Search results

This study aims to deeply understand customer experiences toward Internet of Things (IoT) applications in retail by developing machine learning models for aspect-based sentiment analysis (SA). It includes creating a related terms dictionary and proposing implications for retail businesses in Vietnam based on these analyses. The ultimate goal is to gain insights into customer opinions and assist administrators in formulating effective digital transformation and business strategies within the Vietnamese market.

Initially, this research uses qualitative methods to identify different aspects of customer experience at stores equipped with IoT applications. Then, quantitative methods were applied through classification machine learning models which were trained on the annotated data set to classify comments into aspects and sentiments. Finally, the classification results were analyzed and visualized to draw implications about customer opinions of these stores.

This study collected 77,042 customers’ comment from potential and actual customers who have ever shopped at retail stores with IoT applications deployed worldwide, identified ten new aspects of customer experience in this field and built a dictionary of related terms. Furthermore, this study contributed two efficient ensemble models with an accuracy of 81% and 89% for analyzing aspects and customer sentiments, respectively. This study also proposes implications for managers regarding the use of IoT technology in retail stores to improve shopping experiences for customers.

This study’s findings help managers develop appropriate digital transformation and business strategies for integrating IoT technology into retail stores, especially for retail businesses in the Vietnamese market based on the analysis results and proposed model.

Amid the increasing water risks faced by firms, external investors are becoming more interested in corporate water disclosure and research on its drivers has become prominent. This paper aims to investigate the impact of water resource tax (WRT) on water disclosure and other related drivers.

This study uses the WRT policy as a quasi-natural experiment and applies the difference-in-differences method.

The results indicate that WRT policy significantly stimulates water disclosure. Improving green innovation and strengthening internal control are potential channels through which WRT works. Moreover, WRT’s effect is more pronounced in firms that face high institutional pressures and have better internal resource support.

The findings suggest that water-sensitive firms should disclose water information to acquire resources from external stakeholders to support their green transition. It also provides implications for governments to incorporate other external forces in shaping the direction and intensity of WRT and consider the resource constraints of small and private firms in green transformation.

This study is of assistance in promoting water environmental protection in areas experiencing water stress and provides an opportunity for external stakeholders (external investors, nongovernmental organizations, governments, consumers, suppliers, communities and media) to advocate the water disclosure of firms with high water risks.

The attempt is novel in the context of considering the water regulation risks and the demands of external stakeholders. It provides new insights into the factors influencing water disclosure from the perspective of political stakeholders.

With the development of Web information systems, steel e-commerce platforms have accumulated a large number of quality objection texts. These texts reflect consumer dissatisfaction with the dimensions, appearance and performance of steel products, providing valuable insights for product improvement and consumer decision-making. Currently, mainstream solutions rely on pre-trained models, but their performance on domain-specific data sets and few-shot data sets is not satisfactory. This paper aims to address these challenges by proposing more effective methods for improving model performance on these specialized data sets.

This paper presents a method on the basis of in-domain pre-training, bidirectional encoder representation from Transformers (BERT) and prompt learning. Specifically, a domain-specific unsupervised data set is introduced into the BERT model for in-domain pre-training, enabling the model to better understand specific language patterns in the steel e-commerce industry, enhancing the model’s generalization capability; the incorporation of prompt learning into the BERT model enhances attention to sentence context, improving classification performance on few-shot data sets.

Through experimental evaluation, this method demonstrates superior performance on the quality objection data set, achieving a Macro-F1 score of 93.32%. Additionally, ablation experiments further validate the significant advantages of in-domain pre-training and prompt learning in enhancing model performance.

This study clearly demonstrates the value of the new method in improving the classification of quality objection texts for steel products. The findings of this study offer practical insights for product improvement in the steel industry and provide new directions for future research on few-shot learning and domain-specific models, with potential applications in other fields.

This study is of great significance in revealing the interactive and coordinated relationship between tourism development and the ecological environment, improving the level of environmental governance in the process of tourism economic development and realising the dual-carbon goal and sustainable development of the tourism industry.

This paper performs slack-based measure (SBM)-data envelopment analysis (DEA) analysis, benchmark regression and threshold effect measurement on a sample of 277 cities in China from 2011 to 2019, to estimate the impact of the digital economy on tourism eco-efficiency and to identify the moderating role of environmental regulation.

China’s tourism eco-efficiency shows a spatio-temporal characteristic of steady growth amid fluctuations. The digital economy has a positive facilitating effect on tourism eco-efficiency, which is non-linear with a single threshold effect (0.631), and when the level of the digital economy exceeds the threshold value, its facilitating effect increases from 0.696 to 0.927. Environmental regulation does not play the role of “the icing on the cake” during the digital economy’s impact on tourism eco-efficiency.

This study for the first time includes the digital economy, eco-efficiency and environmental regulation in the research perspective, and analysed the mechanism of action between the three, expanding the depth of research on the digital economy and environmental regulation in the field of tourism. Meanwhile, based on the development needs of policy specialisation and industrial refinement, this study has strong practical significance when conducted at the city level.

This study aims to investigate the circumstances where the positive influences of leader’s self-confidence are weakened in situations of conflict management (CM) arising due to innovation implementation. Specifically, this study tests the moderating influence of financial slack, internal opposition posturing and performance feedback on the relationship between self-confidence and conflict management styles (CMS). The findings are not complete.

This experimental study was conducted in two stages: In Stage 1, the authors studied moderating effects of financial slack (sound/unsound) and internal opposition posture (encouraging/ discouraging) on the relationship between leader’s self-confidence and CMS, namely, dominating, integrating and avoiding. In Stage 2, the authors studied the moderating effect of performance feedback (success/failure) on the relationship between leader’s self-confidence and three CMS. Data were collected from 268 senior-level Indian managers in Stage 1, out of which 235 managers also responded in Stage 2. These participants assumed the positions of chief executive officers (CEOs) within major Indian manufacturer specializing in electrical components, enabling us to empirically test the proposed model. Furthermore, a qualitative analysis of 10 semistructured interviews with Indian CEOs were conducted to enrich the discussion of the results.

Leader’s self-confidence determined the three CMS with highly self-confident leaders displaying irrational behavior in persisting with innovation through dominating style. Internal opposition posture and performance feedback exerted main effects on dominating style.

This study supports behavioral decision theory of firm and escalation of commitment theory.

This study underscores the need for personality and self-awareness training amongst senior managers to mitigate irrational behavior due to excessive self-confidence and enhanced effective CM.

This study identifies a crucial boundary condition where high self-confidence in innovation implementation may lead to irrational behavior and ineffective CM.

While prosocial motives are found to be conducive to unique manager behaviors, the literature lacks empirical evidence on the relationships between prosocial motives and managers’ willingness to engage in environmental and social innovation (ESI) in business-to-business (B2B) firms and the boundary conditions that shape these relationships. This research endeavors to unlock the potential of prosocial motives in fostering ESI, while investigating the mediating roles of creativity-relevant skills and the moderating influence of business moral values.

Using a quantitative survey approach, we gathered insights from 242 managers within Australia’s food and beverage industry. The authors tested their hypotheses by adopting a covariance-based structural equation modeling approach.

First, prosocial motives drive the ESI behaviors of managers in B2B firms. Second, creativity-relevant skills act as the critical bridge connecting prosocial motives with ESI within the realm of B2B firms. Third, business moral values emerge as potent moderating, positively influencing the relationship between prosocial motives and “managers’ willingness to engage in social innovation” within B2B firms.

To the best of the authors’ knowledge, this is the first study examining the relationship between prosocial motives and social and environmental innovation and how this relationship is influenced by creative-relevant skills and business moral values.

Based on the moral licensing theory, this study aims to reveal the mechanism of self-sacrificial leadership inducing abusive supervision from two paths of leader moral credit and leader moral credential. At the same time, it also discusses the moderating effect of leader behavioral integrity on the two paths.

In this study, 434 employees and their direct leaders from six Chinese companies were investigated in a paired survey at three time points, and the empirical data was analyzed using Mplus 7.4 software.

Self-sacrificial leadership has a positive effect on leader abusive supervision through the mediating role of leader moral credit and leader moral credential. In addition, this study also finds that leader behavioral integrity is the “gate” for self-sacrificial leadership to promote abusive supervision, and the leader behavioral integrity has a moderating effect on the process of self-sacrificial leadership influencing on leader moral credit and leader moral credential.

This study explores the evolution of self-sacrificial leadership from “good” to “bad” from the perspective of moral licensing and broadens the research on the mechanism and boundary conditions of self-sacrificial leadership. At the same time, it also provides important reference value for preventing the negative effects of self-sacrificial leadership in organizations.

This study aims to understand the mechanism by which the value of ride-sharing services influences consumers’ continuance intention.

The authors collected data from 484 Chinese ride-sharing respondents and analyzed them using partial least squares structural equation modeling.

The results show that hedonic value, social connection value and environmental value positively affect consumers’ cognitive fit and emotional fit, while utilitarian value has no significant effect on either cognitive fit or emotional fit. In addition, both cognitive fit and emotional fit significantly affect consumers’ satisfaction and continuance intention. Furthermore, satisfaction mediates the effects of cognitive and emotional fit on continuance intention.

Ride-sharing practitioners should have a clear understanding of all the value dimensions of ride-sharing services, which would subsequently increase customers’ continuance intention.

This study defines and divides the dimensions of ride-sharing value and demonstrates the significant impact of environmental value on the sustainability of ride-sharing services. This study extends fit theory by dividing it into two dimensions.

Purpose: This piece delves into the transformative potential of artificial intelligence (AI) in the healthcare field within the emerging realm of Industry 5.0, highlighting a people-focused and eco-friendly approach.

Need for the study: While Industry 4.0 set the foundation for digitization in healthcare, it frequently overlooked the human factor and concerns about sustainability. Industry 5.0 tackles these deficiencies by giving importance to human welfare, efficiency in resource usage, and societal consequences alongside technological progress.

Methodology: This research utilizes a survey of existing written works on Industry 5.0, AI in healthcare, and associated empowering technologies. It also leans on insights from recent investigations and business actions to pinpoint current patterns and future paths.

Findings: This chapter showcases how AI-driven solutions can greatly alter various facets of healthcare. Some of these healthcare facets encompass personalized medicine and treatment, intelligent diagnostics and decision support, robot-supported surgery and care, and enhanced availability and affordability.

Practical applications: This piece offers valuable perspectives for healthcare investors. These investors cover healthcare suppliers, technology creators, rule creators, and patients. By embracing the standards of Industry 5.0, the merging of AI into healthcare brings significant potential for crafting a more competent, sustainable, and people-centered healthcare network that benefits both patients and society as a complete unit. This research investigates the stance, viewpoints, and potential impacts of machine intelligence (MI) in health with an emphasis on Industry 5.0.

The purpose of this study is to examine the impact of customers' environmental concern on corporate green innovation and its underlying mechanisms.

This study empirically examines the impact of customer environmental concern on corporate green innovation using 967 company-customer-year observations of Chinese A-share listed companies over the period 2012–2022.

The empirical results show that customer environmental concern significantly enhances corporate green innovation. Furthermore, executive environmental awareness and research and development (R&D) investment play a partial mediating role in this relationship. The heterogeneity analysis reveals that state-owned customers, customers located in the same province with the corporate and the intellectual property model cities contribute to strengthening this relationship. Moreover, corporate performance analysis shows that customer environmental concern can significantly increase corporate financial performance and sustainable performance.

This study innovatively proposes a measure of customer environmental concern and examines its impact on corporate green innovation and its underlying mechanisms. In addition, this study also proposes some insights for policymakers.