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Over the past decade, artificial intelligence (AI) technologies have rapidly advanced organizational management, with many organizations adopting AI-based algorithms to enhance employee management efficiency. However, there remains a lack of sufficient empirical research on the specific impacts of these algorithmic management practices on employee behavior, particularly the potential negative effects. To address this gap, this study constructs a model based on the psychological ownership theory, aiming to investigate how algorithmic management affects employees’ knowledge hiding.

This study validates the model through a situational experiment and a multi-wave field study involving full-time employees in organizations implementing algorithmic management. Various analytical methods, including analysis of variance, regression analysis and path analysis, were used to systematically test the hypotheses.

The study reveals that algorithmic management exerts a positive indirect influence on knowledge hiding through the psychological ownership of personal knowledge. This effect is particularly pronounced when employees have lower organizational identification, highlighting the critical role of organizational culture in the effectiveness of technological applications.

This study is among the first empirical investigations to explore the relationship between algorithmic management and employee knowledge hiding from an individual perception perspective. By applying psychological ownership theory, it not only addresses the current theoretical gap regarding the negative effects of algorithmic management but also provides new theoretical and empirical support for the governance and prevention of knowledge hiding within organizations in the context of AI algorithm application. The study highlights the importance of considering employee psychology (i.e. psychological ownership of personal knowledge) and organizational culture (i.e. organizational identification) under algorithmic management. This understanding aids organizations in better managing knowledge risks while maximizing technological advantages and effectively designing organizational change strategies.

This study aims to empirically investigate how knowledge paradox affects collaboration performance. Knowledge paradox, which arises from the simultaneous need for knowledge sharing and protection, is common in interorganizational collaboration. Using the ambidexterity perspective, this paper aims to reexamine the effect of the knowledge paradox on collaborative performance to explore the moderating roles of structural and contextual ambidexterity.

This study used a sample of 153 firms involved in vertical and horizontal collaboration, collected via questionnaires. Hypotheses were tested using hierarchical regression analysis.

This study demonstrates that the stronger the knowledge paradox is, the higher the potential for value creation. Thus, knowledge paradox has a positive impact on collaborative performance. The functions of structural ambidexterity and contextual ambidexterity strengthen this positive relationship.

This paper not only expands the theoretical application of the knowledge paradox and ambidexterity theory in the context of interorganizational relationships but also provides significant managerial implications. By comprehending the dynamics of the knowledge paradox and the role of ambidexterity, managers can make well-informed decisions to enhance their collaborative performance.

In the practice of live streaming e-commerce, the consumer demand is usually uncertain, and the inventory and prices can be decided by brand owners or streamers. To this end, this study examines the inventory and pricing decisions of the brand owner and streamer in a live streaming e-commerce supply chain under demand uncertainty.

In this study, four scenarios are considered, i.e. the brand owner determines the inventory and price (Scenario BB), the brand owner determines the inventory and the streamer determines the price (Scenario BS), the streamer determines the inventory and the brand owner determines the price (Scenario SB), and the streamer determines the inventory and price (Scenario SS).

The results show that the inventory and prices, as well as the profits of the brand owner and streamer increase with the consumer sensitivity to streamer’s sales effort level under the four scenarios. The inventory (price) is the highest under Scenario SS (SB), while that is the lowest under Scenario BB (BS). In addition, when the sensitivity is low, the brand owner’s profit is the highest under Scenario BB, otherwise, the profit is the highest under Scenario SS. Regardless of the sensitivity, the streamer’s profit is always the highest under Scenario SS.

Few studies focused on the inventory and pricing decisions of brand owners and streamers in live streaming e-commerce supply chains under demand uncertainty, while this work bridges the research gap. This study can provide theoretical basis and decision support for brand owners and streamers.

This study aims to investigate the impact of viewing different types of digital environment images on college students’ positive emotions, nature relatedness and environmental preference. This aimed to address the gap in empirical studies regarding the effects of digital environment images on these psychological constructs.

This study used a three-group experimental between-subjects design. Participants (48 undergraduate students) were divided into three groups, each viewing a different set of digital images (nature, city or object). Pre- and posttest measures assessed positive emotions and nature relatedness, whereas environmental preference was measured after image viewing. One-way analysis of variance and post hoc Tukey’s honestly significant difference (HSD) tests were used to analyze the data.

Viewing digital nature images elicited significantly higher positive emotions, nature relatedness and environmental preference compared to viewing city or object images. In addition, environmental preference for the digital object group was marginally higher than the city group.

This study was limited by its relatively small sample size. Although further research is needed to investigate the underlying mechanisms behind the observed effects, this study provides valuable implications for education, economic dimensions and public policy initiatives, encouraging the development of pro-environmental attitudes and behaviors.

The findings suggest that incorporating digital nature images into learning activities can promote positive emotions, nature relatedness and environmental preference among college students. This has implications for the design of digital learning environments, especially for those with limited access to natural environments.

By promoting positive emotions and nature relatedness, digital nature experiences can contribute to emotional well-being and potentially foster pro-environmental behaviors. This can have implications for promoting sustainable lifestyles and environmental conservation efforts.

This study provides original empirical evidence on the impact of viewing digital environment images on several psychological constructs. It highlights the potential of digital nature experiences as a complement to direct nature exposure, particularly for those with limited access to natural environments. These findings contribute to the growing body of literature on the benefits of digital nature experiences and have implications for various fields, including education, environmental psychology and human–computer interaction.

The purpose of this paper is to explore how and when employee smart technology, artificial intelligence, robotics and algorithms (STARA) awareness affects job crafting through challenge appraisal and threat appraisal and provides positive stress mindset as a moderator.

The survey data was collected from 319 employees in four Chinese companies. The hypotheses were tested using Mplus 7.0 and regression analysis.

The results indicate that STARA awareness positively prompts approach job crafting via challenge appraisal and also positively predicts avoidance job crafting via threat appraisal. Meanwhile, positive stress mindset enhanced the mediating effect of challenge appraisal and weakened the mediating effect of threat appraisal.

Leaders should prioritize hiring high-positive-stress mindset candidates for jobs, and organizations should also cultivate employees’ positive stress mindset.

Building on the cognitive appraisal theory of stress, this study reveals the underlying mechanism and boundary conditions behind the linkage of STARA awareness and job crafting.

Automated social media messaging tactics can undermine trust in health institutions and public health advice. As such, we examine automated software programs (ASPs) and social bots in the Twitter anti-vaccine discourse before and after the release of COVID-19 vaccines.

We compare two Twitter datasets comprising user accounts and associated English-language tweets featuring the keywords “#antivaxx” or “anti-vaxx.” The first dataset, from 2018 (pre-COVID vaccine), includes 3,154 user accounts and 6,380 tweets. The second comprises 327,067 accounts and 545,268 tweets published during the 12 months following December 1, 2020 (post-COVID vaccine). Using Information Laundering Theory (ILT), the datasets were examined manually and through user analytics and machine learning to identify activity, visibility, verification status, vaccine position, and ASP or bot technology use.

The post-COVID vaccine dataset showed an increase in highly probable bot accounts (31.09%) and anti-vaccine accounts. However, both datasets were dominated by pro-vaccine accounts; most highly active (59%) and highly visible (50%) accounts classified as probable bots were pro-vaccine.

This research is the first to compare bot behaviors in the “#antivaxx” discourse before and after the release of COVID-19 vaccines. The prevalence of mostly benevolent probable bot accounts suggests a potential overstatement of the threat posed by anti-vaccine accounts using ASPs or bot technologies. By highlighting bots as intermediaries that disseminate both pro- and anti-vaccine content, we extend ILT by identifying a benevolent variant and offering insights into bots as “pathways” to generating mainstream information.

The purpose of this study is to explore how to select a suitable supply chain collaboration paradigm (SCCP) based on the intelligent manufacturing model (IMM) of enterprises.

Given the fit between internal collaboration and external collaboration, we propose a model to select a suitable SCCP based on two-sided matching between SCCPs and IMMs. In this decision problem, we invited five university scholars and seven related consultants to evaluate SCCPs and IMMs based on the regret theory, which is used to obtain the perceived utility and matching results. The evaluation values are comfortably expressed through probabilistic linguistic term sets (PLTSs). Also, we set the lowest acceptance threshold to improve the accuracy of matching results.

The findings indicate that the characteristics of IMMs can significantly influence the selection of SCCPs, and an SCCP is not suitable for all IMMs. Interestingly, the study findings suggest that the selection of SCCP is diverse and multi-optional under the constraints of IMMs.

Existing studies have explored supply chain collaboration (SCC) in Industry 4.0 to improve supply chain performance, but less attention has been paid to the impact of the match between SCCPs and IMMs on supply chain performance. And even fewer studies have addressed how to select a suitable SCCP in different IMMs. This study provides a unique contribution to the practice of SCC and expands the understanding of supply chain management in Industry 4.0.

Financial firms are looking for better ways to harness the power of data analytics to improve their decision quality in the financial modeling era. This study aims to explore key factors influencing big data analytics-driven financial decision quality which has been given scant attention in the relevant literature.

The authors empirically examined the interrelations between five factors including technology capability, data capability, information quality, data-driven insights and financial decision quality drawing on quantitative data collected from Jordanian financial firms using a cross-sectional questionnaire survey.

The SmartPLS analysis outcomes revealed that both technology capability and data capability have a positive and direct influence on information quality and data-driven insights without any direct influence on financial decision quality. The findings also point to the importance and influence of information quality and data-driven insights on high-quality financial decisions.

The study for the first time enriches the knowledge and relevant literature by exploring the critical factors affecting big data-driven financial decision quality in the financial modeling context.

Cracks are prevalent signs of pavement distress found on highways globally. The use of artificial intelligence (AI) and deep learning (DL) for crack detection is increasingly considered as an optimal solution. Consequently, this paper introduces a novel, fully connected, optimised convolutional neural network (CNN) model using feature selection algorithms for the purpose of detecting cracks in highway pavements.

To enhance the accuracy of the CNN model for crack detection, the authors employed a fully connected deep learning layers CNN model along with several optimisation techniques. Specifically, three optimisation algorithms, namely adaptive moment estimation (ADAM), stochastic gradient descent with momentum (SGDM), and RMSProp, were utilised to fine-tune the CNN model and enhance its overall performance. Subsequently, the authors implemented eight feature selection algorithms to further improve the accuracy of the optimised CNN model. These feature selection techniques were thoughtfully selected and systematically applied to identify the most relevant features contributing to crack detection in the given dataset. Finally, the authors subjected the proposed model to testing against seven pre-trained models.

The study's results show that the accuracy of the three optimisers (ADAM, SGDM, and RMSProp) with the five deep learning layers model is 97.4%, 98.2%, and 96.09%, respectively. Following this, eight feature selection algorithms were applied to the five deep learning layers to enhance accuracy, with particle swarm optimisation (PSO) achieving the highest F-score at 98.72. The model was then compared with other pre-trained models and exhibited the highest performance.

With an achieved precision of 98.19% and F-score of 98.72% using PSO, the developed model is highly accurate and effective in detecting and evaluating the condition of cracks in pavements. As a result, the model has the potential to significantly reduce the effort required for crack detection and evaluation.

The proposed method for enhancing CNN model accuracy in crack detection stands out for its unique combination of optimisation algorithms (ADAM, SGDM, and RMSProp) with systematic application of multiple feature selection techniques to identify relevant crack detection features and comparing results with existing pre-trained models.

This study aims to investigate the information-seeking behaviours of home buyers – primarily owner-occupants – using digital real estate platforms, a key element in the industry’s shift towards digital services. It focuses on first-time buyers and repurchasers to examine how these platforms assist in the home-buying process and influence buyer behaviour in Taiwan.

A mixed methods approach was adopted, combining quantitative surveys and qualitative interviews to gather comprehensive data on user experiences and preferences.

The research identifies brand perception, search functionality and search results as critical factors influencing platform usage. Furthermore, it reveals an increasing demand for innovative artificial intelligence-driven search features to enhance user experience and platform convenience, reflecting evolving user expectations.

By addressing the specific context of Taiwan’s real estate market, this study provides novel insights into the interplay between digital platform features and user behaviour. The findings offer practical recommendations for improving platform design to better align with user needs.