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1 – 10 of 42Moza Tahnoon Al Nahyan, Muna Saeed Al Suwaidi, Noora Al Zaabi, Fatima Al Qubaisi and Fauzia Jabeen
Based on the componential theory of organizational creativity and innovation, this study examined the relationship between managerial coaching (MC) and innovative work behavior…
Abstract
Purpose
Based on the componential theory of organizational creativity and innovation, this study examined the relationship between managerial coaching (MC) and innovative work behavior (IWB). It focused on the mediating role of psychological empowerment and the moderating role of task interdependence.
Design/methodology/approach
The self-administered questionnaires were used to collect data from 420 employees of the United Arab Emirates’s public sector organizations. A hierarchical linear model (HLM) with different regression techniques was used.
Findings
The results showed that MC directly influences IWB. The path analysis also revealed that MC has an indirect effect on IWB via psychological empowerment. The moderating role of task independence in MC and IWB was also revealed.
Practical implications
The findings shall provide insights that will help practitioners and academics understand frontline employees' innovative behavior in public sector settings and formulate strategies that will increase the involvement of employees in displaying innovation-based activities at the workplace.
Originality/value
This study adds value to the literature by integrating the componential theory of organizational creativity and innovation in public sector settings.
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Evans Sokro, Theresa Obuobisa-Darko and Bernard Okpattah
This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone…
Abstract
Purpose
This study examines learner satisfaction and success as mechanisms through which online learning quality translates into learners’ continuous intentions of use by extending DeLone and McLean’s information system success model. It also examines the moderating effect of perceived supervisory support and learners’ self-regulation on online learning quality in Higher Education Institutions.
Design/methodology/approach
Survey data were obtained from 540 students in both private and public higher institutions of learning in Ghana. The Partial Least Squares – Structural Equations Modelling (PLS-SEM) technique was used to test the hypothesised relationships.
Findings
The results revealed that system quality emerged as the single most important variable in the DeLone and McLean model, that influences learner success and satisfaction. Further, learner satisfaction has a significant positive effect on learner attitudes, whilst self-regulation was found to moderate the relationship between online learning quality and learner success as well as learner satisfaction.
Originality/value
The study appears to be among the first to explore the inter-relationship among online learning environment quality and learner attitudes and moderating factors perceived supervisory support and self-regulation. The study highlights insightful practical implications for students, faculty and administrators of higher institutions.
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Muruganantham Ganesan and B. Dinesh Kumar
This study aims to investigate the impact of customer perceptions of Augmented Reality (AR) attributes such as augmentation, interactivity and vividness on attitudes towards AR…
Abstract
Purpose
This study aims to investigate the impact of customer perceptions of Augmented Reality (AR) attributes such as augmentation, interactivity and vividness on attitudes towards AR mobile apps, virtual product and behavioural intentions. Also, the mediation role of customer engagement in the effect of perceptions of AR attributes on attitudes and behavioural intentions is examined using the Theory of Interactive Media Effects.
Design/methodology/approach
The study used a cross-sectional design. A total of 456 valid data were collected from the Millennials and Generation Z cohorts using purposive sampling. The conceptual framework was assessed using Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Partial Least Squares-Multi Group Analysis (PLS-MGA).
Findings
The research revealed that customer perceptions of AR features such as augmentation, interactivity and vividness significantly influenced customer engagement, leading to favourable attitudes towards both the AR mobile app and the Virtual product as well as behavioural intentions. Furthermore, the study substantiates the role of customer engagement as a mediator in the relationship between customer perceptions of AR attributes and both attitudinal and behavioural outcomes.
Originality/value
To the best of the authors’ knowledge, this is the first attempt to investigate the significance of perceived augmentation as an antecedent to customer engagement and the mediating role of customer engagement on the influence of perceptions of AR attributes on attitudinal and behavioural intention.
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This study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information…
Abstract
Purpose
This study examines the key determinants of subscription intentions for ChatGPT Plus (paid version) in business settings, focusing on tasks such as system quality, information support, service quality, perceived intelligence, goal-congruent outcome and self-efficacy.
Design/methodology/approach
The study utilized a survey of office workers, analyzed through structural equation modeling, to explore these determinants.
Findings
The results demonstrate that system quality, service quality and perceived intelligence significantly influence satisfaction, while service quality and perceived intelligence also impact goal-congruent outcomes. Contrary to traditional models, satisfaction does not significantly correlate with usage. Instead, a significant relationship is observed between goal-congruent outcomes and usage. Self-efficacy emerges as a crucial predictor of subscription intentions, further underlined by the significant impact of usage on subscription intention.
Research limitations/implications
The study’s focus on office workers and a single artificial intelligence (AI) chatbot type may limit generalizability. Its findings illuminate several avenues for future research, particularly in diversifying the context and demographics studied.
Practical implications
This research offers actionable insights for businesses and practitioners in the implementation of AI chatbots. It highlights the importance of enhancing system quality, personalization and user confidence to boost subscription intentions, thereby guiding strategies for user engagement and technology adoption.
Originality/value
This study pioneers in investigating subscription intentions towards AI chatbots, particularly ChatGPT, providing a novel framework that expands upon traditional user behavior theories.
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Thamaraiselvan Natarajan, P. Pragha and Krantiraditya Dhalmahapatra
Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables…
Abstract
Purpose
Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables real-world activities in the virtual environment, which attracts organizations to adopt the new fascinating technology. This paper thus explores the uses and gratification factors affecting user adoption and recommendation of metaverse from the management perspective.
Design/methodology/approach
The study adopts a mixed approach where structural topic modeling is used to analyze tweets about the metaverse, and the themes uncovered from structural topic modeling were further analyzed through data collection using structural equation modeling.
Findings
The analyses revealed that social interaction, escapism, convenient navigability, and telepresence significantly affect adoption intent and recommendation to use metaverse, while the trendiness showed insignificance. In the metaverse, users can embody avatars or digital representations, users can express themselves, communicate nonverbally, and interact with others in a more natural and intuitive manner.
Originality/value
This paper contributes to research as it is the first of its kind to explore the factors affecting adoption intent and recommendation to use metaverse using Uses and Gratification theory in a mixed approach. Moreover, the authors performed a two-step study involving both qualitative and quantitative techniques, giving a new perspective to the metaverse-related study.
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Ellen A. Donnelly, Madeline Stenger, Daniel J. O'Connell, Adam Gavnik, Jullianne Regalado and Laura Bayona-Roman
This study explores the determinants of police officer support for pre-arrest/booking deflection programs that divert people presenting with substance use and/or mental health…
Abstract
Purpose
This study explores the determinants of police officer support for pre-arrest/booking deflection programs that divert people presenting with substance use and/or mental health disorder symptoms out of the criminal justice system and connect them to supportive services.
Design/methodology/approach
This study analyzes responses from 254 surveys fielded to police officers in Delaware. Questionnaires asked about views on leadership, approaches toward crime, training, occupational experience and officer’s personal characteristics. The study applies a new machine learning method called kernel-based regularized least squares (KRLS) for non-linearities and interactions among independent variables. Estimates from a KRLS model are compared with those from an ordinary least square regression (OLS) model.
Findings
Support for diversion is positively associated with leadership endorsing diversion and thinking of new ways to solve problems. Tough-on-crime attitudes diminish programmatic support. Tenure becomes less predictive of police attitudes in the KRLS model, suggesting interactions with other factors. The KRLS model explains a larger proportion of the variance in officer attitudes than the traditional OLS model.
Originality/value
The study demonstrates the usefulness of the KRLS method for practitioners and scholars seeking to illuminate patterns in police attitudes. It further underscores the importance of agency leadership in legitimizing deflection as a pathway to addressing behavioral health challenges in communities.
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The existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI…
Abstract
Purpose
The existing technology acceptance models have not yet investigated functional and motivational factors impacting trust in and use of conversational artificial intelligence (AI) by integrating the feedback and sequential updating mechanisms. This study challenged the existing models and constructed an integrated longitudinal model. Using a territory-wide two-wave survey of a representative sample, this new model examined the effects of hedonic motivation, social motivation, perceived ease of use, and perceived usefulness on continued trust, intended use, and actual use of conversational AI.
Design/methodology/approach
An autoregressive cross-lagged model was adopted to test the structural associations of the seven repeatedly measured constructs.
Findings
The results revealed that trust in conversational AI positively affected continued actual use, hedonic motivation increased continued intended use, and social motivation and perceived ease of use enhanced continued trust in conversational AI. While the original technology acceptance model was unable to explain the continued acceptance of conversational AI, the findings showed positive feedback effects of actual use on continued intended use. Except for trust, the sequential updating effects of all the measured factors were significant.
Originality/value
This study intended to contribute to the technology acceptance and human–AI interaction paradigms by developing a longitudinal model of continued acceptance of conversational AI. This new model adds to the literature by considering the feedback and sequential updating mechanisms in understanding continued conversational AI acceptance.
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Hui-Min Lai, Shin-Yuan Hung and David C. Yen
Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge…
Abstract
Purpose
Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.
Design/methodology/approach
A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).
Findings
The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.
Originality/value
We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.
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Asif Hasan, Amer Ali Alenazy, Sufyan Habib and Shahid Husain
This study investigates the factors influencing citizen attitudes toward e-government services and their effects on the adoption of e-government services in Saudi Arabia. It sheds…
Abstract
Purpose
This study investigates the factors influencing citizen attitudes toward e-government services and their effects on the adoption of e-government services in Saudi Arabia. It sheds light on the moderating role of citizen motivation in the relationship between factors influencing citizen attitudes in favor of e-government services and their adoption and usage behavior in the Saudi Arabian context. The study analyzes both the drivers propelling the uptake and the barriers impeding it.
Design/methodology/approach
A descriptive research design was employed in this study, which surveyed 487 respondents from Jeddah and Madina cities and the surrounding region. The research identifies key drivers, including cultural factors, digital literacy, government policy and interventions, privacy and security, technical infrastructure, support services and citizen trust, alongside barriers such as concerns about data security and digital literacy.
Findings
The findings reveal the complex interplay of these factors in shaping citizen attitudes toward e-government services and their effects on adoption in Saudi Arabia. The study indicates that citizen motivation toward e-government services moderates the relationship between, adoption and usage behavior.
Originality/value
This study contributes valuable insights for policymakers and practitioners by offering a nuanced perspective on e-government service adoption in the Saudi Arabian context. It enhances our understanding of the factors influencing citizen attitudes and their impact on e-government adoption, highlighting the importance of citizen motivation as a moderating factor in this relationship.
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Augustino Mwogosi, Deo Shao, Stephen Kibusi and Ntuli Kapologwe
This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.
Abstract
Purpose
This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.
Design/methodology/approach
A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.
Findings
The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.
Originality/value
This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.
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