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The development of regional public brands for agricultural products necessitates compelling narratives that resonate deeply with consumers. Given the distinctiveness of agricultural products, consumers prioritize the inherent connection to roots and heritage when making purchasing decisions. Therefore, crafting brand narratives must emphasize this root appeal, namely, consumers’ information appeal preference, to positively influence consumers’ brand perceptions and underscore the value of regional public brands. This study investigates this phenomenon through the lens of cue utilization theory.

Four experiments were conducted for this purpose. Study 1 examined the stimulus materials for brand story type (typical vs atypical). The purpose of study 2 was to verify whether the experimental material could be used to categorize participants' information appeal preferences (geographic vs cultural). Study 3 employed a between-subjects design with a 2 (brand story: typical vs atypical) × 2 (consumers’ information appeal preferences: cultural vs geographic) factorial design. Study 4 used a between-subjects design of 2 (brand story: typical and atypical) × 2 (consumers’ information appeal preferences: cultural vs geographic) × 2 (culturally derived power perception: individual and social).

The findings indicated that the type of brand story and consumers’ information appeal preferences interact with consumers’ brand attitudes toward regional public brands for agricultural products. In addition, a sense of place was found to mediate the interaction between the type of brand story and consumers’ information appeal preferences. Furthermore, culturally derived power perceptions moderated this mechanism.

This study offers valuable insights into marketing regional public brands for agricultural products by categorizing their brand stories into typical and non-typical narratives.

Following the rapid expansion of data volume, velocity and variety, techniques and technologies, big data analytics have achieved substantial development and a surge of companies make investments in big data. Academics and practitioners have been considering the mechanism through which big data analytics capabilities can transform into their improved organizational performance. This paper aims to examine how big data analytics capabilities influence organizational performance through the mediating role of dual innovations.

Drawing on the resource-based view and recent literature on big data analytics, this paper aims to examine the direct effects of big data analytics capabilities (BDAC) on organizational performance, as well as the mediating role of dual innovations on the relationship between (BDAC) and organizational performance. The study extends existing research by making a distinction of BDACs' effect on their outcomes and proposing that BDACs help organizations to generate insights that can help strengthen their dual innovations, which in turn have a positive impact on organizational performance. To test our proposed research model, this study conducts empirical analysis based on questionnaire-base survey data collected from 309 respondents working in Chinese manufacturing firms.

The results support the proposed hypotheses regarding the direct and indirect effect that BDACs have on organizational performance. Specifically, this paper finds that dual innovations positively mediate BDACs' effect on organizational performance.

The conclusions on the relationship between big data analytics capabilities and organizational performance in previous research are controversial due to lack of theoretical foundation and empirical testing. This study resolves the issue by provides empirical analysis, which makes the research conclusions more scientific and credible. In addition, previous literature mainly focused on BDACs' direct impact on organizational performance without making a distinction of BDAC's three dimensions. This study contributes to the literature by thoroughly introducing the notions of BDAC's three core constituents and fully analyzing their relationships with organizational performance. What's more, empirical research on the mechanism of big data analytics' influence on organizational performance is still at a rudimentary stage. The authors address this critical gap by exploring the mediation of dual innovations in the relationship through survey-based research. The research conclusions of this paper provide new perspective for understanding the impact of big data analytics capabilities on organizational performance, and enrich the theoretical research connotation of big data analysis capabilities and dual innovation behavior.

This study aims to focus on the dynamics in influencer-consumer relationships to understand how Generation Z consumers’ identification and social comparison with influencers shape their response to influencers’ branded posts. Specifically, this study investigates how perceived similarity and wishful identification lead to distinct social comparison mechanisms that affect Generation Z consumers’ self-improvement motives, which, in turn, drive their message engagement, brand attitudes and purchase intentions.

An online survey was conducted with 295 college students who are digital natives and whose purchase decisions are heavily influenced by social media influencers.

The study findings confirmed that perceived similarity positively influenced assimilative comparison emotions of optimism, admiration and aspiration while negatively influenced contrastive comparison emotions of envy, depression and resentment. Wishful identification positively affected both assimilative and contrastive comparison emotions. Both types of social comparison emotions further affected consumers’ motivations to follow the influencer for self-improvement, thereby enhancing their brand attitude, purchase intention and engagement behaviors.

This study is one of the earliest attempts to investigate the relationship dynamics between influencers and consumers from the lens of social comparison. The study examines the antecedents of perceived similarity and wishful identification, the mediators of upward comparison emotions and self-improvement motives and the brand evaluation outcomes of message engagement, brand attitude and purchase intention.

This study aims to fill three theoretical gaps in previous literature on exploration and exploitation: the relationship between exploration and exploitation is inconclusive; the influences of exploration and exploitation on firm performance are not consistent; and no empirical studies have integrated the antecedents of exploration and exploitation from the different research fields.

The study conducted a meta-analysis to quantitatively synthesize 143 studies with 257 independent samples to understand the relationship between exploration and exploitation and their consequences and antecedents.

The results show that exploration and exploitation are positively correlated with each other, and both of them can boost firm performance. Moreover, firm capabilities, firm size, firm age, competitive intensity, market orientation and entrepreneurial orientation positively influence exploration, and firm resources, firm capabilities, firm size, firm age, market orientation and entrepreneurial orientation positively influence exploitation. Competitive intensity negatively influences exploitation. Surprisingly, market turbulence does not significantly influence exploration or exploitation.

The results not only contribute to the theories by reconciling the inconsistent results but also provide insight for firms with guidance about under what conditions they should use what strategies.

Optimized plating conditions, included proper designs of insulating shield (IS), auxiliary cathode (AC) and different patterns, contribute to the uniformity enhancement of copper deposition.

Plating experiments were implemented in vertical continuous plating (VCP) line for manufacturing in different conditions. Multiphysics coupling simulation was brought to investigate and predict the plating uniformity improvement of copper pattern. In addition, the numerical model was based on VCP to approach the practical application.

With disproportionate current distribution, different plating pattern design formed diverse copper thickness distribution (CTD). IS and AC improved plating uniformity of copper pattern because of current redistribution. Moreover, optimized plating condition for effectively depositing more uniformed plating copper layer in varied pattern designs were derived by simulation and verified by plating experiment.

The comparison between experiment and simulation revealed that multiphysics coupling is an efficient, reliable and of course environment-friendly tool to perform research on the uniformity of pattern plating in manufacturing.

The purpose of this paper is to gain an in-depth understanding into the research progress, hot spots and future trends in smart gripping technology in the field of apparel smart manufacturing.

This work scrutinised the current research status of the five automatic grasping methods for garment fabrics including the pneumatic suction grasping, the electrostatic grasping, the intrusive grasping and the dexterous grasping. Specifically, the principles, characteristics, main devices and the impact on garment production were discussed.

In particular, soft finger of the dexterous grasping method has good flexibility and adaptability in the process of fabric grasping, which provides a new solution for garment production automation. Up to now, the reviewed method in general exhibit good grasping speed, high grasping stability and flat grasping process. However, in the face of complex fabric materials which are thin and flexible and do not return their original shapes when deformed in practical applications, the gripper for automatic fabric grasping need new technological breakthroughs in the positioning accuracy, grab efficiency and flexible grasping.

The outcomes offered an overview of the research status and future trends of the automatic grasping methods for garment fabrics in the field of apparel intelligent manufacturing. It could not only provide scholars with convenience in identifying research hot spots and building potential cooperation in the follow-up research but also assist beginners in searching core scholars and literature of great significance.

To reduce the difficulty of the sewing process and promote the automation process of fabric sewing, a soft finger-assisted feeding method is proposed to investigate the effect of sewing process parameters on the quality of automatic sewing.

Taking cotton woven fabrics as an example, the causes of sewing deviation are firstly investigated from three aspects: fabric properties, sewing speed and sewing edge position. By simulating the sewing action of human hands, the method of reducing sewing deviation by using soft fingers to press and feed the fabric is proposed. Then, four sewing process factors, namely, robot arm end pressure, sewing machine speed, sewing needle gauge and stitch density, were selected, and three levels were set for each factor to design orthogonal sewing experiments. The sewing deviation of 1# sample under different sewing processes was measured, and the optimal parameter matching for automatic sewing of this specimen was derived.

The findings demonstrate that, while sewing cloth automatically, the sewing deviation is significantly influenced by the robotic arm's end pressure, sewing speed, and stitch density, whereas the sewing deviation is not significantly impacted by the needle number.

The findings offer fundamental information for the development of an automated sewing procedure using soft fingers, which has theoretical and real-world application value to speed up the intelligent modernization and transformation of the apparel industry.

This paper addresses the limitations of current graph neural network-based recommendation systems, which often neglect the integration of side information and the modeling of complex high-order interactions among nodes. The research motivation stems from the need to enhance recommendation performance by effectively utilizing all available data. We propose a novel method called MSHCN, which leverages hypergraph neural networks to integrate side information and model complex interactions, thereby improving user and item representations.

The MSHCN method employs a hypergraph structure to incorporate various types of side information, including social relationships among users and item attributes, which are essential for enriching user and item representations. The k-means clustering algorithm is utilized to create item-associated hypergraphs, while sentiment analysis on user reviews refines the modeling of user interests. Additionally, hypergraphs are constructed for user-user and item-item interactions based on interaction similarity. MSHCN also incorporates contrastive learning as an auxiliary task to enhance the representation learning process.

Extensive experiments demonstrate that MSHCN significantly outperforms existing recommendation models, particularly in its ability to capture and utilize side information and high-order interactions. This results in superior user and item representations and improved recommendation performance.

The novelty of MSHCN lies in its use of a hypergraph structure to integrate diverse side information and model intricate high-order interactions. The incorporation of contrastive learning as an auxiliary task sets it apart from other hypergraph-based models, providing a significant enhancement in recommendation accuracy.

Drawing on the social cognitive theory, this study investigated the effect of mentors' drinking norms on their protégés' alcohol misuse by focusing on the mediating role of conformity drinking motives and the moderating role of moral disengagement. We conducted a three-wave survey of 148 mentor–protégé dyads and found that mentors' drinking norms were positively related to their protégés' alcohol misuse and that this relationship was fully mediated by conformity drinking motives. Moreover, the moderated mediation model revealed that moral engagement strengthens the main effects of mentors' drinking norms on conformity drinking motives and the indirect effects of mentors' drinking norms on protégés' alcohol misuse via enhanced conformity drinking motives. The theoretical and practical implications are discussed.

Short-form video advertisements have recently gained popularity and are widely used. However, creating attractive short video advertisements remains a challenge for sellers. Based on the visual-audio perspective and signaling theory, this study investigated the impacts of three visual features (number of shots, pixel-level image complexity and vertical versus horizontal formats) and two audio features (speech rate and average spectral centroid) on user engagement behavior.

We conducted a field study on TikTok. To test our various hypotheses, we used regression analysis on 2,511 videos containing product promotion information posted by 60 sellers between January 1, 2020 and November 20, 2021.

For visual variables, the number of shots and pixel-level image complexity were found to have nonlinear (inverted U-shaped) relationships with user engagement behavior. The vertical video form was found to have a positive effect on comments and shares. In the case of audio variables, speech rate was found to have a significant positive effect on shares but not on likes and comments. The average spectral centroid was found to have significant negative influences on likes and comments.

This study provides specific suggestions for sellers who create short-form videos to improve user engagement behavior.

This study contributes to the literature on short-form video advertising by extending the potential drivers of user engagement behavior. Additionally, from a methodological perspective, it contributes to the literature by using computer vision and speech-processing techniques to analyze user behavior in a video-related context, effectively overcoming the limitations of the widely adopted survey method.