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This investigation endeavors to examine the routes by which environmental–social–governance (ESG) performance influences the competitive landscape for logistics enterprises, with a particular emphasis on the function of digitalization in this complex process. The research underscores the significance of the ESG context in the realm of digitalization, providing valuable insights into its impact on the overall competitiveness of logistics enterprises.

This research gathers information from a total of 90 logistics enterprises that are publicly traded on the Shanghai and Shenzhen A-share stock markets for analysis and model testing. Due to the multiple pathways of influence and the constrained size of the sample, it has been decided that the Piecewise structural–equation–modeling (SEM) approach will be employed.

The research reveals that ESG factors positively impact enterprises' competitiveness (EC). The augmentation of competitiveness is attributed to the moderating role of green technology innovation (GTI) and agency costs between ESG and EC. In the context of digitalization, the level of digitalization of logistics enterprises may create a capital squeeze effect on environmental performance, weakening competitiveness. Conversely, the level of digitalization positively regulates the promoting effect of governance performance on competitiveness.

This research provides a sound theoretical foundation for understanding how ESG contributes to boosting the competitiveness of Chinese logistics enterprises and extends the application of Piecewise SEM in the research field of logistics enterprise competitiveness. Furthermore, it offers a practical pathway for companies to implement ESG practices and foster competitiveness in digital environments.

Environmental concerns and the urgent issues of climate change have shifted the organization’s focus toward achieving sustainability. Therefore, this study aims to evaluate the complex relationships among green human resource management (GHRM), green innovation (GI), green human capital (GHC) and sustainable performance (SP).

To investigate the relationships, the study employed partial least square structural equation modeling to run an analysis on 384 managers working in the hotel sector in Egypt, selected through a simple random sampling technique.

The results demonstrate that GHRM positively influences both GI and GHC. Additionally, GI and GHC have a positive impact on SP. Furthermore, GHRM directly contributes to SP, with GI and GHC acting as significant mediators in the relationship between GHRM and SP.

This study advances theoretical understanding and offers practical insights by employing the resource-based view theory and the ability-motivation-opportunity theory.

This research introduces and empirically tests a novel conceptual framework that comprehensively assesses the impacts of GHRM, GI and GHC on SP.

Consumer purchasing behavior (CPB) plays a crucial role within the hospitality sector, specifically in restaurants situated in China, as it directly impacts the performance and profitability of businesses. In this context, there is a need to assess the factors influencing CPB. Therefore, this research seeks to inspect the influence of consumer attitudes (CA) on interpersonal influences (II) and CPB. Additionally, seeks to explore the mediation of II and the moderation of corporate social responsibility (CSR).

This study utilized cross-sectional data obtained through online surveys. To analyze the data, structural equation modeling was employed.

The research unveils a substantial and positive association between CA, II and CPB. It also found that II partially mediates CA and CPB association. Moreover, CSR was identified as a moderator in the connection between CA and CPB.

The implications of this study are of great significance for companies operating in the hospitality industry. They indicate that cultivating favorable CA and promoting II, while considering CSR initiatives, can effectively augment overall purchasing behavior. As a result, it can contribute to the achievement of business success.

This study offers a unique perspective on the factors driving purchasing behavior, shedding light on how businesses can enhance their performance and profitability in this specific setting.

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.

Studying mobile learning – the use of electronic devices (i.e. cellphone and tablets) to engage in learning across multiple contexts via connection to peers, media, experts and the larger world is a relatively new academic enterprise. This study analyzes the influencing factors of mobile learning (M-learning) motivation based on the time continuum model of motivation (TCMM).

The study uses structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to verify relationships between mobile learning motivation, attitude, need, stimulation, emotion, ability and reinforcement. Justification for the use of both methods lies in the complementarity relationships that existed between the variables and research methodologies. The sample contains 560 mobile learners' feedback.

Results show that attitude, need, emotion, ability and reinforcement are important factors to enhance mobile learning motivation, while stimulation is not.

This work highlights the importance of training for app designers on how to design an M-learning App with high learning motivation by paying prior attention to learning content, teaching team and online learning communities.

This study proposes three precise solutions (scholars, managers and practitioners) to improve learning motivation based on the categorization of mobile learners.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0226.