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Technology upgrade has been adopted as a strategy for technology vendors to modify and improve their incumbent technologies. However, user resistance is widespread in practice. In order to understand user technology upgrade behavior, this study integrates the retrospective and prospective sides of actions and proposes an inertia-mindfulness ambidexterity perspective to explore the antecedents of technology upgrade.

An online survey was conducted to collect data from 520 Microsoft Windows users to test this research model. Structural equation modeling (SEM) approach was used to evaluate measurement model and structural model.

Inertia can induce individuals' psychological reactance and thus reduce their intention to upgrade. In contrast, mindfulness can decrease users' psychological reactance and then motivate them to upgrade to a new version of technology. Finally, individuals' dissatisfaction with the current version of technology would weaken the negative impact of psychological reactance on upgrade intention.

This study generates an inertia-mindfulness ambidexterity perspective to investigate the factors that influence user technology upgrade intention from both retrospective and prospective sides and then identifies psychological reactance as underlying mechanism to explain how inertia and mindfulness work. Finally, this study posits that user dissatisfaction with current version of technology can moderate the relationship between psychological reactance and technology upgrade intention.

Sharing co-owned information on social network platforms has become a common and inevitable phenomenon. However, due to the uniqueness of co-owned information, the privacy calculus theory based on a single information owner cannot explain co-owned information disclosure. Therefore, this study tries to investigate the underlying mechanism of users’ co-owned information disclosure from a collective privacy calculus perspective.

Through a survey of 740 participants, covariance-based structural equation modeling (CB-SEM) was used to verify the proposed model and hypotheses.

The results show that personal benefit, others’ benefit and relationship benefit promote users’ co-owned information disclosure by positively affecting personal distributive fairness and others’ distributive fairness perception. Meanwhile, personal privacy risk and others’ privacy risk prevent users’ co-owned information disclosure by negatively affecting personal distributive fairness and others’ distributive fairness perception. Besides, others’ information ownership perception enhances the positive effect of others’ distributive fairness perception on co-owned information disclosure intention. Furthermore, others’ information ownership strengthens the mediating role of others’ distributive fairness.

The findings of this study enrich the research scope of information disclosure and privacy calculus theory and help social network platform developers design collective privacy protection functions.

This study develops a collective privacy calculus model to understand users’ co-owned information disclosure on social network platforms, confirming the mediating role of collective distributive fairness and the moderating role of others’ information ownership perception in the process of collective privacy calculus.

To setup a theoretical model for grasping cutting pieces of garment better, which will help to design a special soft gripper and push forward the automated level of garment manufacturing.

This paper first analyzed the mechanics of the grasping process and concluded the main factors that affect the success of grasping. A theoretical model named grasping fabric model (GFM) was constructed to show the mechanical relationship between the soft gripper and the fabric pieces. Subsequently, two fabric samples were selected and tested for their friction properties and critical buckling force, and the test data were substituted into the theoretical model GFM to obtain the grasping parameters required for fabric grasping layer by layer.

It was found that (1) the critical buckling force of the fabric is mainly influenced by the bending stiffness and the deformation length of the fabric during grab. (2) The difference between the friction between the soft gripper and the fabric and the friction between the fabric, that is DF1-2, has an important influence on the accuracy of grasping layer-by-layer.

It showed that the grasping parameters provided by GFM enable the two samples to be more effectively separated layer by layer, which verifies that the GFM model is strong enough for the possible application in garment automated production.

The aim of this study is to investigate how team-level leader-member exchange (LMX) and the instrumental use of artificial intelligence (AI) by team members influence team psychological empowerment and information systems development (ISD) team performance.

A survey approach was employed to collect time-lagged, multi-source data for testing the proposed model of this study (N = 514 responses from 88 teams). PROCESS macro was used to analyze the data to generate empirical results.

The results suggest that instrumental AI use indirectly influences ISD team performance by enhancing team psychological empowerment. Additionally, it moderates the effects of team-level LMX on team psychological empowerment and ISD team performance. Furthermore, the results demonstrate that the interaction effect of LMX and instrumental AI use on ISD team performance is mediated by team psychological empowerment.

While research on ISD consistently demonstrates that teams, data, and technology collectively contribute to the success of these projects. What is less known, however, is how the exchange relationship between ISD teams and their leader, as well as technological factors, contribute to ISD projects. This study draws on LMX theory to propose how team-level LMX and the instrumental use of AI by team members influence team psychological empowerment and ISD team performance. The study puts forth a mediated moderation model to develop a set of hypotheses. It offers valuable contributions to AI and LMX, along with implications for ISD team management.

Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.

In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.

The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.

To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.

Misperceptions hinder our ability to effectively respond to health crises such as the COVID-19. We aimed to examine the dynamic influences between information exposure, information trust and misperceptions during the early phase of the COVID-19 pandemic. Specifically, we focused on the relative influence of exposure to COVID-19-related information via social media versus interpersonal offline communication.

The current study conducted a two-wave national survey of US adults in May and June of 2020 with a two-week time interval. A professional polling firm recruited participants, and 911 and 679 respondents participated in the first and the second wave survey, respectively. To test proposed hypotheses, researchers conducted path analyses using AMOS 27.0.

Findings show that individuals exposed to COVID-19-related information via social media are likely to hold increased misperceptions. In contrast, exposure to COVID-19-related information offline did not elicit any effects on misperceptions. The exposure to information on social media was positively associated with trust in that information, which, in turn, contributed to an increase in misperceptions. Furthermore, when examining the effects of misperception, it was found that misperceptions increased the likelihood of individuals being exposed to and having trust in COVID-19-related information on social media. The findings provide valuable insights into the role of social media as a platform where a detrimental cycle thrives, shaping the formation of misperceptions and cultivating a heightened dependence among individuals with elevated misperceptions.

The current study significantly extends the findings of prior research by examining the differential effects of social media and interpersonal communication offline on misperception and by revealing the intricate dynamics between information exposure and misperception by focusing on the role of trust. The findings emphasize the detrimental role of social media in generating a vicious information cycle. That said, seemingly superficial discussions about health crises within a social media environment rich in misinformation can contribute to fueling a self-reinforcing loop, making it challenging to effectively counteract misperceptions.

A digital servitization ecosystem (DSE) is a cooperation model based on the concept of value cocreation. However, capability asymmetry among enterprises can lead to unfair benefit distribution and hinder value cocreation and digital service transformation. This paper aims to investigate the impact of the varying capabilities of enterprises (manufacturers, service providers and digital technology providers) on revenue distribution when these enterprises collaborate on digital servitization transformation. This analysis is performed from an ecosystem perspective to facilitate the stable development of DSEs.

The rise of DSEs has engendered extensive literature, and the distribution of benefits within DSEs is in dire need of new mechanisms to adapt to the new competitive environment. The importance of investment contribution, digital servitization level, digitalization level, risk-taking ability, digital servitization effort level and brand awareness is determined by combining the expert scoring method and the entropy value method to determine different weights for manufacturers, service providers and digital technology providers. The Shapley value is used to design the benefit distribution mechanism for stable cooperation among DSE enterprises, thus providing a more scientific basis for the development of cooperative relationships.

Digital servitization is a collaborative process that involves multienterprise activities, and it is significantly affected by digital servitization level and digitalization level. Moreover, constructing the modified Shapley value benefit distribution mechanism according to the relevant capabilities of digital servitization can promote the stable development of DSEs and value cocreation among members.

The main contributions of this study are as follows: First, it summarizes the stability-influencing factors of DSEs on the basis of empirical and literature research on the demand for enterprise digital servitization capabilities and transformation difficulties, delves deeper into the capability composition and cooperative relationship of DSE members and combines the expert scoring method and the entropy value method to determine the weighting to design the benefit distribution mechanism. Second, it reflects system stability and development by studying the revenue distribution of DSE members, thereby expanding the ecosystem construction and business model transformation of digital servitization in the existing research.

This study provides a comprehensive overview of the research landscape of Kansei engineering (KE) within the domain of emotional clothing design. It explores the pivotal technologies, challenges and potential future directions of KE, offering application methodologies and theoretical underpinnings to support emotional clothing design.

This study briefly introduces KE, outlining its overarching research methodologies and processes. This framework lays the groundwork for advancing research in clothing Kansei. Subsequently, by reviewing literature from both domestic and international sources, this research initially explores the application of KE in the design and evaluation of clothing products as well as the development of intelligent clothing design systems from the vantage point of designers. Second, it investigates the role of KE in the customization of online clothing recommendation systems and the optimization of retail environments, as perceived by consumers. Finally, with the research methodologies of KE as a focal point, this paper discusses the principal challenges and opportunities currently confronting the field of clothing Kansei research.

At present, studies in the domain of clothing KE have achieved partial progress, but there are still some challenges to be solved in the concept, technical methods and area of application. In the future, multimodal and multisensory user Kansei acquisition, multidimensional product deconstruction, artificial intelligence (AI) enabling KE research and clothing sales environment Kansei design will become new development trends.

This study provides significant directions and concepts in the technology, methods and application types of KE, which is helpful to better apply KE to emotional clothing design.

Objectively evaluating the fairness of the algorithm, exploring in specific scenarios combined with scenario characteristics and constructing the algorithm fairness evaluation index system in specific scenarios.

This paper selects marketing scenarios, and in accordance with the idea of “theory construction-scene feature extraction-enterprise practice,” summarizes the definition and standard of fairness, combs the application link process of marketing algorithms and establishes the fairness evaluation index system of marketing equity allocation algorithms. Taking simulated marketing data as an example, the fairness performance of marketing algorithms in some feature areas is measured, and the effectiveness of the evaluation system proposed in this paper is verified.

The study reached the following conclusions: (1) Different fairness evaluation criteria have different emphases, and may produce different results. Therefore, different fairness definitions and standards should be selected in different fields according to the characteristics of the scene. (2) The fairness of the marketing equity distribution algorithm can be measured from three aspects: marketing coverage, marketing intensity and marketing frequency. Specifically, for the fairness of coverage, two standards of equal opportunity and different misjudgment rates are selected, and the standard of group fairness is selected for intensity and frequency. (3) For different characteristic fields, different degrees of fairness restrictions should be imposed, and the interpretation of their calculation results and the means of subsequent intervention should also be different according to the marketing objectives and industry characteristics.

First of all, the fairness sensitivity of different feature fields is different, but this paper does not classify the importance of feature fields. In the future, we can build a classification table of sensitive attributes according to the importance of sensitive attributes to give different evaluation and protection priorities. Second, in this paper, only one set of marketing data simulation data is selected to measure the overall algorithm fairness, after which multiple sets of marketing campaigns can be measured and compared to reflect the long-term performance of marketing algorithm fairness. Third, this paper does not continue to explore interventions and measures to improve algorithmic fairness. Different feature fields should be subject to different degrees of fairness constraints, and therefore their subsequent interventions should be different, which needs to be continued to be explored in future research.

This paper combines the specific features of marketing scenarios and selects appropriate fairness evaluation criteria to build an index system for fairness evaluation of marketing algorithms, which provides a reference for assessing and managing the fairness of marketing algorithms.

Algorithm governance and algorithmic fairness are very important issues in the era of artificial intelligence, and the construction of the algorithmic fairness evaluation index system in marketing scenarios in this paper lays a safe foundation for the application of AI algorithms and technologies in marketing scenarios, provides tools and means of algorithm governance and empowers the promotion of safe, efficient and orderly development of algorithms.

In this paper, firstly, the standards of fairness are comprehensively sorted out, and the difference between different standards and evaluation focuses is clarified, and secondly, focusing on the marketing scenario, combined with its characteristics, key fairness evaluation links are put forward, and different standards are innovatively selected to evaluate the fairness in the process of applying marketing algorithms and to build the corresponding index system, which forms the systematic fairness evaluation tool of marketing algorithms.