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Studies have shown that customer orientation has a substantial impact on a business's success. This study examines the effects of positive personality on salespeople's proactive customer orientation (PCO) and responsive customer orientation (RCO) by incorporating the effects of job enthusiasm and transformational leadership.

A questionnaire survey is conducted. A total of 511 questionnaires are received from Taiwan's life insurance salespeople. Partial least squares (PLS) regression is used to test the hypotheses.

The results show that positive personality influences PCO and RCO both directly and indirectly through job enthusiasm. The effect of transformational leadership is also found to be significant. Financial service companies should be concerned about the important role of positive personality and transformational leadership in promoting job enthusiasm, PCO and RCO among salespeople.

Previous studies mostly focused on the direct relationship between customer orientation and organizational outcomes, neglecting the role of individual personality. This gap leaves us wondering how a positive personality influences a salesperson's proactive and responsive customer orientation. To the authors' knowledge, this is the first study to examine the mechanisms of a positive personality, job enthusiasm, and transformational leadership on salespeople's PCO and RCO.

Rotating machinery is a crucial component of large equipment, and detecting faults in it accurately is critical for reliable operation. Although fault diagnosis methods based on deep learning have been significantly developed, the existing methods model spatial and temporal features separately and then weigh them, resulting in the decoupling of spatiotemporal features.

The authors propose a spatiotemporal long short-term memory (ST-LSTM) method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

Through these two experiments, the authors demonstrate that machine learning methods still have advantages on small-scale data sets, but our proposed method exhibits a significant advantage due to the simultaneous modeling of the time domain and space domain. These results indicate the potential of the interactive spatiotemporal modeling method for fault diagnosis of rotating machinery.

The authors propose a ST-LSTM method for fault diagnosis of rotating machinery. The authors collected vibration signals from real rolling bearing and gearing test rigs for verification.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

The research aims to develop an assessment framework that evaluates critical success factors (CSFs) for the Quality 4.0 (Q 4.0) transition among Indian firms.

The authors use the fuzzy-Delphi method to validate the results of a systematic literature review (SLR) that explores critical aspects. Further, the fuzzy decision-making trial and laboratory (DEMATEL) method determines the cause-and-effect link. The findings indicate that developing a Q 4.0 framework is essential for the long-term success of manufacturing companies. Utilizing the power of digital technology, data analytics and automation, manufacturing companies can benefit from the Q 4.0 framework. Product quality, operational effectiveness and overall business performance may all be enhanced by implementing the Q 4.0 transition framework.

The study highlights significant awareness of Q 4.0 in the Indian manufacturing sector that is acquired through various means such as training, experience, learning and research. However, most manufacturing industries in India still follow older quality paradigms. On the other hand, Indian manufacturing industries seem well-equipped to adopt Q 4.0, given practitioners' firm grasp of its concepts and anticipated benefits, including improved customer satisfaction, product refinement, continuous process enhancement, waste reduction and informed decision-making. Adoption hurdles involve challenges including reliable electricity access, high-speed Internet, infrastructure, a skilled workforce and financial support. The study also introduces a transition framework facilitating the shift from conventional methods to Q 4.0, aligned with the principles of the Fourth Industrial Revolution (IR).

This research exclusively examines the manufacturing sector, neglecting other fields such as medical, service, mining and construction. Additionally, there needs to be more emphasis on the Q 4.0 implementation frameworks within the scope of the study.

This may be the inaugural framework for transitioning to Q 4.0 in India's manufacturing sectors and, conceivably, other developing nations.

This research proposes and examines a theoretical model grounded in anthropomorphism theory considering the curvilinear and linear relationships between service robot anthropomorphism and consumer usage intention and explores the mediating effect of perceived risk.

To examine the developed model, two complementary studies are designed. In Study 1, multi-time data of 511 participants show that service robot anthropomorphism inverts U-shaped (curvilinear) relationship on consumer usage intention and perceived risk mediates this curvilinear relationship. In Study 2, multi-source data of 460 volunteers are used to confirm the findings of Study 1 and examine that consumer empathy moderates the complex nonlinear effect of service robot anthropomorphism on perceived risk, and the indirect curvilinear effect of service robot anthropomorphism on consumer usage intention through perceived risk.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.

This research provides preliminary and yet important findings on how service robot anthropomorphism most likely is positively associated with consumer usage intention, i.e. the positively influence mechanism of service robot anthropomorphism on consumer usage intention.

This study proposes a novel method utilising a speech-word pair bipartite network to examine the correlation structure between members of parliament (MPs) in the context of the UK- China relationship.

We construct MP-word pair bipartite networks based on the co-occurrence relationship between MPs and words in their speech content. These networks are then mapped into monopartite MPs correlation networks. Additionally, the study calculates correlation network indicators and identifies MP communities and factions to determine the characteristics of MPs and their interrelation in the UK-China relationship. This includes insights into the distribution of key MPs, their correlation structure and the evolution and development trends of MP factions.

Analysis of the parliamentary speeches on China-related affairs in the British Parliament from 2011 to 2020 reveals that the distribution and interrelationship of MPs engaged in UK-China affairs are centralised and discrete, with a few core MPs playing an integral role in the UK-China relationship. Among them, MPs such as Lord Ahmad of Wimbledon, David Cameron, Lord Hunt of Chesterton and Lord Howell of Guildford formed factions with significant differences; however, the continuity of their evolution exhibits unstableness. The core MP factions, such as those led by Lord Ahmad of Wimbledon and David Cameron, have achieved a level of maturity and exert significant influence.

The research has several limitations that warrant acknowledgement. First, we mapped the MP-word pair bipartite network into the MP correlation network for analysis without directly analysing the structure of MPs based on the bipartite network. In future studies, we aim to explore various types of analysis based on the proposed bipartite networks to provide more comprehensive and accurate references for studying UK-China relations. In addition, we seek to incorporate semantic-level analyses, such as sentiment analysis of MPs, into the MP-word -pair bipartite networks for in-depth analysis. Second, the interpretations of MP structures in the UK-China relationship in this study are limited. Consequently, expertise in UK-China relations should be incorporated to enhance the study and provide more practical recommendations.

Firstly, the findings can contribute to an objective understanding of the characteristics and connotations of UK-China relations, thereby informing adjustments of focus accordingly. The identification of the main factions in the UK-China relationship emphasises the imperative for governments to pay greater attention to these MPs’ speeches and social relationships. Secondly, examining the evolution and development of MP factions aids in identifying a country’s diplomatic focus during different periods. This can assist governments in responding promptly to relevant issues and contribute to the formulation of effective foreign policies.

First, this study expands the research methodology of parliamentary debates analysis in previous studies. To the best of our knowledge, we are the first to study the UK-China relationship through the MP-word-pair bipartite network. This outcome inspires future researchers to apply various knowledge networks in the LIS field to elucidate deeper characteristics and connotations of UK-China relations. Second, this study provides a novel perspective for UK-China relationship analysis, which deepens the research object from keywords to MPs. This finding may offer important implications for researchers to further study the role of MPs in the UK-China relationship.

This study proposes a novel scheme for analysing the correlation structure between MPs based on bipartite networks. This approach offers insights into the development and evolving dynamics of MPs.

This study attempts to fill the research gap by extending sustainability literature and providing empirical evidence that considers sustainability marketing commitment (SMC) as a fundamental attribute of effective marketing strategy that consequently improves tourism service quality, as represented by service attractiveness.

In the current study, data was collected from 313 tourism and hospitality firms. To test the model, this study applied structural equation modeling (SEM) to investigate the relationships among environmental strategy, SMC, supplier trust and service attractiveness in a mediation-moderation setting.

The results indicate that the multiple mediation effects of environmental strategy may indirectly influence tourist attractiveness through SMC and tourism services. The two-way moderating effects reveal that supplier trust and socialization strengthen the service attractiveness development process, while three-way interaction discovered that socialization and supplier trust positively moderate the relationships between tourism services and service attractiveness.

Sustainable strategy is a future trend for tourism business management; however, unknown to most is the role of marketing and environmental strategy in tourism business due to lack of integration with concepts in marketing strategy, with the multidimensionality of tourism services, and with the function of trust and socialization, critically undermining analyses of service attractiveness. This paper combines corporate sustainability and sustainability marketing methods to explore how an environmental strategy can improve tourism services and enhance a destination's attractiveness based on a mediation-moderation mechanism.

The environment in which users acquire medical and health information has changed dramatically, with online health communities (OHCs) emerging as an essential means for accessing health information. It is imperative to comprehend the factors that shape the users' compliance willingness (UCW) to health information in OHCs.

This study adopted the information adoption model (IAM) and theory of planned behavior (TPB) to investigate the influence of argument quality (AQ), source credibility (SC) and subjective norms (SN) on UCW while considering the two types of online health information – mature and emerging treatments. The authors conducted an explanatory-predictive study based on a 2 (treatment types: mature vs. emerging) * 2 (AQ: high vs. low) * 2 (SC: high vs. low) scenario-based experiment, using the partial least squares structural equation modeling (PLS-SEM).

SC positively influences AQ. AQ, SC and SN contribute to information usefulness (IU). These factors positively affect UCW through the mediation of IU. SN were found to improve UCW directly. Moreover, the moderating effect of SC on AQ and IU was more substantial for emerging treatments.

The research model integrates IAM and TPB, considering information types as an additional variable. The approach and findings provide a valuable explanation for UCW to health information in OHCs.

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 investigate specific green-brand affect in terms of commitment and connection through the morality–mortality determinants of consumer social responsibility and the assumptions of terror management theory in the proposed three-layered framework. Religiosity serves as a moderator within the framework.

Data are collected in Taipei, Taiwan, while quota sampling is applied, and 420 valid questionnaires are collected. The partial least squares technique is applied for data analysis.

With the contingent role of religiosity, consumer social responsibility influences socially conscious consumption, which in turn drives the commitment and connection of green-brand affect. The death anxiety and self-esteem outlined in terror management theory influence materialism, which then drives green-brand commitment; however, contrary to expectations, they do not drive green-brand connection.

By considering green brands beyond their cognitive aspects and into their affective counterparts, morality–mortality drivers of green-brand commitment and green-grand connection are explored to provide unique contributions so as to better understand socially responsible consumption.