This study aims to understand how deepfakes affect customer stickiness, which characterizes the degree of customer retention on an online retail platform like Metaverse. Metaverse retail platforms can offer deepfake marketing to give customers an innovative buying experience. The study, built on the attachment theory and socio-technological approach, empirically evaluated affective and cognitive responses from 278 metaverse platform users using structural equation modelling. The results show that the technology factors, namely synchronicity, vicarious expression, and security and privacy, impacted platform attachment. The social factor, deepfakes interaction, impacted emotional attachment to deepfakes. The other social factors, deepfakes' familiarity and reputation, did not affect emotional attachment to Deepfakes' content. This study advances the literature on attachment theory and offers practical recommendations for retailers intending to explore deepfake usage on metaverse platforms. Our study proposes strategies for enhancing customers' attachment to retail brands through deepfakes and emphasizes the critical factors influencing customer retention in the context of metaverse retail.
{"title":"Exploring the affective and cognitive dimensions of customer stickiness in deepfake platforms through the theoretical lens of attachment","authors":"Kanchan Patil , Dhanya Pramod , Vijayakumar Bharathi S , Dhoha AlSaleh","doi":"10.1016/j.jjimei.2025.100344","DOIUrl":"10.1016/j.jjimei.2025.100344","url":null,"abstract":"<div><div>This study aims to understand how deepfakes affect customer stickiness, which characterizes the degree of customer retention on an online retail platform like Metaverse. Metaverse retail platforms can offer deepfake marketing to give customers an innovative buying experience. The study, built on the attachment theory and socio-technological approach, empirically evaluated affective and cognitive responses from 278 metaverse platform users using structural equation modelling. The results show that the technology factors, namely <em>synchronicity, vicarious expression, and security and privacy</em>, impacted platform attachment. The social factor, <em>deepfakes interaction</em>, impacted emotional attachment to deepfakes. The other social factors, deepfakes' familiarity and reputation, did not affect emotional attachment to Deepfakes' content. This study advances the literature on attachment theory and offers practical recommendations for retailers intending to explore deepfake usage on metaverse platforms. Our study proposes strategies for enhancing customers' attachment to retail brands through deepfakes and emphasizes the critical factors influencing customer retention in the context of metaverse retail.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100344"},"PeriodicalIF":0.0,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-02DOI: 10.1016/j.jjimei.2025.100343
Mahmoud Abdulhadi Alabdali , Muhammad Zafar Yaqub , Josef Windsperger
Supply chains face resilience and sustainability challenges from disruptions and digital advancements, with limited insight into leadership-driven digital solutions. This study investigates how green digital transformational leadership (G-DTL) influences green digital supply chain transformation (G-DSCT) and resilience (G-DSCR). We examine the serial mediation roles of algorithmic management (ALGM) and green digital absorptive capacity (G-DAC) in these relationships. Utilizing transformational leadership and absorptive capacity theories within the stimulus–organism–response framework, we collected and analyzed survey data from 324 supply chain professionals in Saudi Arabia using partial least squares structural equation modeling. The results confirm that G-DTL significantly and positively impacts G-DSCT and G-DSCR, with ALGM and G-DAC sequentially mediating the G-DTL–G-DSCR relationship. Practically, leaders should proactively invest in developing green digital leadership competencies and adopt ALGM tools to enhance their absorptive capacity. This strategic combination enables organizations to tackle disruptions, ensuring sustainable transformation and supply chain resilience.
{"title":"Green digital leadership and algorithmic management for sustainable supply chains: A serial mediation model","authors":"Mahmoud Abdulhadi Alabdali , Muhammad Zafar Yaqub , Josef Windsperger","doi":"10.1016/j.jjimei.2025.100343","DOIUrl":"10.1016/j.jjimei.2025.100343","url":null,"abstract":"<div><div>Supply chains face resilience and sustainability challenges from disruptions and digital advancements, with limited insight into leadership-driven digital solutions. This study investigates how green digital transformational leadership (G-DTL) influences green digital supply chain transformation (G-DSCT) and resilience (G-DSCR). We examine the serial mediation roles of algorithmic management (ALGM) and green digital absorptive capacity (G-DAC) in these relationships. Utilizing transformational leadership and absorptive capacity theories within the stimulus–organism–response framework, we collected and analyzed survey data from 324 supply chain professionals in Saudi Arabia using partial least squares structural equation modeling. The results confirm that G-DTL significantly and positively impacts G-DSCT and G-DSCR, with ALGM and G-DAC sequentially mediating the G-DTL–G-DSCR relationship. Practically, leaders should proactively invest in developing green digital leadership competencies and adopt ALGM tools to enhance their absorptive capacity. This strategic combination enables organizations to tackle disruptions, ensuring sustainable transformation and supply chain resilience.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100343"},"PeriodicalIF":0.0,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-05-01DOI: 10.1016/j.jjimei.2025.100342
Shinta Oktaviana R , Putu Wuri Handayani , Achmad Nizar Hidayanto , Bambang Budi Siswanto
Hospitals are the most prominent health data producers of clinical data and administrative data. Reusing health data from hospitals has problems regarding data quality and integrity within the institution. These problems raise healthcare expenses, cause errors in patient treatment, and consume a lot of hospital IT personnel's work time for data extraction and synchronization. Implementing health data governance helps hospitals improve patient safety, increase research in the health sector, establish policy, protect data and information assets, and determine accountabilities and processes for managing data and information. However, only a few countries can implement health data governance in hospitals. In developing countries, research related to health data governance is rare. We conducted this research to determine the requirement for health data governance for hospitals in developing countries. This study is qualitative research with a single case study. The data was taken from the National Cardiovascular Center in Jakarta from June 2022 until December 2023. We used semi-structured interviews with IT and hospital management. There are 18 people as our interviewees. We used thematic analysis. Data governance domain areas are used in our initial code, and we continue to open coding to find the meaning of the interviewee's statement. The axial code was performed to get critical problem-related health data in the hospital. We got six themes as our problem identification. The themes were incomplete data, incorrect data, data redundancy, lack of documentation, data discoverability issues, and no ethical consideration in data access. After that, we mapped the problem to the domain area of data governance. This research found that the domain areas most required for health data governance were data quality management, metadata management, and data security management.
{"title":"Healthcare data governance assessment based on hospital management perspectives","authors":"Shinta Oktaviana R , Putu Wuri Handayani , Achmad Nizar Hidayanto , Bambang Budi Siswanto","doi":"10.1016/j.jjimei.2025.100342","DOIUrl":"10.1016/j.jjimei.2025.100342","url":null,"abstract":"<div><div>Hospitals are the most prominent health data producers of clinical data and administrative data. Reusing health data from hospitals has problems regarding data quality and integrity within the institution. These problems raise healthcare expenses, cause errors in patient treatment, and consume a lot of hospital IT personnel's work time for data extraction and synchronization. Implementing health data governance helps hospitals improve patient safety, increase research in the health sector, establish policy, protect data and information assets, and determine accountabilities and processes for managing data and information. However, only a few countries can implement health data governance in hospitals. In developing countries, research related to health data governance is rare. We conducted this research to determine the requirement for health data governance for hospitals in developing countries. This study is qualitative research with a single case study. The data was taken from the National Cardiovascular Center in Jakarta from June 2022 until December 2023. We used semi-structured interviews with IT and hospital management. There are 18 people as our interviewees. We used thematic analysis. Data governance domain areas are used in our initial code, and we continue to open coding to find the meaning of the interviewee's statement. The axial code was performed to get critical problem-related health data in the hospital. We got six themes as our problem identification. The themes were incomplete data, incorrect data, data redundancy, lack of documentation, data discoverability issues, and no ethical consideration in data access. After that, we mapped the problem to the domain area of data governance. This research found that the domain areas most required for health data governance were data quality management, metadata management, and data security management.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100342"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143894991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the relationship between AI literacy, diagnostic capabilities, prognostic capabilities, and risk management skills among Generation Z accounting students in Thailand. Using a quantitative research design, data were collected from 400 participants and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that AI literacy positively influences both diagnostic and prognostic capabilities. In turn, these capabilities significantly contribute to the development of risk management skills, with prognostic capabilities showing a stronger effect. The findings highlight the value of integrating AI literacy into accounting education to better prepare students with essential analytical and risk-related competencies for the evolving digital landscape.
{"title":"The influence of AI literacy on risk management skills and the roles of diagnostic capabilities and prognostic capabilities: Empirical insight from Thai gen Z accounting students","authors":"Kanokwan Meesook , Narinthon Imjai , Berto Usman , Busaya Vongchavalitkul , Somnuk Aujirapongpan","doi":"10.1016/j.jjimei.2025.100341","DOIUrl":"10.1016/j.jjimei.2025.100341","url":null,"abstract":"<div><div>This study investigates the relationship between AI literacy, diagnostic capabilities, prognostic capabilities, and risk management skills among Generation Z accounting students in Thailand. Using a quantitative research design, data were collected from 400 participants and analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that AI literacy positively influences both diagnostic and prognostic capabilities. In turn, these capabilities significantly contribute to the development of risk management skills, with prognostic capabilities showing a stronger effect. The findings highlight the value of integrating AI literacy into accounting education to better prepare students with essential analytical and risk-related competencies for the evolving digital landscape.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims to expand upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions and the populations that traverse them to establish a more efficient prediction model. The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution significantly outperforms its competitors in an experimental evaluation of 19 forecasting models across several datasets. Finally, an additional ablation study determined that each component of the proposed solution enhances its overall performance.
{"title":"WEST GCN-LSTM: Weighted stacked spatio-temporal graph neural networks for regional traffic forecasting","authors":"Theodoros Theodoropoulos , Angelos-Christos Maroudis , Uwe Zdun , Antonios Makris , Konstantinos Tserpes","doi":"10.1016/j.jjimei.2025.100338","DOIUrl":"10.1016/j.jjimei.2025.100338","url":null,"abstract":"<div><div>Regional traffic forecasting is a critical challenge in urban mobility, with applications to various fields such as the Internet of Everything. In recent years, spatio-temporal graph neural networks have achieved state-of-the-art results in the context of numerous traffic forecasting challenges. This work aims to expand upon the conventional spatio-temporal graph neural network architectures in a manner that may facilitate the inclusion of information regarding the examined regions and the populations that traverse them to establish a more efficient prediction model. The end-product of this scientific endeavor is a novel spatio-temporal graph neural network architecture for regional traffic forecasting referred to as WEST (WEighted STacked) GCN-LSTM. Furthermore, the aforementioned information is included via two novel dedicated algorithms, the Shared Borders Policy and the Adjustable Hops Policy. Through information fusion and distillation, the proposed solution significantly outperforms its competitors in an experimental evaluation of 19 forecasting models across several datasets. Finally, an additional ablation study determined that each component of the proposed solution enhances its overall performance.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100338"},"PeriodicalIF":0.0,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143878934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-21DOI: 10.1016/j.jjimei.2025.100339
Retno Dewanti , Ridho Bramulya Ikhsan
The growing implementation of chatbots in the service industry reflects continued innovation in this area. However, concerns persist regarding chatbot competence in addressing consumer inquiries, potentially hindering both the interaction experience and consumer performance. This study investigates the factors driving consumer performance in chatbot interactions. Data were collected via an online survey of 336 Indonesian consumers. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the structural model. The findings reveal that e-lifestyle significantly influences both chatbot experience and competence. Anthropomorphism significantly impacts chatbot experience, competence, and the resulting consumer performance. Moreover, both chatbot experience and competence significantly affect consumer performance. Furthermore, chatbot competence significantly shapes the user experience. This study contributes to both theoretical and practical understanding of chatbots within the service industry.
{"title":"Do chatbots support consumer performance? Investigating the role of E-lifestyle and anthropomorphism in the service industry","authors":"Retno Dewanti , Ridho Bramulya Ikhsan","doi":"10.1016/j.jjimei.2025.100339","DOIUrl":"10.1016/j.jjimei.2025.100339","url":null,"abstract":"<div><div>The growing implementation of chatbots in the service industry reflects continued innovation in this area. However, concerns persist regarding chatbot competence in addressing consumer inquiries, potentially hindering both the interaction experience and consumer performance. This study investigates the factors driving consumer performance in chatbot interactions. Data were collected via an online survey of 336 Indonesian consumers. Partial least squares-structural equation modeling (PLS-SEM) was employed to test the structural model. The findings reveal that e-lifestyle significantly influences both chatbot experience and competence. Anthropomorphism significantly impacts chatbot experience, competence, and the resulting consumer performance. Moreover, both chatbot experience and competence significantly affect consumer performance. Furthermore, chatbot competence significantly shapes the user experience. This study contributes to both theoretical and practical understanding of chatbots within the service industry.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100339"},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-08DOI: 10.1016/j.jjimei.2025.100337
Yudhvir Seetharam, Kingstone Nyakurukwa
This study investigates the connectedness of various firm-level investor sentiment proxies—news, social media, ESG positive (ESGpos), and ESG negative (ESGneg) sentiment using aggregate connectedness measures and a sample of DJIA stocks between 2015 and 2024. Our findings reveal that each sentiment proxy maintains strong internal consistency, predominantly shaped by its own sources. Specifically, news and social media exhibit high self-connection scores, indicating that these proxies are primarily influenced by their respective content. ESG sentiment proxies show minimal cross-influence from news and social media, indicating their distinct and independent nature. Network analysis further highlights that news and social media transmit sentiment shocks, while ESG-based proxies are predominantly receivers. The most significant flow of sentiment shocks is from social media to ESG negative sentiment. This reflects the central role of social media in shaping sentiment within the system, in contrast to the more isolated influence of news. During significant global event periods, ESGpos and ESGneg shift roles, with ESGpos becoming a transmitter and ESGneg a receiver of sentiment shocks. Sector-specific analysis shows that the Financials (Technology) sector is a net transmitter (receiver) of sentiment shocks. The practical implications of the findings are discussed. The paper contributes to the literature, which has treated different sentiment proxies as distinct phenomena despite their interconnectedness. Additionally, we find that the aggregate connectedness measures used in this study exhibit stronger connectedness compared to the traditional Diebold-Yilmaz framework.
{"title":"Financial textual sentiment connectedness: Evidence from alternative data","authors":"Yudhvir Seetharam, Kingstone Nyakurukwa","doi":"10.1016/j.jjimei.2025.100337","DOIUrl":"10.1016/j.jjimei.2025.100337","url":null,"abstract":"<div><div>This study investigates the connectedness of various firm-level investor sentiment proxies—news, social media, ESG positive (ESGpos), and ESG negative (ESGneg) sentiment using aggregate connectedness measures and a sample of DJIA stocks between 2015 and 2024. Our findings reveal that each sentiment proxy maintains strong internal consistency, predominantly shaped by its own sources. Specifically, news and social media exhibit high self-connection scores, indicating that these proxies are primarily influenced by their respective content. ESG sentiment proxies show minimal cross-influence from news and social media, indicating their distinct and independent nature. Network analysis further highlights that news and social media transmit sentiment shocks, while ESG-based proxies are predominantly receivers. The most significant flow of sentiment shocks is from social media to ESG negative sentiment. This reflects the central role of social media in shaping sentiment within the system, in contrast to the more isolated influence of news. During significant global event periods, ESGpos and ESGneg shift roles, with ESGpos becoming a transmitter and ESGneg a receiver of sentiment shocks. Sector-specific analysis shows that the Financials (Technology) sector is a net transmitter (receiver) of sentiment shocks. The practical implications of the findings are discussed. The paper contributes to the literature, which has treated different sentiment proxies as distinct phenomena despite their interconnectedness. Additionally, we find that the aggregate connectedness measures used in this study exhibit stronger connectedness compared to the traditional Diebold-Yilmaz framework.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100337"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143791105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-05DOI: 10.1016/j.jjimei.2025.100330
Yusuf Aliyu , Aliza Sarlan , Kamaluddeen Usman Danyaro , Abdullahi Sani abd Rahman , Aminu Aminu Muazu , Mustapha Yusuf Abubakar
Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities.
{"title":"Deep learning techniques for sentiment analysis in code-switched Hausa-English tweets","authors":"Yusuf Aliyu , Aliza Sarlan , Kamaluddeen Usman Danyaro , Abdullahi Sani abd Rahman , Aminu Aminu Muazu , Mustapha Yusuf Abubakar","doi":"10.1016/j.jjimei.2025.100330","DOIUrl":"10.1016/j.jjimei.2025.100330","url":null,"abstract":"<div><div>Social media serve as a crucial platform for expressing opinions and perspectives. Its texts often characterised by code-switching or mixed languages in multilingual setting. This results in a diverse and complex linguistic context, which can negatively affect the accuracy of sentiment analysis for low-resource languages such as Hausa. Prior research has predominantly concentrated on sentiment analysis within single-language data rather than code-switched data. This paper proposes an efficient hyperparameter tuning framework and a novel stemming algorithm for the Hausa language. The framework leverages word embeddings to determine the polarity scores of code-mixed tweets and enhances the accuracy of sentiment analysis models in low-resource language. The extensive experiments demonstrate the framework's efficiency and reveal a superior performance of transformer models over conventional deep learning models. The framework achieves a balance between accuracy and computational efficiency, making it suitable for deployment in practical applications. Compared to state-of-the-art transformer models, our framework significantly reduces computational costs while maintaining competitive performance. Notably, the AfriBERTa model achieves outstanding results, with an F1-score of 0.92 and an accuracy of 0.919, surpassing current baseline standards. These findings have broad implications for social media monitoring, customer feedback analysis, and public sentiment tracking, enabling more inclusive and accessible NLP tools for underrepresented linguistic communities.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100330"},"PeriodicalIF":0.0,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-04DOI: 10.1016/j.jjimei.2025.100336
Fadi Herzallah , Amer J. Abosamaha , Yousef Abu-Siam , Mohammed Amer , Uzair Sajjad , Khalid Hamid
This study investigates Generation Z's behavioral intentions toward mobile wallet (m-wallet) usage in Jordan by extending UTAUT2 with personal innovativeness in a dual role—as both a direct predictor and moderator—alongside gender as an additional moderator. Data were collected from 389 Gen Z users across Jordan using an online survey and analyzed using partial least squares structural equation modelling (PLS-SEM). Results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, and personal innovativeness significantly influence behavioral intentions, while hedonic motivation shows no significant effect. Personal innovativeness demonstrated significant moderating effects on the relationship between the three determinants (performance expectancy, facilitating conditions, and hedonic motivation) and behavioral intention. Notably, gender showed no significant moderating effects, suggesting diminishing gender disparities in m-wallet use among Gen Z users. The extended model explains 75.1 % of behavioral intentions variance. This study advances understanding of m-wallet usage by: (1) focusing explicitly on Gen Z users, a demographic not previously studied in Jordan's m-wallet context; (2) examining usage patterns across all regions of Jordan and multiple m-wallet platforms, extending beyond previous studies limited to specific cities or platforms; and (3) revealing the dual role of personal innovativeness in shaping behavioral intentions. These findings provide valuable insights for m-wallet providers and policymakers in developing strategies to enhance usage among young consumers in developing countries.
{"title":"Determinants of mobile wallet usage among Gen Z: Extending the UTAUT2 model with moderating effects of personal innovativeness and gender","authors":"Fadi Herzallah , Amer J. Abosamaha , Yousef Abu-Siam , Mohammed Amer , Uzair Sajjad , Khalid Hamid","doi":"10.1016/j.jjimei.2025.100336","DOIUrl":"10.1016/j.jjimei.2025.100336","url":null,"abstract":"<div><div>This study investigates Generation Z's behavioral intentions toward mobile wallet (m-wallet) usage in Jordan by extending UTAUT2 with personal innovativeness in a dual role—as both a direct predictor and moderator—alongside gender as an additional moderator. Data were collected from 389 Gen Z users across Jordan using an online survey and analyzed using partial least squares structural equation modelling (PLS-SEM). Results indicate that performance expectancy, effort expectancy, social influence, facilitating conditions, habit, and personal innovativeness significantly influence behavioral intentions, while hedonic motivation shows no significant effect. Personal innovativeness demonstrated significant moderating effects on the relationship between the three determinants (performance expectancy, facilitating conditions, and hedonic motivation) and behavioral intention. Notably, gender showed no significant moderating effects, suggesting diminishing gender disparities in m-wallet use among Gen Z users. The extended model explains 75.1 % of behavioral intentions variance. This study advances understanding of m-wallet usage by: (1) focusing explicitly on Gen Z users, a demographic not previously studied in Jordan's m-wallet context; (2) examining usage patterns across all regions of Jordan and multiple m-wallet platforms, extending beyond previous studies limited to specific cities or platforms; and (3) revealing the dual role of personal innovativeness in shaping behavioral intentions. These findings provide valuable insights for m-wallet providers and policymakers in developing strategies to enhance usage among young consumers in developing countries.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100336"},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143767679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.
{"title":"Transforming business management practices through metaverse technologies: A Machine Learning approach","authors":"Raghu Raman , Santanu Mandal , Angappa Gunasekaran , Thanos Papadopoulos , Prema Nedungadi","doi":"10.1016/j.jjimei.2025.100335","DOIUrl":"10.1016/j.jjimei.2025.100335","url":null,"abstract":"<div><div>This study critically reviews the literature on metaverse technologies, developing an integrative framework to explore their sector-specific implications and transformative impact on business management. Employing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework and machine learning-based BERTopic modeling, the study identifies nine key themes, reflecting the diverse ways augmented reality (AR), virtual reality (VR), extended reality (XR), digital twins, and decentralized finance (DeFi) influence industries. These themes include the metaverse as a tool for economic and environmental policy experiments, navigating financial risk and regulatory dynamics, adapting human resource development to VR-driven environments, Industry 4.0 applications of VR and digital twins, digital twin applications in manufacturing and supply chain optimization, AR and VR in digital marketing and customer experience, AR in enhancing retail and consumer experiences, exploring user interaction and affordances in the metaverse, and VR and AR in tourism experience and engagement. The framework highlights drivers, constraints, and cross-sector linkages, addressing practical challenges such as high implementation costs, regulatory uncertainties, interoperability barriers, cybersecurity risks, and ethical concerns surrounding data privacy and inclusion. The study critically evaluates contradictions in metaverse adoption, such as the tension between sustainability goals and energy-intensive technologies like blockchain, the gap between immersive training potential and workforce adaptation challenges, and the disparity between metaverse-driven economic models and real-world policy implementation hurdles. Research propositions suggest integrating metaverse technologies into business operations while balancing ethical dimensions, psychological impacts, cost limitations, and accessibility barriers. Additionally, the study advocates for expanding theoretical frameworks such as the Resource-Based View (RBV), Technology Acceptance Model (TAM), and experiential learning to account for the dynamic capabilities, risks, and industry-specific constraints of metaverse adoption. Policymakers and practitioners are encouraged to address regulatory and ethical challenges, sectoral disparities, and the unintended consequences of metaverse-driven digital transformation, ensuring operational efficiency, resilience, and consumer engagement while fostering sustainable and inclusive adoption. This research offers actionable insights for strategic implementation, interdisciplinary theoretical expansion, and ethical progress in business management.</div></div>","PeriodicalId":100699,"journal":{"name":"International Journal of Information Management Data Insights","volume":"5 1","pages":"Article 100335"},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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