evident in the specific ways the participants examined the various status components. Recommendations for Practitioners: As part of digital literacy education, major focus should be given to the role of peripheral cues on credibility assessment in social networking sites. Educators should emphasize the mechanisms by which these cues interact with message framing, so Internet users would be encouraged to reflect upon their own credibility assessment skills, and eventually improve them. Recommendation for Researchers: The use of eye tracking data may help in collecting and analyzing fine grained data on credibility assessment processes, and on Internet behavior at large. The data shown here may shed new light on previously studied phenomena, enabling a more nuanced understanding of them. Impact on Society: In an era when Internet users are flooded with information that can be created by virtually anyone, credibility assessment skills have become ever more important, hence the prominence of this skill. Improving citizens' assessment of information credibility — to which we believe this study contributes — results on a greater impact on society. Future Research: The role of peripheral cues and of message framing should be studied in other contexts (not just scientific news) and in other platforms. Additional peripheral cues not tested here should be also taken into consideration (e.g., connections between the information consumer and the information sharer, or the type of the leading image).

Aim/Purpose: The purpose of this study is to discover usage differences in task performance by students of different cultures, by examining procrastination patterns from a national cultural perspective and exploring the effect of multicultural virtual teamwork on students’ individual procrastination. Background: This study aims to examine higher-education entrepreneurial learning in the context of multicultural virtual teamwork, as performed during participation on a Global Entrepreneurship course. Methodology: The methodology consists of quantitative comparative data analytics preceding and subsequent to intercultural team activities. This research is based on analyses of objective data collected by Moodle, the LMS used in the In2It project, in its built-in log system from the Global Entrepreneurship course website, which offers students diverse entities of information and tasks. In the examined course, there were 177 participants, from three different countries: United Kingdom, France and Israel. The students were grouped into 40 multicultural virtual (not face-to-face) teams, each one comprised of participants from at least two countries. The primary methodology of this study is analytics of the extracted data, which was transferred into Excel for cleaning purposes and then to SPSS for analysis. Contribution: This study aims to discover the effects of multicultural teamwork on individual procrastination while comparing the differences between cultures, as there are only a few studies exploring this relation. The uniqueness of this study is using and analyzing actual data of student procrastination from logs, whereas other studies of procrastination in multicultural student teams have measured perceived procrastination, collected using surveys. Findings: The results show statistical differences between countries in procrastination of individual assignments before team working: students from UK were the most procrastinators and Israeli students were the least procrastinators, but almost all students procrastinated. However, the outcome of the teamwork was submitted almost without procrastination. Moreover, procrastination in individual assignments performed after finishing the multicultural teamwork dramatically decreased to 10% of the students’ prior individual procrastination. Recommendations for Practitioners: The results from this study, namely, the decline of the procrastination after the multicultural virtual teamwork, can be used by global firms with employees all over the world, working in virtual multicultural teams. Such firms do not need to avoid multicultural teams, working virtually, as they can benefit from this kind of collaboration. Recommendation for Researchers: These results can be also beneficial for academic researchers from different cultures and countries, working together in virtual multicultural teams. Impact on Society: Understanding the positive effect of virtual multicultural teamwork, in mitigating the negative tendency of students from diverse cultures to procrastinate, as concluded in this study, can provide a useful tool for higher education or businesses to mitigate procrastination in teamwork processes. It can also be used as an experiential learning tool for improving task performance and teamwork process. Future Research: The relation between procrastination and motivation should be further examined in relation to multicultural virtual teams. Further research is needed to explore the effect of multicultural virtual teamwork during the teamwork process, and the reasoning for this effect.

The paper introduces a novel Personal Knowledge Management (PKM) concept and prototype system. The system’s objective is to aid life-long-learning, resourcefulness, creativity, and teamwork of individuals throughout their academic and professional life and as contributors and beneficiaries of organizational and societal performance. Such a scope offers appealing and viable opportunities for stakeholders in the educational, professional, and developmental context. To further validate the underlying PKM application design, the systems thinking techniques of the transdiscipline of Informing Science (IS) are employed. By applying Cohen’s IS-Framework, Leavitt’s Diamond Model, the IS-Meta Approach, and Gill’s and Murphy’s Three Dimensions of Design Task Complexity, the more specific KM models and methodologies central to the PKMS concept are aligned, introduced, and visualized. The extent of this introduction offers an essential overview, which can be deepened and broadened by using the cited URL and DOI links pointing to the available resources of the author’s prior publications. The paper emphasizes the differences of the proposed meme-based PKM System compared to its traditional organizational document-centric counterparts as well as its inherent complementing synergies. As a result, it shows how the system is closing in on Vannevar Bush’s still unfulfilled vison of the ‘Memex’, an as-close-as-it-gets imaginary ancestor celebrating its 70th anniversary as an inspiring idea never realized. It also addresses the scenario recently put forward by Levy which foresees a decentralizing revolution of knowledge management that gives more power and autonomy to individuals and self-organized groups. Accordingly, it also touches on the PKM potential in terms of Kuhn’s Scientific Revolutions and Disruptive Innovations.

The main aim of this paper is to provide a theoretically and empirically grounded discussion on IT-based organizational creativity support. This study attempts to answer the following questions: (1) what is the issue of organizational creativity and its IT-based support, (2) what is the demand for IT –based organizational creativity support; (3) what are the main determinants and barriers to IT-based organizational creativity support; and (4) what success factors are crucial for IT-based organizational creativity support. This paper presents the analysis results of a survey conducted in 25 selected organizations. The paper provides valuable information on the possibilities of IT applications in organizational creativity support as well as the associated success factors. It makes useful contribution to our better understanding of IT-based organizational creativity support issues.

Aim/Purpose: Professional companies selling persuasive-communication services via the World Wide Web need to be exemplars of effective informing practices. Their credibility is at risk if their websites do not excel in marketing message and use of medium. Their unique brands need to be expressed through website technology and content, or they cannot compete successfully. Background: Compares marketing communication consultants’ websites with expert criteria. Methodology: Content analysis of 40 advertising agency websites. Contribution: Links an evaluation of advertising agency compliance with expert website criteria to established branding constructs. Findings: Most small advertising agencies could improve their brand reputations through better compliance with experts’ recommended website design and content criteria. Recommendations for Practitioners: A hierarchy of recommendations for practitioners is offered, addressing ease and importance. Impact on Society: Clarity and credibility of message and medium improve our ability to practice effective informing. Future Research: Explore online communications of specialized populations such as digital marketing experts.

Aim/Purpose: To understand the process of social change creation in social entrepreneurial ventures (SEVs), specifically emphasizing the role and nature of the communicative process in social change creation. Background: Drawing on data from seven SEVs from India and the US and employing a grounded theory methodology, this research scrutinizes the social change process and uncovers the role and characteristics of dialogue in this process. Methodology: Qualitative data was collected from seven social entrepreneurial organizations over a period of eight months from July 2011 to February 2012. Semi-structured interviews were conducted with a wide range of members within these social entrepreneurial organizations (n=27) with additional informal interviews with field workers and volunteers. Data from the semi-structured interviews and notes from observations were integrated with analyses of archival resources. Contribution: There is little scholarship about the process of social change creation and the necessary conditions to promote social change over time. Understanding the process of social change creation and the individual, interpersonal, and organizational conditions that facilitate the process is central to design of effective trans-sector TD problem solving ventures. This paper focuses on the process of social change creation in social entrepreneurial settings, specifically emphasizing the role and nature of the communicative process in social change creation. Findings: The reflections and experiences of the members of SEVs revealed that social entrepreneurship is a collective endeavor and this collective character is essential to its success. Collective organization and synergy, deep intra-organizational communication, and a conducive organizational context are critical for the creation of collective wisdom and knowledge networks for long-term collaborative community capacity building. Dialogue emerged as a central category linking the other categories to explain the process of social change creation. Organic organizational structure enables knowledge creation and integration through the process of organizational learning through deep and continuous social interaction, or dialogue. Recommendations for Practitioners: This research elucidated the key characteristics of the organizational context required to support the creation of social change. It also identified the critical role and characteristics of the communicative process required to generate structural knowledge and collective wisdom at the organizational level. Recommendation for Researchers: For individual and organizational learning, trans-sector transdisciplinary organizations require an appropriate organizational context. Key elements of such an organizational context include (1) understanding the ecology of the social problem; (2) organic organizational structure; (3) continuous and deep social interaction among all levels of the organization; (4) employee and community autonomy and empowerment; and (5) attention to subtle environmental changes in the system. These elements in combination lead to the creation of collective wisdom. Collective wisdom then feeds back into the conception, planning, and action stages of the iterative cycle of organizational knowledge creation to create positive social change. Impact on Society: Same as above Future Research: Future research model theoretically and study empirically the ecology of social entrepreneurship and trans-sector TD problem solving more broadly. For example, the ways in the personal attributes of social entrepreneurs (e.g., their leadership style, networking abilities) combine with circumstances at organizational, institutional, and international levels to influence the effectiveness of their efforts to promote positive social change within local and global communities. Second, the grounded theoretical framework developed here should be further refined and elaborated through the identification of additional key contextual factors that affect SEVs’ capacity to promote positive social change and to achieve sustainability in different socio-environmental contexts. There is also a need to translate the findings from this research to facilitate the creation of more inclusive problem solving contexts and practices.