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This study reduces carbon emission in logistics distribution to realize the low-carbon site optimization for a cold chain logistics distribution center problem.

This study involves cooling, commodity damage and carbon emissions and establishes the site selection model of low-carbon cold chain logistics distribution center aiming at minimizing total cost, and grey wolf optimization algorithm is used to improve the artificial fish swarm algorithm to solve a cold chain logistics distribution center problem.

The optimization results and stability of the improved algorithm are significantly improved and compared with other intelligent algorithms. The result is confirmed to use the Beijing-Tianjin-Hebei region site selection. This study reduces composite cost of cold chain logistics and reduces damage to environment to provide a new idea for developing cold chain logistics.

This study contributes to propose an optimization model of low-carbon cold chain logistics site by considering various factors affecting cold chain products and converting carbon emissions into costs. Prior studies are lacking to take carbon emissions into account in the logistics process. The main trend of current economic development is low-carbon and the logistics distribution is an energy consumption and high carbon emissions.

This study aims to develop a measurement scale for employee aesthetic labor (AL) in hospitality from the perspectives of frontline employees of international tourist hotels and airlines.

The authors utilized both qualitative and quantitative methods to develop the AL scale. Participants were frontline employees of international tourist hotels and airlines in Taiwan. The authors’ analysis incorporated both exploratory and confirmatory factor analyses to examine the results.

A four-factor, 21-item hospitality AL scale with satisfactory validity and reliability was created. The four AL factors are appropriate voice and response, pleasant appearance, corporate aesthetic image delivery and polite and elegant demeanor.

The developed AL scale can serve as a useful tool for the hospitality industry in terms of employee recruitment and training to align with the corporation's aesthetic image and reduce the potential burden of AL on employees.

Based on dramaturgical theory, this study focuses on the AL practices that are performed during service encounters with customers. It is potentially the first AL scale to be constructed using rigorous scale development procedures.

This study aims to investigate whether gender diversity in audit committees affects the purchase of nonaudit services in China. Results from family and nonfamily firms are compared and the critical mass participation of females are further examined.

The sample comprises 1,834 Chinese listed companies from 2012 to 2021, among which 910 are family firms. The Heckman (1979) two-stage model is used to mitigate the potential endogeneity issue in the selection of gender diversity. Propensity score matching is also used to further alleviate the endogeneity problem in relation to family firms.

Results show a significant and negative correlation between the gender diversity in audit committees and nonaudit service fees. This association is more apparent in nonfamily than in family firms. Findings are consistent and robust to endogeneity tests and sensitivity analyses. The analysis of critical mass and symbolic participation shows that three female directors can more significantly restrain nonaudit fees than one to two females on the board.

This study contributes to literature on resource dependence theory, which posits that audit committees help enterprises establish contact with auditors, improve the company legitimacy, assist in communication and provide relevant expertise. This study also relates to agency theory, which holds that differences in the severity of types I and II agency problems between family and nonfamily firms lead to differences in auditor selection and related costs.

Extending from previous research on the relation between the gender diversity in audit committees and nonaudit fees, the present study delves into this connection within the context of China, an emerging economy. As a result, this investigation offers novel insights and expands upon current knowledge. In addition, the correlation between the gender diversity of audit committees and nonaudit fees is explored for family and nonfamily firms.

The study explores and builds theories in Customer Perceived Values (CPVs) that drive counterfeit buying intention, using a Grounded Theory Approach (GTA) in an emerging market, India.

Counterfeit studies have either resorted to a survey approach or modelling approach in investigating various aspects and dimensions. This study, among a few, attempted a GTA in building theory on CPVs. Based on the observations and recorded responses that emerged through several Focus Group Discussions (FGDs); conducted in two metropolitan cities (India), newer insights into this illicit phenomenon of “Counterfeiting” were derived.

Adding to the counterfeit literature, the study presents a comprehensive view of the CPVs. Findings reveal economic, socio-normative, pleasure-based, euphemistic, acquisition-centrality, self-regulating, situational and sustainable consumption values that influence counterfeit attitudes and in turn impact counterfeit buying intentions. Although Economic Values (ECV) have been the primary motivation for counterfeit purchase, complex and newer values that emerged through this research study bears significance.

As a single point of reference, this study will provide impetus to scholars and academicians in expanding the counterfeit research domain. While aiding policymakers and marketers in further understanding this illicit practice, it will also guide brand managers in strategizing their offerings and reaching out to the masses with strong brand aesthetic values.

Based on a systematic literature review using the 4 Ws framework, this study is one of the few attempts that has adopted a GTA to explore and develop theories on CPVs in counterfeit research.

With the development of social media, live-streaming has become an indispensable marketing activity for firms, especially in China. From the initial cooperation with the influencer, firms begin to create their own live-streaming channel, namely, the brands' self-built live-streaming. The purpose of this study is to explore the process of consumer engagement in the brands' self-built live-streaming.

This research comprises two experimental studies. Study 1 examined the effect of streamer types (CEO vs. celebrity) on consumer engagement. Study 2 investigated the moderating effects of product innovativeness.

Results showed that CEO streamers could enhance consumer engagement by increasing consumers' cognitive trust, and celebrity streamers could enhance consumer engagement by increasing consumers' emotional trust. In addition, consumer engagement was higher for really new products (vs. incremental new products) in CEO streamers' (vs. celebrity streamers') live-streaming.

Compared with previous studies that focused on streamers based on the influencer marketing, this study expands the scope of research on the live-streaming ecosystem by exploring the effect of different streamer types on the brands' self-built live-streaming. By investigating consumer engagement, this study gives implications for the sustainable traffic issue in live-streaming e-commerce.

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.

The purpose of this study is to investigate consumers' emotional responses to artificial intelligence (AI) defeating people. Meanwhile, the authors investigate the negative spillover effect of AI defeating people on consumers' attitudes toward AI companies. The authors also try to alleviate this spillover effect.

Using four studies to test the hypotheses. In Study 1, the authors use the fine-tuned Bidirectional Encoder Representations from the Transformers algorithm to run a sentiment analysis to investigate how AI defeating people influences consumers' emotions. In Studies 2 to 4, the authors test the effect of AI defeating people on consumers' attitudes, the mediating effect of negative emotions and the moderating effect of different intentions.

The authors find that AI defeating people increases consumers' negative emotions. In terms of downstream consequences, AI defeating people induces a spillover effect on consumers' unfavorable attitudes toward AI companies. Emphasizing the intention of helping people can effectively mitigate this negative spillover effect.

The authors' findings remind governments, policymakers and AI companies to pay attention to the negative effect of AI defeating people and take reasonable steps to alleviate this negative effect. The authors help consumers rationally understand this phenomenon and correctly control and reduce unnecessary negative emotions in the AI era.

This paper is the first study to examine the adverse effects of AI defeating humans. The authors contribute to research on the dark side of AI, the outcomes of competition matches and the method to analyze emotions in user-generated content (UGC).

Customer referral programs (CRPs) are popular; however, they often generate low referral rates. The authors propose that certain CRP referral tasks may hinder consumers’ referral likelihood. This study aims to explore the effects of referral tasks (communication content and approach) on customers’ referral likelihood on social platforms and the role of self-construal.

This study establishes a theoretical model based on online social platforms and conducts three scenario-based experiments. The authors obtain data from consumers on Sojump platform and test the hypotheses using analysis of variance (ANOVA) analysis and mediation analysis in SPSS. The valid sample sizes for these three experiments are 288, 203 and 214, respectively.

Three experimental studies indicate that communication content and approach have a significant effect on referral likelihood. Furthermore, the effect of communication content on referral likelihood depends on the communication approach. Self-construal plays a moderating role in the effect of communication content and approach on perceived social costs.

CRPs typically involve tasks and rewards; consumers are asked to complete a referral task and then receive a reward. Both tasks and rewards can affect an individual’s willingness to participate; however, existing studies on CRP focus primarily on the reward component. To the best of the authors’ knowledge, this is the first study to systematically investigate the role of referral tasks (communication content and approach) in CRPs. The authors extend the related research by examining the impact of referral tasks on consumers’ willingness to recommend. In addition, this study introduces self-construal into CRPs research.