Search results

The coronavirus disease 2019 (COVID-19) pandemic has had a big impact on organisations globally, leaving organisations with no choice but to adapt to the new reality of remote work to ensure business continuity. Such an unexpected reality created the conditions for testing new applications of smart home technology whilst working from home. Given the potential implications of such applications to improve the working environment, and a lack of research on that front, this paper pursued two objectives. First, the paper explored the impact of smart home applications by examining the factors that could contribute to perceived productivity and well-being whilst working from home. Second, the study investigated the role of productivity and well-being in motivating the intention of remote workers to use smart home technologies in a home-work environment in the future.

The study adopted a cross-sectional research design. For data collection, 528 smart home users working from home during the pandemic were recruited. Collected data were analysed using a structural equation modelling approach.

The results of the research confirmed that perceived productivity is dependent on service relevance, perceived usefulness, innovativeness, hedonic beliefs and control over environmental conditions. Perceived well-being correlates with task-technology fit, service relevance, perceived usefulness, perceived ease of use, attitude to smart homes, innovativeness, hedonic beliefs and control over environmental conditions. Intention to work from a smart home-office in the future is dependent on perceived well-being.

The findings of the research contribute to the organisational and smart home literature, by providing missing evidence about the implications of the application of smart home technologies for employees' perceived productivity and well-being. The paper considers the conditions that facilitate better outcomes during remote work and could potentially be used to improve the work environment in offices after the pandemic. Also, the findings inform smart home developers about the features of technology which could improve the developers' application in contexts beyond home settings.

Purpose: This study examines the integration of smart agriculture and supply chain management systems within Turkey’s agricultural sector under Industry 5.0.

Need for the study: With global challenges like the COVID-19 pandemic and climate change, ensuring safe food production and accessibility is critical. However, there is a gap in understanding the readiness and awareness of Industry 5.0 technologies in agriculture and logistics. This study aims to fill this gap by investigating the adoption and implications of smart agriculture and logistics in Turkey.

Methodology: Drawing on secondary data from regulators, farmers, and supply chain experts, this study employs coding methods, particularly theoretical coding, to develop a framework for assessing the sector’s readiness for smart technologies and Industry 5.0 awareness.

Findings: This study reveals insights into the adoption and impacts of smart agriculture and supply chain systems in Turkey. It identifies factors shaping institutional logics within the sector and explores how Industry 5.0 technologies influence these logics. Additionally, it offers theoretical insights into Turkey’s agricultural future in the Industry 5.0 era.

Practical implications: Practically, this study informs policymakers, regulators, farmers, and supply chain stakeholders about Industry 5.0 technology readiness and awareness in Turkey’s agricultural sector. It guides strategies for smart technology adoption, improving productivity, food safety, accessibility, and sustainability. Furthermore, it contributes to institutional logics literature, shedding light on the independent logics driving organizational settings in smart agriculture and supply chain management.

This paper aims to investigate the impact of organizational identification on employees’ usage of newly introduced Accounting Information Systems (AIS), with a particular focus on the mediating influence of attitude and the moderating roles of gender and collectivism. Drawing on social identity theory, the research framework was developed.

Data were collected using a survey method from 500 service sector employees adapting to a newly implemented AIS. Structural equation modeling (SEM) in Mplus was used for the model testing.

The results indicate that organizational identification does not directly influence employees’ AIS usage. Nevertheless, the study findings demonstrate that it exerts an indirect influence on employees’ AIS usage through the mediation of their attitude toward the using of new AIS. The moderation of gender and collectivism emerged as crucial, showing the strongest effect of organizational identification on AIS usage among highly collectivist female employees, followed by highly collectivist male employees, and then less collectivist female employees. This relationship is weakest in less collectivist male employees.

To the best of the authors’ knowledge, this investigation stands as the first to integrate social identity theory with cultural values and the gender of employees, offering insights into the employees’ AIS usage.

As a highly knowledge-intensive activity, digitalization is changing the construction industry landscape and is encouraging construction firms to explore the transformation. This study establishes a new theoretical model aimed at examining the impact of three types of intellectual capital (IC) on digitalization through the lens of knowledge-based view and explores how IC and digitalization influence sustainability performance from the triple bottom line principles.

A questionnaire survey was conducted to collect data from Chinese construction firms using convenience sampling. A total of 181 valid responses were obtained. Then, a partial least squares structural equation modelling (PLS-SEM) technique was executed through Smart PLS 3.0 software. The measurement model was assessed to ensure reliability and validity, and the structural model was analysed to test the proposed hypotheses.

The empirical results confirm the positive impact of IC on digitalization and digitalization on sustainability performance. Moreover, digitalization plays a significant mediating role in the relationship between IC and sustainability performance.

The results provide empirical evidence supporting the different roles of IC and digitalization in improving sustainability. The findings contribute to enhancing the understanding of digitalization practices from the perspective of IC and provide theoretical and managerial implications for sustainability issues in the context of the construction industry.

In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

The current study aims to investigate how young smartphone users in Bangladesh relate their brand experiences to brand loyalty. In addition, we want to visualize the direct and mediating effects of brand satisfaction, brand love and brand advocacy in our model.

The researchers examined the hypotheses by employing structural equation modeling (SEM) in AMOS and Decision Analyst STATS, version 2.0, with a sample size of 470 Bangladeshi smartphone users. The authors constructed the conceptual model by drawing upon both theoretical and empirical foundations. The researchers obtained data by utilizing an adopted and self-administered pre-structured questionnaire distributed via an online platform.

The results showed that brand experience greatly influences brand satisfaction, love, advocacy and loyalty, all of which have a significant impact on users’ brand loyalty across the country. The findings also suggested that the function of brand satisfaction as a critical mediator in the link between brand experience and brand loyalty was significant.

This experiment contributes to the body of knowledge by focusing on emotional brand attachments like brand satisfaction, love and advocacy and proposing that they can mediate experience and loyalty in the mobile market. The study also helps managers and executives better understand the primary drivers of smartphones, which are essential for generating and sustaining consumers’ happiness and loyalty in today’s highly competitive consumer market.

This study aims to focus on exploring the role of fear of missing out (FOMO) in the technology adoption context, whereby the bottom-of-the-pyramid (BOP) segment was studied to explore the factors responsible for the development of behavioural intentions (BI) to use unified payments interface (UPI), a disruptive technological phenomenon in the mobile payment systems field.

A mixed-method research approach involving both qualitative and quantitative methods was used. Initially, qualitative data obtained through interviews with UPI’s BOP users were subjected to thematic analysis, leading to the identification of eight factors and the construction of a conceptual model. Subsequently, 354 responses were gathered, and empirical analysis was conducted using structural equation modelling in AMOS 23.0.

Eight factors, including personal and social benefits, perceived security risk, socio-cultural influences, governmental influence, usability, psychological inertia, perceived value (PV) and FOMO, were discovered. The quantitative examination confirmed the validity of the conceptual model in the BOP context, explaining 51% of the variance in BI. FOMO and PV emerged as robust indicators of UPI adoption, with PV significantly regulating the impact of FOMO.

To the best of the author’s knowledge, this study is one of the first to explore what drives BOP users in an emerging economy to adopt UPI. The conceptual model it presents contributes to the advancement of technology adoption literature by incorporating FOMO alongside newly identified BOP-specific factors.

This research aims to investigate the impact of artificial intelligence (AI) adoption on the innovation dynamics of Chinese manufacturing enterprises, with a specific focus on the intricate interplay with the labor structure.

Leveraging panel data of listed companies from 2010 to 2022, this study employs the two-way fixed effects (TWFE) model to examine the influence of AI adoption on Chinese manufacturing companies' innovativeness. Firm-level AI adoption is measured by constructing a three-dimensional attention, application and absorption index.

The results indicate that (1) AI adoption has a positive impact on both internal innovation capability and external innovation interaction, (2) AI adoption has dual effects on the education and skill structure of labor in manufacturing enterprises and (3) enterprises with a highly educated and skilled workforce exhibit a stronger influence of AI adoption on innovativeness.

This research contributes to the academic and practical discourse by unveiling the underlying mechanisms of AI affecting innovation and introducing a new measurement of the AI adoption index. The findings emphasize the need for a highly educated and skilled workforce to navigate the complexities of AI-driven innovation, offering valuable theoretical and practical implications for policymakers and enterprises.

The purpose of this systematic literature review is to identify the antecedents that have enabled the adoption of artificial intelligence (AI) in Higher Education (HE) institutions at both a macro and micro level. The term adoption is in reference to the diffusion of technology that is actively chosen for use by the targeted demographic. Within the context of this paper, adoption is largely referring to the factors that influence the acceptance and use of AI as a tool for personalized learning.

To develop our understanding and appreciation of the valuable impact that AI potentially has upon personalized learning the following systematic literature review was conducted. An acceptable systematic literature review is a comprehensive method of fully analysing and evaluating all available research in the chosen area or specific research query.

The findings from this study have particular implications for personalized learning in the adoption and diffusion of AI and an increasing integration of macro, structural, and micro, individual. Developing and managing AI in education is seen, from the literature, to becoming more embedded in the teaching and learning process. The paper identifies the following: antecedents that supports the adoption of AI for personalized learning; application of AI technologies in the teaching and learning process; AI technologies that enable personalized instruction and learning; generative AI that supports intuitive learning through tracking data.

Personalized learning remains focused on customizable “choice-driven” learning and education. In addition, personalized learning and instruction is defined as being a responsive and structured method that adapts to each individual learner’s method of learning so that all may achieve their capabilities and actively participate. This solidifies the intrinsic connection between teaching and learning through personalized technologies such as AI.

This study focuses on the unsolved problem of the mechanism underlying the effect of big data analytics capabilities (BDAC) on organizational learning (OL) by shedding light on the roles of knowledge management (KM) and data-driven decision-making (DDDM) as mediators. Practitioners and academics have considered the underlying mechanism through which BDAC enhance OL.

This study proposes a framework by reviewing existing management literature and collecting data from 343 respondents. The model was tested using structural equation modeling and the bootstrapping method.

The results indicate that BDAC enhances OL, KM and DDDM. Furthermore, KM enhanced the DDDM and OL. In addition, DDDM enhances the OL. Finally, we verified that KM and DDDM mediated the link between BDAC and OL.

This study provides new insights into the mechanism underlying the effect of BDAC on OL by elucidating the mediating roles of DDDM and KM.