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This research aimed to determine the relationship between technostress and information technology support (IT) and HRIS-related user satisfaction. The study also investigated the moderating influence of technological self-efficacy on the links between technostress, IT support, and HRIS user satisfaction.

This quantitative study used the deductive research approach. The study's population was HRIS users from 37 Malaysian public sector organizations. The judgmental sampling technique was employed, and the sample size was 490. Data analysis was conducted through SPSS and PLS-SEM.

The study showed that technostress is significantly associated with HRIS user satisfaction. While IT support, results show that literacy support, technological support, and technological involvement facilities significantly correlate with HRIS user satisfaction. In contrast, innovation support had an insignificant relationship with HRIS user satisfaction. The study also tested the moderation effect of technology self-efficacy in the relationships and found that technology self-efficacy only moderated the association of technostress and HRIS user satisfaction.

HR departments of government agencies can use the study's findings to understand to what extent HRIS system users are satisfied and what needs to be done to improve the HRIS system. The study also sheds light on the technological pressures that employees encounter.

Present study contributed to the literature by developing a study model of technostress, information technology support, technology self-efficacy, and HRIS user satisfaction. Further, the relationship of technostress with HRIS user satisfaction and the moderating effect of technology self-efficacy was explored for the first time, representing the study's novelty. The study also contributed to social cognitive theory and the theory of reasoned action by exploring attitudinal and behavioral aspects along with the cognitive aspects of users.

This study aims to investigate the motivations for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Specifically, the effects of relative advantage of the technologies, competitive pressure, and government support on the adoption are explored. Moreover, the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies is examined.

A research model is developed based on the technology-organization-environment (TOE) framework strengthened by institutional theory. Structural equation modeling (SEM) approach is employed to evaluate the model using data obtained from 215 manufacturing firms through a cross-industry survey. Additionally, a post-hoc analysis is conducted using cluster analysis and ANOVA.

The results show that competitive pressure and government support significantly promote top management support, which in turn contributes to the adoption of Industry 4.0 technologies. Relative advantage of the technologies is not significantly related to the adoption.

This study does not explore the relationship between technology type and the specific needs of manufacturing firms. Future researchers can conduct a more comprehensive analysis by examining how different technology types align with the unique needs of individual companies.

The findings of this study have implications for both policymakers and managers. Policymakers can leverage these insights to understand the underlying motivations behind manufacturing firms' adoption of Industry 4.0 technologies and develop promoting policies. In turn, managers should keep an eye on government policies and utilize government support to facilitate technology adoption.

This study uncovers the underlying motivations—government support and competitive pressure—for the adoption of Industry 4.0 technologies among manufacturing firms in developing economies. Meanwhile, it complements previous research by showing the mediating role of top management support between environmental factors (government support and competitive pressure) and the adoption of Industry 4.0 technologies.

The purpose of this paper is to examine the influence of government support and technology usage on family business entrepreneurial intention. The paper also investigates the moderating impacts of gender on the family business entrepreneurial intention with its two predictors.

This paper has used both resource-based view and dynamic capability view theory as well as literature on family business entrepreneurship to develop the theoretical model. Later, the theoretical model has been validated using structural equation modelling (CB-SEM) with respondents from Indian family business enterprises. This study has used a purposeful and convenience sampling approach.

This study has shown the significance of technology usage as well as government support to improve the family business enterprise. The study highlights that there is a moderating impact of gender on the relationship between government support and technology usage with entrepreneurial intention in family business.

This study adds value towards body of literature in entrepreneurship, gender, and business, as well as family business literature. The study shows how gender acts as a moderator in case of family business entrepreneurship. The study is cross sectional in nature and has limited number of respondents from India. Thus, the findings cannot be generalizable.

This study is a unique study as it investigates the influence of both government support as well as technology usage by the family business firms for entrepreneurial intention. The proposed theoretical model has a high predictive power which makes the model effective. Moreover, this study also examines the moderating impacts of gender on entrepreneurial intention in the family business which adds value to the existing body of knowledge.

When the emergency transition started in the spring of 2020 in the USA, teachers had to quickly switch from traditional in-person teaching to distance and remote teaching, regardless of their level of preparation. The distance and remote learning environments and contexts were different from traditional classrooms, which significantly changed the way teachers communicated and engaged with students in learning. The purpose of this study was to explore the workplace learning experience of K-12 educators during their work transition due to the COVID-19 pandemic in the USA.

In total, 30 qualitative, in-depth, semi-structured, one-on-one interviews were conducted with K-12 educators in Arkansas in the USA and synthesized their experiences.

This study identified four major themes in the workplace learning experiences of K-12 teachers during the COVID-19 pandemic: major challenges in workplace learning, including limited time, information overload, lack of relevance and customization and balancing priorities; challenges specific to different subgroups, such as age differences, prior experience and access to technology; strategies of workplace learning, notably collaborative learning, on-the-job training and professional development; and support for workplace learning, encompassing both formal support from schools and districts and informal support from family, friends and peers.

The paper provides original insights into K-12 teachers’ workplace learning during the COVID-19 pandemic by understanding their adaptation strategies. It fills a research gap by highlighting both the challenges and support mechanisms in educational transitions during a crisis.

As global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.

This study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.

The study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.

By introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

Working from home (WFH) was one of the major changes that occurred in many organizations during the COVID-19 pandemic. This also led to online training being conducted during this WFH period. The present study investigated the role of technology, manager support and peer support on self-efficacy and job outcomes (i.e. training transfer, work engagement and job satisfaction) of employees while WFH.

The study framework incorporated Bandura's self-efficacy theory. Data were collected from 852 employees in India, and structural equation modeling was used to analyze the data.

The study found positive relationships between ease of technology use, manager support and peer support on self-efficacy and a negative relationship between self-efficacy and technostress. The study also found significant positive relationships between self-efficacy and training transfer, work engagement and job satisfaction. Moreover, the study also identified the moderating effects of WFH and technical issues in the relationships of self-efficacy with training transfer, work engagement and job satisfaction.

The study is novel in that it extended self-efficacy theory regarding the WFH context with influencers such as technology, managers and peers as organizational factors. It also demonstrated the effectiveness of remote working and online training considering the potential antecedents while WFH. Moreover, the study highlighted the simultaneous role of technology and people (managers and peers) in enhancing job outcomes by increasing self-efficacy among employees.

A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

The purpose of this study is to explore how big data analytics (BDA) as a potential information technology (IT) innovation can facilitate the retail logistics supply chain (SC) from the perspective of outbound logistics operations in the United Kingdom. The authors' goal was to better understand how BDA can be integrated to streamline SCs and logistical networks by using the technology, organisational and environmental model.

The authors applied existing theoretical foundations for theory building based on semi-structured interviews with 15 SC and logistics managers.

The perceived benefits of using BDA in outbound retail logistics comprised the strongest predictor amongst technological, organisational and environmental issues, followed by top management support (TMS). A framework was proposed for the adoption of BDA in retail logistics. Contextual concepts from previous literature have helped us understand how environmental changes impact BDA decision-making, as such: (i) SC maturity levels and connectivity affect BDA utilisation, (ii) connected SCs improve data accessibility and information exchange, (iii) the benefits of BDAs also affect adoption and (iv) outsourcing complex tasks to experts allows companies to focus on core businesses instead of investing in IT infrastructure.

Outside the key findings listed, this study shows that there is no one-size-fits-it-all approach for use within all organisational settings. The proposed framework reveals that the perceived benefit of BDA is non-transferrable and requires top-level management support for successful implementation.

The existing literature focusses on the approaches to applying BDA in SC and logistics but fails to present a deep dive into retail outbound logistics activity. This study addresses the “how” and proposes a social-inclusive framework for a technology-enabled topic.

The success of SMEs' financial and market performance (MAP) depends on the firms' level of blockchain technology adoption (BCA) and identifying the crucial antecedents that influence SMEs' adoption. Therefore, this research attempts to develop an integrated model to understand and predict the determinants of BCA and its effect on SMEs' performance. The purpose of this paper is to address this issue.

The theoretical foundations are the technology–organization –environment (TOE) framework and the resource-based view (RBV) perspective. The authors distributed a survey to SMEs in South Africa and received 311 responses. The covariance-based structural equation modeling (CB-SEM) followed by the artificial neural network (ANN) technique was used for the data analysis.

The SEM results showed that SMEs' relative advantage, compatibility, top management support (TMS), organizational readiness (ORD), competitive pressures (COP), external support, regulations and legislation significantly influence SMEs' BCA. However, complexity negatively impacts SMEs' BCA. The analysis results also revealed that SMEs' BCA significantly influences the financial performance of the firms, followed by MAP. Furthermore, model determinants were input to an ANN modeling. The ANN results showed that TMS is the most critical predictor of SMEs' BCA, followed by ORD, COP, external support, and regulations and legislation.

The results provide valuable information for SMEs when maneuvering their adoption strategies in the scope of blockchain technology. Additionally, from the perspective of an emerging market, the study has successfully contributed the TOE framework and the RBV.

This study is the first work to explore the determinants of BCA in the context of SMEs from a developing country. This paper is also one pioneer in attempts to develop a causal and predictive statistical model for predicting the determinants of BCA in SMEs' performance.

In 2022, the Baltimore professional development school (PDS) partner schools, John Ruhruh Elementary/Middle School (JREMS) and Notre Dame of Maryland University (NDMU) received funds through a Maryland Educational Emergency Revitalization (MEER) grant to determine (a) to what extent additional resources and professional development would increase JREMS teachers’ efficacy in technology integration and (b) to what extent NDMU professional development in the form of workshops and self-paced computer science modules would result in greater use of technology in the JREMS K-8 classrooms. Results indicated a statistically significant improvement in both teacher comfort with technology and integrated use of technology in instruction.

Survey data were collected on teacher-stated comfort with technology before and after grant implementation. Teachers’ use of technology was also measured by unannounced classroom visits by administration before and after the grant implementation and through artifacts teachers submitted during NDMU professional development modules.

Results showing significant increases in self-efficacy with technology along with teacher integration of technology exemplify the benefits of a PDS partnership.

This initiative was original in its approach to teacher development by replacing required teacher professional development with an invitation to participate and an incentive for participation (a personal MacBook) that met the stated needs of teachers. Teacher motivation was strong because teammates in a strong PDS partnership provided the necessary supports to induce changes in teacher self-efficacy.