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This research aims to explore whether or how organizations adopt people analytics (PA), its value and potential socio-technical factors that can enable or hinder PA projects by disrupting and reshaping human resource management. We do this by focusing on the Italian context.

We conduct a scoping review of data collected between 2018 and 2022 via Google Alerts (GA), a content change detection and notification service that is gaining popularity in scholarly research.

Our findings suggest that the diffusion of PA applications in Italy, especially those of a descriptive nature, is growing. Most of the existing PA applications are positioned in a positive technocratic light, envisioning the value of PA for both employees and organizations. The value for the latter appears to be direct, while the value for employees is realized through organizational initiatives. The findings also suggest that while enablers can vary between PA application types, the barriers, especially technological and environmental, are generic for both descriptive and predictive/prescriptive PA applications.

Theoretically, we propose a framework for analyzing PA applications, their values, enablers and barriers. Methodologically, we present and describe in detail a novel approach, drawing on GA that can be used to study PA in specific contexts. Practically, our study serves as a helpful point of reference for managers planning or implementing PA in Italy, for benchmarking PA in Italy over time and for comparative international studies.

This study aims to examine the effect of application architecture and application efficiency on the intention to continue using Islamic bank with data security as a moderator. The investigation was situated within the framework of a hacker attack that compromised the security of customer data at one of Indonesia’s largest Islamic bank.

A survey questionnaire method was used, and the sample population comprised users of Islamic bank in Indonesia. The respondents were then selected purposively with the criteria of individuals who were using mobile banking services. Furthermore, data collection in this study was carried out by distributing questionnaires online. To validate the questionnaire, consultation and validation were conducted by engaging four experts and conducting a pilot study. Hypothesis testing was performed using the structural equation modeling method based on partial least squares.

The results of the partial least square structural model assessment showed that application efficiency and data security positively influenced the intention to continue using Islamic bank, while application architecture had no effect. Furthermore, data security could not moderate the relationship between application architecture and efficiency toward the intention to continue using Islamic bank.

The results of this study suggested that Islamic banking practitioners must prioritize the enhancement of digital banking services, with a specific focus on improving application efficiency and ensuring robust data security. These two dimensions were critical determinants influencing the intention to continue using Islamic bank.

This study addressed the issue of data security as a moderator, particularly in the context of hacker attacks targeting a major Islamic bank in Indonesia. Furthermore, this current report expounded on the study conducted by Mir et al. (2022) by introducing novel dimensions to the e-service quality of internet banking.

The massive adoption of virtual reality (VR) applications has started since the COVID-19 pandemic, and until now, VR applications are still being used. However, there is limited research that analyses the consumer's perspective on the adoption of VR applications. Thus, this study discovers the adoption of VR applications in the hospitality sector by integrating the value-based adoption model (VAM) and VR quality.

The data were gathered through a survey of 500 respondents and evaluated through the structural equation model-partial least squares (SEM-PLS).

Employing SEM-PLS and importance-performance map analysis (IPMA), the findings revealed that VR quality and perceived value are essential determinants in the adoption of VR applications in the hospitality industry.

Practically, this study encourages the hospitality industry to create and develop high-quality VR application technology to benefit visitors. Through this study, hospitality marketing managers, governments and others concerned with the hospitality industry’s future development can create effective ways to increase the adoption of VR applications in this industry.

This study offers novel perspectives into the theory and application of VR quality and VAM in the adoption of VR applications in the hospitality industry.

Farmer sentiment may be an important indicator for the agricultural sector, similar to the way that consumer sentiment is linked to the general economy. This study uses the Purdue University–CME Group Ag Economy Barometer to test the degree to which farmer sentiment is correlated with demand for United States Department of Agriculture Farm Service Agency (FSA) direct loan applications.

We estimate the dynamics between farmer sentiment and applications to FSA direct operating or farm ownership loans using monthly measures of farmer sentiment and loan applications from October 2015 to April 2023 and pairwise vector autoregression.

A negative relationship exists between farmer sentiment and FSA direct operating loan applications. In contrast, a positive relationship exists between farmer sentiment and FSA direct farm ownership loan applications. Together, the estimated nonzero relationships suggests that the Ag Economy Barometer may be a leading indicator for the Agricultural Economy and that FSA loan programs play a nuanced role in the agricultural credit market.

This study uses unique data sources to further the discussion on the link between farmer sentiment and real economic outcomes and the role of an important US Federal Government farmer lending program: FSA direct loans.

The purpose of this study is to investigate the determinants of behavioral intention to use smartphone diet applications within the emerging market. Specifically, it focuses on the Privacy Calculus Model constructs, encompassing perceived risk and perceived benefit, as well as the pivotal elements of trust and self-efficacy. It also explores the moderating influence of experience on the influencing factors and intention to use a diet application.

In a survey with 572 respondents, data analysis was conducted using partial least squares (PLS) structural equation modeling.

The findings reveal that perceived risk exerts a significant negative influence on behavioral intention. Conversely, perceived benefit, trust and self-efficacy exhibit a positive impact on behavioral intention. Moreover, the study delves into the moderating role of users' experience, which is found to significantly influence these relationships, suggesting that user experience plays a pivotal role in shaping the adoption dynamics of diet applications.

The limitations of this study may include the sample size and the specific focus on the emerging market of Saudi Arabia. The implications of the findings are relevant for scholars, developers, marketers, and policymakers seeking to promote the use of smartphone diet applications.

This study adds value by exploring the determinants of behavioral intention in the context of smartphone diet applications, and it is a first attempt to test the moderating role of users' experiences, providing valuable insights for various stakeholders in the field.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

This study examined the determinants of mobile health (M-Health) application, adoption, usage and discontinuation among corporate workers diagnosed with hypertension and diabetes in Ghana.

The diffusion innovation and reasoned action theories were employed using an exploratory design. Three hundred corporate workers diagnosed with diabetes and hypertension from three health facilities for the past six months were sampled for the study using a multi-stage sampling technique and administered questionnaires. Descriptive statistics and logistic regression tools were employed in the analysis of data.

The study found a significant number of factors influencing m-health applications adoption, usage and discontinuity. These factors include nature and demand of job, perceived advantage, compatibility, complexity, triability, aesthetics and trust. Aesthetics emerged as the strongest predictive factor for the adoption, usage and discontinuity of use among diabetic and hypertensive corporate workers. With the adoption of M-Health applications, compatibility, as well as nature and demand of job, were significant predictors. With the usage of M-Health applications, complexity, triability, aesthetics and trust were significant predictors. Moreover, perceived advantage, compatibility, complexity and triability influenced significantly the choice to discontinue using M-Health applications. The study concluded that M-Health application functionalities play a valuable role in patients’ intention to adopt, use and discontinue the use of an M-Health application in Ghana.

This exploratory study offers in-depth insight into how major M-Health application features affect its adoption, usage and discontinuity, providing crucial information for future research and the improvement of chronic condition healthcare delivery.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.