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Artificial intelligence (AI) customer service has grown rapidly in recent years due to the emergence of COVID-19 and the growth of the e-commerce industry. Therefore, this study employs the integration of the stimuli–organism–response (SOR) and the task-technology fit (TTF) frameworks to understand the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia.

The study utilised a survey-based research approach to investigate the factors that affect individuals’ intentions towards AI customer service adoption in Malaysia. The data were collected by conducting an online survey targeting individuals aged 18 or above who had prior customer service interaction experience with human service agents but had not yet adopted AI customer service. A sample of 339 respondents was used to evaluate the hypotheses, adopting partial least squares structural equation modelling as a symmetric analytic technique.

The PLS-SEM analysis revealed that social influence and anthropomorphism have a positive direct relationship with emotional trust. Furthermore, communicative competence, technology characteristics and perceived intelligence were positively correlated with TTF. Moreover, emotional trust significantly impacts AI customer service adoption. In addition, AI readiness positively moderates the association between task technology fit and AI customer service adoption.

The study provides insights to individuals, organisations, the government and educational institutions to improve the features of AI customer service and its development in Malaysia.

The originality of this study is found in its adoption of the SOR theory and TTF to understand the factors affecting AI customer service adoption. Additionally, it incorporates moderating variables during the analysis, adding depth to the findings. This approach introduces a new perspective on the factors that impact the adoption of AI customer service and offers valuable insights for practitioners seeking to formulate effective strategies to promote its adoption.

This study aims to investigate how chatbots’ warmth and competence affect customer behavioural expectation (i.e. purchase, recommendation) through perceived humanness and perceived persuasiveness. Moreover, prior knowledge of chatbot is considered the boundary condition of the effects of chatbots’ warmth and competence.

A lab-in-field experiment with 213 participants and a scenario-based experiment of 186 participants were used to test the model using partial least squares structural equation modelling via SmartPLS 4.

Chatbot warmth positively affects customer behavioural expectation through perceived humanness while chatbot competence positively affects customer behavioural expectation through perceived persuasiveness. Prior knowledge of chatbot positively moderates the effect of chatbot warmth on perceived humanness.

This study provides nuanced insights into the effects of chatbots’ warmth and competence on customer behavioural expectation. Future studies could extend the model by exploring additional boundary conditions of the effects of chatbots’ warmth and competence in different generations.

This study offers insightful suggestions for marketing managers on how to impress and convert online customers through designing verbal scripts in customer−chatbot conversations that encourage the customers to anthropomorphise the chatbots.

This study probes into the effects of chatbots’ warmth and competence on customer behavioural expectation by proposing and examining a novel research model that incorporates perceived humanness and perceived persuasiveness as the explanatory mechanisms and prior knowledge of chatbot as the boundary condition.

This study aims to examine the interaction effects of chatbots’ language style and customers’ decision-making journey stage on customer’s service encounter satisfaction and the mediating role of customer perception of emotional support and informational support using the construal level theory and social support theory as conceptual frameworks.

This study used a scenario-based experiment with a 2 (chatbot’s language style: abstract language vs concrete language) × 2 (decision-making journey stage: informational stage vs transactional stage) between-subjects design.

Findings show that during the informational stage, chatbots that use abstract language style exert a strong influence on service encounter satisfaction through emotional support. During the transactional stage, chatbots that use concrete language style exert a strong impact on service encounter satisfaction through informational support.

Findings provide some suggestions for improving customer–chatbot interaction quality during online service encounters.

This study offers a novel perspective on customer interaction experience with chatbots by investigating the chatbot’s language styles at different decision-making journey stages.

The investigation of pro-environmental behaviors (PEB) among college students is essential for future sustainability endeavors. Existing research seldomly concentrated on college students and their PEB. This study aims to address the gap in understanding PEB among college students.

This study constructed an integrated model combining the theory of planned behavior (TPB) and the value-belief-norm (VBN) theory, with the novel addition of environmental risk perception. Through an empirical study involving 844 college students, this research analyzed the data with the structural model.

The authors identified that environmental values, attitudes, perceived behavioral control, subjective norms and risk perception play crucial roles in shaping PEB. This study also revealed age-related differences, highlighting that older students might be less influenced by attitudes and subjective norms due to more established habits. Findings underscore the importance of fostering PEB through environmental education, promotion of low-carbon lifestyle choices and incentives. This investigation not only enriches the theoretical framework for PEB but also offers practical insights for policymakers and educators to enhance sustainable practices among the youth.

Though the authors offer valuable findings, this research has two key limitations: the use of observational data for hypothesis testing, which weakens causal inference, and the collection of data through questionnaires, which may be biased by social desirability. Respondents of self-report tend to behave in the socially desired ways. Consequently, they usually exaggerate their pro-environmental intention or PEB. To comprehend the influencing aspects more thoroughly, future research should consider incorporating experimental methods and objective data, such as digitalized data.

The findings provide valuable evidence for guiding college students’ PEB, including strengthening environmental education, promoting of low-carbon fashion and providing incentives for PEBs.

First, the authors examine the internal factors influencing PEB among Chinese university students within the “dual-carbon” initiative framework. Second, this research pioneers the use of structural equation modeling to merge TPB and VBN theories, offering a predictive model for university students’ PEB. Third, the authors introduce “environmental risk perception” as a novel variable derived from both TPB and VBN, enhancing the model’s explanatory power.

The commercial sharing service (CSS) represents an emerging business model in which users pay a minor fee to rent a product for a short period of time. Fashion CSSs enable individuals to rent various garments and accessories with the goal of enhancing one’s public image while saving money. Marketers have strived to popularize fashion CSSs, but concerns related to contamination have thwarted their efforts. Based on face consciousness theory, this research examines how consumers’ desire to enhance their public image (i.e. to “gain face”) can attenuate the negative impacts of contamination concerns and thus facilitate fashion CSS usage.

Two scenario-based studies were conducted to collect data. Participants were recruited via online survey platforms in mainland China. The hypotheses were tested by partial least squares (PLS) path modeling and linear regression analysis.

The analysis results revealed a two-stage mediation model. Contamination concerns were found to inhibit consumers’ participation in fashion-sharing by increasing their perceived risk, which further decreased the perceived value of the CSS. However, consumers’ desire to gain face can mitigate the negative (direct and indirect) effects of contamination concerns on CSS usage, facilitating CSS adoption.

Our findings suggest that eliciting consumers’ desire to gain face can promote fashion CSS usage and attenuate the negative impacts of contamination concerns. Moreover, consumers are less risk-averse and less concerned about shared pieces being contaminated when they seek to enhance their face through fashion products. Practical implications for fashion marketers are discussed.

Nurses' turnover intention has become a major issue in developing countries with high power distance cultures. Therefore, the authors attempt to investigate how turnover intention among nurses' can be reduced through paternalistic leadership (PL). The authors further investigate the mediating role of job satisfaction between the associations of benevolent, moral and authoritarian dimensions of PL with turnover intention. Finally, the authors examined perceived organizational support (POS) as a conditional variable between job satisfaction and turnover intention.

The authors collected data from 374 nurses working in public and private hospitals of high power distance culture using a questionnaire-based survey on convenience basis.

Structural equation modeling confirms that benevolent and moral dimensions of PL positively affect nurses' job satisfaction which helps them reduce their turnover intention. While the authoritarian dimension of PL negatively affects job satisfaction to further enhance their turnover intention. In addition, the authors noted POS as a conditional variable to trigger the negative effect of job satisfaction on turnover intention.

The authors used a cross-sectional design to collect responses and ensured the absence of common method variance through Harman's Single factor test.

This study identified the mechanism (job satisfaction and POS) through which benevolent, moral and authoritative dimensions of PL predict turnover intention among nurses working in high power distance culture.

護士有離職意向，在擁有高權力距離文化的發展中國家，已成為一個重大的問題。因此，我們擬探討如何可以透過採用家長式領導、把護士離職的意欲減低，繼而研究工作滿足感，在離職意向與家長式領導中仁慈、道德和獨裁這三個層面的關係中所起的中介作用。最後，我們就組織支持感，作為是工作滿足感與離職意向之間的一個條件變數，進行了研究。

本研究透過採用在便利的基礎上進行的問卷調查，從374名在高權力距離文化的公營和私營醫院內工作的護士取得數據，進行分析。

結構方程模型證實了家長式領導中的仁慈和道德這兩個層面，會對可減低護士離職意欲的工作滿足感，產生積極的影響。家長式領導中的獨裁層面、則會對護士的工作滿足程度產生負面的影響，繼而增強其離職意欲。而且，我們確認了組織支持感是一個會增強工作滿足感與離職意向之間負相聯的條件變數。

我們以橫斷面的設計法來收集回應，並透過採用哈曼 (Harman) 的單因素檢定法，來確保共同方法變異不會存在。

本研究確定了一個 (工作滿足感與組織支持感) 機制，透過這機制，家長式領導中的仁慈、道德和獨裁這三個層面可預測於高權力距離文化工作的護士的離職意向。

The ever-demanding role of employees in the hospitality sector stimulates job crafting. This study examines the relationship between inclusive leadership and job-crafting dimensions under the mediating role of work engagement through the lens of conservation of resources (COR) theory. It also aims to analyse the boundary condition of job autonomy between inclusive leadership and work engagement.

The data were collected in 3-time lags from 319 front-line workers in the hospitality sector. The adopted and adapted questionnaires were executed through a deductive approach and an applied research method. The data were analysed through SmartPLS by applying the structural equation modelling (SEM) technique.

This study provides evidence for a predictive relationship between inclusive leadership and job-crafting dimensions under the mediating psychological mechanism of work engagement. Additionally, the moderating role of job autonomy is established in the unique context of the hospitality sector of an underdeveloped country, Pakistan.

Services-based organisations need to endure the inclusive leadership style by establishing work engagement practices. Engaged employees result in better job-crafting behaviours through better training and subsequent performance.

This study established that work engagement and job autonomy are imperative forces that impact the relationship between inclusive leadership and job-crafting dimensions. The research study has time-lagged data and conveys meaningful theoretical and practical implications.

This paper elucidates the role of anger in error management (EM) and organizational learning behaviors. The study explores how anger can catalyze learning, emphasizing its strategic implications.

A double-layered moderated-mediated model was developed and tested using data from 744 Chinese CEOs. The psychometric properties of the survey instrument were rigorously examined through structural equation modeling, and hypotheses were tested using Hayes's PROCESS macros.

The findings reveal that anger is a precursor for recognizing the value of significant errors, leading to a positive association with learning behavior among top management team members. Additionally, the study uncovers a triple interaction effect of anger, EM culture and supply chain disruptions on the value of learning from errors. Extensive experience and positive grieving strengthen the relationship between recognizing value from errors and learning behavior.

This study uniquely integrates affect-cognitive theory and organizational learning theory, examining anger in EM and learning. The authors provide empirical evidence that anger can drive error value recognition and learning. The authors incorporate a more fine-grained approach to leadership when including executive anger as a trigger to learning behavior. Factors like experience and positive grieving are explored, deepening the understanding of emotions in learning. The authors consider both negative and positive emotions to contribute to the complexity of organizational learning.

Fine-tuning pre-trained language models (PLMs), e.g. SciBERT, generally require large numbers of annotated data to achieve state-of-the-art performance on a range of NLP tasks in the scientific domain. However, obtaining fine-tuning data for scientific NLP tasks is still challenging and expensive. In this paper, the authors propose the mix prompt tuning (MPT), which is a semi-supervised method aiming to alleviate the dependence on annotated data and improve the performance of multi-granularity academic function recognition tasks.

Specifically, the proposed method provides multi-perspective representations by combining manually designed prompt templates with automatically learned continuous prompt templates to help the given academic function recognition task take full advantage of knowledge in PLMs. Based on these prompt templates and the fine-tuned PLM, a large number of pseudo labels are assigned to the unlabelled examples. Finally, the authors further fine-tune the PLM using the pseudo training set. The authors evaluate the method on three academic function recognition tasks of different granularity including the citation function, the abstract sentence function and the keyword function, with data sets from the computer science domain and the biomedical domain.

Extensive experiments demonstrate the effectiveness of the method and statistically significant improvements against strong baselines. In particular, it achieves an average increase of 5% in Macro-F1 score compared with fine-tuning, and 6% in Macro-F1 score compared with other semi-supervised methods under low-resource settings.

In addition, MPT is a general method that can be easily applied to other low-resource scientific classification tasks.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.