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Because of their large field of view, omnistereo vision systems have been widely used as primary vision sensors in autonomous mobile robot tasks. The purpose of this article is to achieve real-time and accurate tracking by the omnidirectional vision robot system.

The authors provide in this study the key techniques required to obtain an accurate omnistereo target tracking and location robot system, including stereo rectification and target tracking in complex environment. A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

The experiments are conducted with all the necessary stages involved in obtaining a high-performance omnistereo vision system. The proposed correction algorithm can process the image in real time. The experimental results of the improved tracking algorithm are better than the original algorithm. The statistical analysis of the experimental results demonstrates the effectiveness of the system.

A simple rectification model is proposed, and a local image processing method is used to reduce the computation time in the localization process. A target tracking method is improved to make it suitable for omnidirectional vision system. Using the proposed methods and some existing methods, an omnistereo target tracking and location system is established.

This paper explores the potential competitive advantages from the development of an internal network of subsidiaries and external network of alliances. Given the broad scope and lack of systematic investigation in prior research, clinical field research was conducted in eleven Japanese subsidiaries in China. Our in‐depth interviews revealed that there are benefits and costs associated with the development of both subsidiary networks and alliance networks. While there are exploitation and exploration benefits from subsidiary network development, internationalizing firms (especially smaller firms) are subject to the liability of foreignness. Alliance network development is an effective way to mitigate this liability if internationalizing firms choose the right alliance strategy.

The paper aims to explore the emotion learning experiences of some Chinese immigrants in Canadian engineering workplaces.

The paper is based on life history style interviews with 14 Chinese immigrant engineers and 14 key informant interviews.

Research respondents constructed a competitive, masculine, and individualistic engineering “culture” in Canada, where the emotional and the relational, supposedly central to the Asian and Chinese culture, are largely devalued. To fit into the individualistic culture, some Chinese immigrants learned to change their patterns of socialization and some became hard‐nosed competitors. To ameliorate the deleterious effects of the alienating workplace environments, some resorted to their cultural teaching to maintain emotional health; some also tried to build collegiality and collectivity to humanize their work settings.

The study was not a comprehensive study of the emotion work in engineering workplaces. It does not capture the emotional order that may as well exist to afford different people differential negotiation power in the engineering culture.

The study suggests that while emotional connection and personalized relationship are “culturally” discouraged in engineering workplaces, they are of significant use value for immigrant workers and should be addressed to create humanized work environments.

This is one of the few articles that examine emotion learning at the intersection of gender, race and class relations.

Celebrity endorsement advertising receives increased attention in the relevant literature. Approaching from the abnormal stock market return perspective, the purpose of this paper is to investigate the potential risks and expected profit associated with celebrity endorsement. The factors that are included in this investigation are the attributes relating to celebrities and the endorsed firms.

Data were collected from over 300 firms that use celebrity endorsements and are listed in two of the biggest stock exchanges in China. The study uses the event study method to analyze the proposed relationships.

Some of the findings in the current study are consistent with or contrast to those in previous research. Specifically, this study finds that the celebrities’ demographics such as age and gender have little influence on financial return of the endorsed firm. However, investors respond rather negatively toward using actor celebrities to endorse a product or a brand, especially for high-tech products. The match-up endorsement has a positive effect on the firm’s abnormal return.

The current study has implications for the relevant literature and practitioners. Very few studies have used stock market return to measure celebrity endorsement effectiveness. This study provides insights into the influence of various factors associating with celebrities and the endorsed firm, extending the celebrity endorsement research into a broader domain. In particular, this study has practical implications for firms that have used or intend to use actor celebrity endorsement.

This study is the first to use event study method to comprehensively analyze influence of attributes relating to both the celebrities and the endorsed firms in China on stock market return.

The purpose of this study is to use the grounded theory to summarize the types of celebrity persona and to construct a theoretical model for celebrity persona on consumer purchase intention. Based on the study results, it provides better suggestions for merchants and live streamers and is an expansion of previous research on live-streaming e-commerce.

The grounded theory is recognized as the most scientific qualitative research method and is the ideal explorative method for generating theory. First, the participants were interviewed, and interview data were collected. Then the interview data were organized and analyzed. Finally, this paper summarizes the types of celebrity persona and constructes a theoretical model framework of celebrity persona on consumers' purchase intention.

The results show that the celebrity live streamer persona can be divided into two types: personalized persona and professional persona. Through emotional attachment, the celebrity's persona affects the consumer's purchase intentions. As well as, product type plays a moderating role between celebrity persona and consumer purchase intentions.

The contribution of this research is to start from the celebrity persona, link the celebrity persona with the consumer purchase intentions and expand the research scope of the celebrity persona. It opens the “black box” of the heterogeneity of celebrity live streamers' characteristics on consumer purchase intentions.

This study aims to assess the effectiveness of different AI models in accurately aggregating information about the protein quality (PQ) content of food items using four artificial intelligence (AI) models -– ChatGPT 3.5, ChatGPT 4, Bard AI and Bing Chat.

A total of 22 food items, curated from the Food and Agriculture Organisation (FAO) of the United Nations (UN) report, were input into each model. These items were characterised by their PQ content according to the Digestible Indispensable Amino Acid Score (DIAAS).

Bing Chat was the most accurate AI assistant with a mean accuracy rate of 63.6% for all analyses, followed by ChatGPT 4 with 60.6%. ChatGPT 4 (Cohen’s kappa: 0.718, p < 0.001) and ChatGPT 3.5 (Cohen’s kappa: 0.636, p: 0.002) showed substantial agreement between baseline and 2nd analysis, whereas they showed a moderate agreement between baseline and 3rd analysis (Cohen’s kappa: 0.538, p: 0.011 for ChatGPT 4 and Cohen’s kappa: 0.455, p: 0.030 for ChatGPT 3.5).

This study provides an initial insight into how emerging AI models assess and classify nutrient content pertinent to nutritional knowledge. Further research into the real-world implementation of AI for nutritional advice is essential as the technology develops.

This study serves two purposes: (1) to evaluate the effects of organizational ambidexterity by examining how the balanced and the combined sales–service configurations of chatbots differ in their abilities to enhance customer experience and patronage and (2) to apply information boundary theory to assess the contingent role that chatbot sales–service ambidexterity can play in adapting to customers' personalization–privacy paradox.

An online survey of artificial intelligence chatbots users was conducted, and a mixed-methods research design involving response surface analysis and polynomial regression was adopted to address the research aim.

The results of polynomial regressions on survey data from 507 online customers indicated that as the benefits of personalization decreased and the risk to privacy increased, the inherently negative (positive) effects of imbalanced (combined) chatbots' sales–service ambidexterity had an increasing (decreasing) influence on customer experience. Furthermore, customer experience fully mediated the association of chatbots' sales–service ambidexterity with customer patronage.

First, this study enriches the literature on frontline ambidexterity and extends it to the setting of human–machine interaction. Second, the study contributes to the literature on the personalization–privacy paradox by demonstrating the importance of frontline ambidexterity for adapting to customer concerns. Third, the study examines the conduit between artificial intelligence (AI) chatbots' ambidexterity and sales performance, thereby helping to reconcile the previously inconsistent evidence regarding this relationship.

The purpose of this article is to explore the relationship between the attributes of Olympic mascots and their impact on sponsorship effectiveness. Based on a multiattribute model and the introduction of engagement theory and the meaning transfer model, this article uses the 2022 Beijing Winter Olympics mascot “Bing Dwen Dwen” as the research object to empirically analyze the effects and mechanisms of the mascot's attributes on preference, event engagement, sponsorship enterprise trust and sponsorship enterprise attitude, ultimately constructing a sponsorship effectiveness model.

The survey method was used to examine 238 respondents' emotions and attitudes towards companies participating in sponsoring Olympic mascots.

The study found that the main attributes of the mascot include visual and emotional factors, both of which have a positive impact on preference, with emotional factors having a greater influence than visual factors. Visual and emotional factors indirectly affect engagement through preference. Preference and engagement play a completely mediating role in the effect of mascot attributes on sponsorship enterprise trust and sponsorship enterprise attitude.

This study provides practical recommendations for managers to achieve marketing success in sports sponsorship through mascots.

This paper provides a measurement tool for the study of mascot attributes and important support for subsequent research in sponsorship marketing.

The purpose of this study is to investigate the influence of IP (Intellectual Property) on the intention for premium consumption of Generation Z, and to construct a theoretical model of IP on the premium consumption of Generation Z. Based on the results of the study, it provides better marketing suggestions to merchants, and is an expansion of previous research on the consumption behavior of Generation Z.

This paper contains two empirical tests and one experimental analysis. First, this study crawl over 5,000 pieces of Generation Z’s consumption data from Poizon, an e-commerce platform and exclusive trending community for Generation Z. Second, this study designs a two-group online experiment to collect 292 valid data from members of the Generation Z. The authors use Stata software for multiple linear regression, t-tests, and ANOVA to test the hypotheses.

The results of the data analysis show that IP has a significant positive effect on the premium consumption intention of Generation Z, and the limited release strategy positively moderates the effect. Self-image congruence and social identification play mediating role in the influence of IP on Generation Z’s premium consumption.

First, this study finds a link between IP and commodity premiums, which is the first study to explore the effect of IP on commodity price changes. Second, this study is the first to examine the marketing science value of IP using a combination of empirical tests and experimental analysis. These fill research gaps. Finally, the mechanism of IP’s influence on Generation Z’s premium consumption is revealed, enriching the literature on Generation Z’s consumption behavior.

The evaluation of perceived attribute performance reflected in online consumer reviews (OCRs) is critical in gaining timely marketing insights. This study proposed a text mining approach to measure consumer sentiments at the feature level and their asymmetric impacts on overall product ratings.

This study employed 49,130 OCRs generated for 14 wireless earbud products on Amazon.com. Word combinations of the major quality dimensions and related sentiment words were identified using bigram natural language processing (NLP) analysis. This study combined sentiment dictionaries and feature-related bigrams and measured feature level sentiment scores in a review. Furthermore, the authors examined the effect of feature level sentiment on product ratings.

The results indicate that customer sentiment for product features measured from text reviews significantly and asymmetrically affects the overall rating. Building upon the three-factor theory of customer satisfaction, the key quality dimensions of wireless earbuds are categorized into basic, excitement and performance factors.

This study provides a novel approach to assess customer feature level evaluation of a product and its impact on customer satisfaction based on big data analytics. By applying the suggested methodology, marketing managers can gain in-depth insights into consumer needs and reflect this knowledge in their future product or service improvement.