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To enrich the research on the economic consequences of enterprise digital development from the perspective of capacity utilization.

Using a sample of listed firms from 2010 to 2020, this paper exploits text analysis of annual reports to construct a proxy for enterprise digital development.

Results show that enterprise digital development not only improves their own capacity utilization but also generates a positive spillover effect on the capacity utilization of peer firms and firms in the supply chain. Next, based on the incomplete information about market demand and potential competitors when making capacity-building decisions, the mechanism tests show that improving the accuracy of market forecasts and reducing investment surges are potential channels behind the baseline results. Cross-sectional tests show the baseline result is more pronounced when industries are highly homogeneous and when firms have access to less information.

This paper contributes to the research related to the economic consequences of digital development. With the development of the digital economy, the real effects of enterprise digital development have also triggered extensive interest and exploration. Existing studies mainly examine the impact on physical operations, such as specialization division of labor, innovation activities, business performance or total factor productivity (Huang, Yu, & Zhang, 2019; Yuan, Xiao, Geng, & Sheng, 2021; Wang, Kuang, & Shao, 2017; Li, Liu, & Shao, 2021; Zhao, Wang, & Li, 2021). These studies measure the economic benefits from the perspective of the supply (output) side but neglect the importance of the supply system to adapt to the actual market demand. In contrast, this paper focuses on capacity utilization, aimed at estimating the net economic effect of digital development by considering the supply-demand fit scenario. Thus, our findings enrich the relevant studies on the potential consequences of digital development.

The purpose of this study is to determine research areas that are most favorable in supporting the development and manufacturing of electric vehicle (EV) components locally in Indonesia for 2025–2035. Therefore, will provide direction for the formulation of the related government policies and programs. Consequently, an EV technology research priority must be identified.

A technology foresight (TF) procedure which consists of a STEEPV analysis, followed by scenarios development and expert elicitation techniques, was conducted to determine an EV technology research priority that may direct future specific local component innovations, and therefore businesses.

The results of this study indicate that research in a range of EV battery technologies, technologies relating to a variety of key components (to increase local content) and autonomous systems were important to support the local development and manufacturing of EV components in Indonesia.

In this study, the scenarios development process was conducted based on selected available experts, mostly internally from BRIN. Some biased opinions may be present.

There have not been any TF studies regarding the development of EV technology research priority in Indonesia.

The proliferation of digital communication on social media provides new opportunities for businesses to take advantage of Internet memes to boost customer engagement. Academic literature on digital communications mostly focuses on popular forms such as selfies, branded posts, and branded emoticons. Less attention has been paid to brand memes and their implications for brand management. Based on the cue utilization theory, this research aims to investigate the informational cues of brand memes foster brand partnerships.

The structural equation modeling and importance-performance matrix analysis were used to empirically validate the research hypotheses with 595 respondents to an online survey.

Three informational cues of brand memes (i.e. comprehensibility, novelty, and meme-brand congruity) stimulated consumers' attitudes, which in turn impacted consumer-brand relationships. Another brand meme informational cue, sarcasm, negatively moderated the relationships between the three informational cues and consumer-brand relationships.

Our findings indicate that a brand can engage consumers in conversations on social media and foster long-term consumer-brand relationships through brand memes.

The management issues of this article, and the author is attempting to address these issues, are as follows: What is the optimal decision of each entity in the closed-loop supply chain for the cascading utilization of power batteries under three government measures: no subsidies, subsidies and rewards and punishments? How do different measures affect the process of cascading the utilization of power batteries? Which measures will help incentivize cascading utilization and battery recycling efforts?

The paper uses game analysis methods to study the optimal decisions of various stakeholders in the supply chain under the conditions of subsidies, non-subsidies and reward and punishment policies. The impact of various parameters on the returns of game entities is tested through Matlab numerical simulation.

The analysis discovered that each party in the supply chain will see an increase in earnings if the government boosts trade-in subsidies, which means that the degree of recycling efforts of each entity will also increase; under the condition with subsidies, the recycling efforts and echelon utilization rates of each stakeholder are higher than those under the incentive and punishment measure. In terms of the power battery echelon’s closed-loop supply chain incentive, the subsidy policy exceeds the reward and punishment policy.

The article takes the perspective of differential games and considers the dynamic process of exchanging old for new, providing important value for the practice of using old for new behavior in the closed-loop supply chain of power battery cascading utilization.

In the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.

A survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.

Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.

This study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.

The practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.

This study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.

The purpose of this study is to investigate the influence of collaborative culture on product and process innovation via mediating role of knowledge management and moderating role of information technology utilization.

This paper used structural equation modeling to examine the level of how collaborative culture and knowledge management practices (KMC) affect two types of innovation capabilities, namely, product innovation and process innovation, using data collected from 313 participants in 128 manufacturing and service firms.

The research findings highlight positive mediating role of KMC between collaborative culture and two specific forms of innovation. In addition, the paper first confirms the moderating role of information technology utilization in the relationships between KMC and process innovation capability. The results underline the necessity of building a collaborative culture to enhance KMC for promoting innovation capabilities in an organization.

Future research should explore the influence of potential benefits and values of collaborative culture on other strategic or behavioral factor to produce better innovation competence for firms.

This paper offers CEOs/leaders a deeper understanding of the effects of key antecedents, mechanisms and processes to promote product and process innovation in their firms.

This paper is unique in the attempts to provide a prospective solution for firms to pursue and improve innovation by its meaningful insights on the mediating role of KMC and moderating effect of information technology utilization in the relationship between collaborative culture and specific dimensions of innovation capability.

Front-of-packaging (FOP) is a critical branding tool that uses “cues” to communicate product attributes and establish distinct brand images. This paper aims to understand how food brands utilize cues and their relative proportions to hierarchically communicate brand image and belonging to particular subcategories.

A content analysis is used for analysing 543 food FOPs sold in Australia (breakfast cereals, chips, snack bars). Samples are collected and classified into product sub-categories defined by ingredients, consumer-audience and retail placement. A novel 10 × 10 coding grid is applied to each FOP to objectively analyse cue proportion, with statistical comparison undertaken between sub-categories.

Results reveal intrinsic cues are favoured over extrinsic cues, except for those in the eatertainment sub-category. Hierarchies are evidenced that treat product and branding cues as primary, with health cues secondary. Statistically significant differences in cue proportions are consistently evident across breakfast cereals, chips and snack-bar FOPs. Clear differentiation is evidenced through cue proportions on FOP for health/nutrition focused sub-categories and eatertainment foods.

“Cue utilization theory” research is extended to an evaluation of brand encoding (not consumer decoding). Design conventions reveal how cue proportions establish a dialogue of communicating brand/product image hierarchically, the trade-offs that occur, a “meso-level” to Gestalt theory, and achieving categorization through FOP cue proportions. Deeper understanding of packaging design techniques provides inter-disciplinary insights that extend consumer behaviour, retailing and design scholarship.

Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, this study aims to explore the factors influencing educators’ acceptance and utilization of chat generative pretrained transformer (ChatGPT) in the context of higher educational institutions. This study additionally examines the moderating influence of trust on the association between intention and adoption of ChatGPT.

A structured questionnaire was disseminated to 1,214 educators following the purposive sampling method. The hypothesized relationships between the extended UTAUT model constructs and ChatGPT adoption were examined using structural equation modeling.

The results of this study indicate positive impact of ChatGPT adoption and its use behavior (UB) among educators. Performance expectancy, effort expectancy, hedonic motivation, facilitating conditions and habit have a positive impact on behavior intention to use this tool among educators in higher education institutions, However, the educators intend to use ChatGPT in a personal capacity and are negatively influenced by the social groups. Moreover, behavior intention to adopt ChatGPT has a larger effect on the UB due to the higher involvement of trust.

This research adds to the literature on technology adoption by emphasizing the significance of trust in the adoption of emerging technologies, such as big language models. This study’s findings have implications for ChatGPT technology developers, legislators and academics considering the implementation of ChatGPT for teaching and research.

This research investigates ChatGPT adoption among higher education educators using the UTAUT model. It emphasizes the role of trust in adoption, highlights key adoption factors and reveals a paradox: educators are personally inclined to use ChatGPT but hesitant due to negative social influence. This study's novelty lies in its focus on trust and its implications for developers, legislators and educators, offering valuable insights for integrating ChatGPT in education.

This chapter discusses how the real options theory can be useful for understanding the adoption of human resources management (HRM) practices. The authors review how the real options theory has provided insights into the processes through which firms manage uncertainties involved in the adoption of HRM practices. The authors offer propositions for future HRM research from the real options perspective. The authors contend that analyzing HRM practice adoptions through the lens of real options theory can enhance our understanding of the mechanisms through which firms choose which HRM practices to adopt and how they adjust the timing, scale, and methods of investment in these practices. Specifically, the authors suggest that differences in information relevant to valuation of HRM options are the source of distinct choices of HRM options across firms. Finally, the authors propose advancing knowledge on HRM practice adoptions by using a portfolio of options approach, as well as considering factors like competitors, path dependence, and switching options.

Current knowledge and research on students’ utilization and interaction with generative artificial intelligence (AI) tools in their academic work is limited. This study aims to investigate students’ engagement with these tools.

This research used survey-based research to investigate generative AI literacy (utilization, interaction, evaluation of output and ethics) among students enrolled in a four-year public university in the southeastern USA. This article focuses on the respondents who have used generative AI (218; 47.2%).

Most respondents used generative AI to generate ideas for papers, projects or assignments, and they also used AI to assist with their original ideas. Despite their use of AI assistance, most students were critical of generative AI output, and this mindset was reflected in their reported interactions with ChatGPT. Respondents expressed a need for explicit guidance from course syllabi and university policies regarding generative AI’s ethical and appropriate use.

Literature related to generative AI use in higher education specific to ChatGPT is predominantly from educators’ viewpoints. This study provides empirical evidence about how university students report using generative AI in the context of generative AI literacy.