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This study examines the inverted U-shaped relationship between a live-streaming seller’s disclosure of two-sided product information and consumers’ trust in the seller and product. It also explores the interaction between these two types of information disclosure and their impact on purchase intention in the live-streaming sales context.

An e-questionnaire survey was conducted in China, followed by multiple regression and structural equation modeling analyses.

The disclosure of both negative and positive product information is positively correlated with consumers’ trust in the seller or product but does not directly affect their purchase intention. Negative information disclosure neither enhances nor diminishes the positive impact of disclosing positive information on consumer trust.

Live-streaming sellers (i.e. retailers or manufacturers) should disclose both positive and negative product information to form consumers’ trust toward them (or products) and enhance sales.

Live-streaming sellers often worry about the negative effects of excessive promotions or disclosure of positive or negative product information. However, these negative effects were not statistically significant.

Since some researchers have found nonlinear effects of two-sided product information in other contexts, this study is the first to focus on the impact of live-streaming sellers’ disclosure of two-sided product information on consumers’ trust in the live-streaming sales context rather than on the information per se.

Considering that low-level general trust may hinder communication, this study aims to detect the factors that can influence general trust between exhibitors and visitors during business-to-business trade fairs.

Based on a literature review and stakeholders’ behavior analysis, a conceptual model of general trust formation between exhibitors and visitors is proposed.

The preconditions of strangers’ general trust patterns mainly include their early experience regarding trust, institutional trust in the environment and trust propensity. Stakeholders’ treatment, trust transfer, on-site restraints, reward and punishment expansion and on-site personnel arrangement may facilitate the formation of general trust between exhibitors and visitors.

This paper is a conceptual article that requires further investigation to verify the main factors that influence general trust and the impact of general trust on other trust components between exhibitors and visitors.

Organizers, exhibitors and visitors should pay attention to participants’ selection, supervision, self-discipline and personnel management before and during trade fairs. International and small-scale, especially new trade fairs in developed and developing countries, must consider additional measures to improve general trust.

The existing literature has not focused on general trust in the trade fair context. In this paper, research on network and relationship marketing is further deepened in terms of a specific trust type. The interactions between stakeholders before and during fair may promote general trust among participants than in other settings, which partially explains why trade fair (even other two-sided markets) can increase social capital.

The purpose of this paper is to utilize the spontaneous brain potentials as an index to quantifying the consumers’ inner emotions, and propose an objective method to obtain the clothing recognitions of consumers by only monitoring brain activities.

Different styles of men’s casual jacket were studied as a case. The research included four phases: first, stimuli samples were constructed by clustering algorithm. Second, self-report for the perception of stimuli samples were recorded by self-assessment manikin. Third, real-time brain potentials while viewing stimuli samples were recorded and analyzed. Finally, the output data were compared with the classical research achievements of visual evoked emotional ERPs to examine the effectiveness.

The results indicated significant difference in main effect of different emotional categories which was identified a corresponding relationship between the emotional trigger and the emotional reaction, of which the early components were the typical components that provided the major physiology characteristics for emotional fashion design. The middle components could be used as the assist reference indexes. The negative stimuli were first noticed because its shorter processing times and larger amplitudes. The comparison confirmed that the proposed method was capable of quantifying cognitive activities of consumers by only monitoring brain activities and then transferred the analyzed data to the design references.

The results quantifying the qualities of consumers’ emotional preference for men’ casual jackets based on the neural mechanism of human brain, which could eliminate the systematic biases associated with the uses of words and semantic comprehension in self-report methods. The proposed method may help to enrich and complete the sentimental fashion design for the cognitive experience of consumer oriented. Moreover, it also could be beneficial to optimize design process and improve efficiency and core competitiveness for clothing producers.

The carbon complementary supply chain (CCSC) is a collaborative framework that facilitates internal carbon credit trading agreements among supply chain agents in compliance with prevailing carbon regulations. Such agreements are highly beneficial, prompting agents to consider joint investment in emission reduction initiatives. However, capital investments come with inevitable opportunity costs, compelling agents to weigh the potential revenue from collaborative investments against these costs. Thus, this paper mainly explores carbon abatement strategies and operational decisions of the CCSC members and the influence of opportunity costs on the strategic choice of cooperative and noncooperative investment.

The authors propose a novel biform game-based theoretical framework that captures the interplay of pricing competition and investment cooperation among CCSC agents and assesses the impact of opportunity costs on CCSC profits and social welfare. Besides, the authors also compare the biform game-based collaborative scenario (Model B) to the noncooperative investment scenario (Model N) to investigate the conditions under which collaborative investment is most effective.

The biform game-based collaborative investment strategy enhances the economic performance of the traditional energy manufacturer, who bears the risk of opportunity costs, as well as the retailer. Additionally, it incentivizes the renewable energy manufacturer to improve environmental performance through renewable projects.

This research contributes significantly by establishing a theoretical framework that integrates the concepts of opportunity costs and biform game theory, offering new insights into the strategic management of carbon emissions within supply chains.

The manufacturing sector is well known for its significance in upholding the economic prosperity of many nations. However, in today's unprecedented environment, the resilience of this sector has become vulnerable to relentless catastrophic events, thus gaining a serious concern among the economies driven by this sector. Albeit the various determinants, human capital emerges as the widely accepted core factor that holds the key to proliferate organisational resilience. Therefore, the present systematic literature review seeks to intensify the understanding of the link between human capital and organisational resilience in the manufacturing context.

This paper systematically reviews the studies converging human capital and organisational resilience in the context of manufacturing from the year 2011 to 2021 based on the PRISMA protocol. A bibliographic coupling analysis was carried out using VOSviewer software to expose the main research themes and trends concerning the relationship.

The bibliographic coupling analysis discovered links between publications to produce a framework outlining a holistic state-of-art of the literature intersecting human capital and organisational resilience. The analysis identified main research themes by clustering the prior studies into seven groups, which describe the direction of the literature.

This study offers a novel framework and in-depth understanding to the research community to delve into the interrelationship between human capital and organisational resilience research. Guided by the gaps in the literature, a set of outstanding avenues for the forthcoming studies are also proposed.

Despite the growing interest in the role of relation-specific investments (RSIs) in superior firm performance, their impact on sustainability performance remains unexplored, as do the underlying mechanisms of such effects. Drawing on the relational view and resource orchestration theory (ROT), the authors propose that supply chain learning (SCL) mediates the link between RSIs and sustainability performance.

A multi-method approach was adopted, combining a case study and survey. An exploratory case study of four Chinese manufacturing firms was first conducted to develop research hypotheses. A quantitative survey of data collected from 269 firms was then undertaken to test hypotheses.

Property-based, knowledge-based and personal-based RSIs positively impact firm sustainability performance and SCL. SCL fully mediates the relationship between knowledge-as well as personal-based RSIs and sustainability performance, and partially mediates the relationship between property-based RSIs and sustainability performance.

The study unveils important practical insights and approaches for firms endeavouring to achieve sustainability performance through RSIs and SCL.

The study extends the RSIs literature by linking RSIs and sustainability performance and differentiating the effects of different types of RSIs on sustainability performance. The theorized underlying mechanism advances the understanding of SCL in the link between RSIs and sustainability performance.

The purpose of this paper is to prepare structural colors of fabrics coated with Silver/Zinc Oxide (Ag/ZnO) composite films by magnetron sputtering and analyze the relationship between the colors and the thickness of Zinc Oxide (ZnO) film in Ag/ZnO composite film and the photocatalytic property of the fabrics coated with Ag/ZnO composite film.

Ag/ZnO composite films deposited on polyester fabrics were prepared by magnetron sputtering technology. The structural colors of textiles coated with Ag/ZnO composite films and the relationship between the colors and Ag/ZnO composite films were analyzed, and the photocatalytic property of Ag/ZnO composite films was also discussed.

The results indicated that the colors varied with the thicknesses of the ZnO film in Ag/ZnO composite films. The reactive sputtering time of ZnO film was 5, 8, 10 and 14 min, respectively, and the colors of the corresponding fabrics were purple, blue, blue-green and yellow. Meanwhile, the polyester fabrics coated with Ag/ZnO composite films showed the excellent photocatalytic properties, and silver (Ag) films deposited under the ZnO films in Ag/ZnO composite films could also improve the photocatalytic activities of ZnO films, and the formaldehyde degradation rates was 77.5%, which was higher than the 69.9% for the fabrics coated only with the ZnO film.

The polyester fabrics coated with Ag/ZnO composite films not only created various structural colors using change the thicknesses of the ZnO film, but also achieved the multifuctionality, which will have a broad application prospect in textile fields.

Based on user-generated content from a Chinese social media platform, this paper aims to investigate multiple methods of constructing user profiles and their effectiveness in predicting their gender, age and geographic location.

This investigation collected 331,634 posts from 4,440 users of Sina Weibo. The data were divided into two parts, for training and testing . First, a vector space model and topic models were applied to construct user profiles. A classification model was then learned by a support vector machine according to the training data set. Finally, we used the classification model to predict users’ gender, age and geographic location in the testing data set.

The results revealed that in constructing user profiles, latent semantic analysis performed better on the task of predicting gender and age. By contrast, the method based on a traditional vector space model worked better in making predictions regarding the geographic location. In the process of applying a topic model to construct user profiles, the authors found that different prediction tasks should use different numbers of topics.

This study explores different user profile construction methods to predict Chinese social media network users’ gender, age and geographic location. The results of this paper will help to improve the quality of personal information gathered from social media platforms, and thereby improve personalized recommendation systems and personalized marketing.

In order to help companies better grasp the perceptual needs of consumers for patterns, so as to carry out more accurate product pattern development and recommendation, this research develops a product pattern design system based on computer-aided design.

First, use the Kansei engineering theory and method to obtain the user's perceptual image, and deconstruct and encode the pattern based on the morphological analysis method, then through the BP neural network to construct the mapping relationship between the user's perceptual image and the pattern design elements, and finally calculate and find the corresponding design code combination according to the design goal to guide the pattern design.

Taking costume paper-cut patterns as an example, the feasibility of this system is verified, the design system can well reflect the user's perceptual image in the pattern design and improve the efficiency of pattern customization service.

Compared with the traditional method that relies on the designer's personal experience to propose a design plan, this research provides scientific and intelligent design methods for product pattern design.

The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing.

A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed.

This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing.

This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.