The popularity of live streaming is driving the emergence of a new business model, known as live-streaming commerce (LSC). While there are more and more broadcasters in LSC, their behaviors and performance of them are significantly different. To have a better understanding of broadcasters, we employ different machine learning models to identify different portraits in both static and dynamic dimensions. We collect a rich live-streaming dataset from one leading platform in China. Our dataset features information for both broadcasters and viewers, including viewers’ purchasing behaviors, viewers’ records of posting words, broadcasters’ gender, the number of followers for broadcasters, and the live streaming show information, including the start and end time, and the viewers in each live streaming show. The rich textual information in broadcasters’ profile induction provides us a good opportunity to uncover different static portraits and the records in live streaming shows give us a chance to identify different dynamic behavioral portraits for broadcasters.

Artificial intelligence generated content (AIGC) refers to content produced by artificial intelligence that represents the perspectives of its users, and a new technique of content Generation. Continuous development in deep learning and algorithms have facilitated the adoption of AIGC. This research summarizes literature published under the topic of AIGC using bibliometric analysis method, aims to provide insightful research directions for future studies. 342 documents were collected from Database of Web of science, network visualization analysis among authors and citation analysis over publications are presented to scholars who wish to further research into this area.

In recent years, advances in the digital and live streaming economy have led to exponential growth in the number of self-employed streamers who have become an integral part of the self-driven digital labor force. However, previous research on the impact of streamers' work time arrangements on their virtual gifts remains scarce. To fill this gap in the literature, using large-scale data from Kuaishou live streaming platform, we demonstrate that several features of streamers' work time have an important impact on their virtual gifts. Specifically, our results suggest that work time duration and timing improve streamers' virtual gifts; meanwhile, work time tempo has an adverse effect on streamers' earnings. Taken together, our results provide novel and actionable insights for millions of self-employed streamers, agencies, platforms, and policymakers.

Companies frequently rely on pricing algorithms to automate their price-setting in online markets; thereby, algorithmic dynamic pricing (ADP) has become a common pricing practice in the digital era, with retailers regularly tweaking products’ prices in their online shops. On Amazon.com alone, millions of price changes occur within a day, which corresponds to a price change approximately every ten minutes for each product. Yet, so far, the effects of such pricing algorithms on consumers are unclear. Since ascertaining consumer reactions is essential for retailers’ pricing strategies and retailers need to know how to mitigate negative reactions, our focal research questions are: How do consumers respond to ADP? How can retailers mitigate negative consumer reactions to ADP?

The costs associated with law enforcement have seen a sharp increase, driven by rising personnel costs and the growing demand for policing services (Gascón, 2010; Urban Institute, 2020). Considerable discussion has arisen about how science can potentially help law enforcement “do more with less”, and some scholars have suggested introducing new crime control technologies to address this problem (e.g., Roach, 2022; Weisburd & Neyroud, 2011). With the onset of the COVID-19 pandemic, police departments around the world had additional demand, as they were made responsible for overseeing and ensuring compliance with COVID protocols. As a response, some countries (e.g., Singapore and China; Barrett, 2021) resorted to employing service robots either alongside or in place of police officers to assist with COVID-related compliance tasks.

Service encounters increasingly feature AI-powered inputs such as add-ons recommendations or aftercare solutions. These novel forms of customer service, provided by AI rather than humans, can shape customers’ sense of agency throughout the customer journey. Customers find themselves in a form of competitive collaboration with AI, sharing tasks, resources, inputs, and decisions. This research conceptualises and develops a scale to measure shared agency power during customer-AI interactions. Understanding the role of agency in AI- customer interactions is important, as agency represents a source, mechanism, delimiter and effect of a human’s or a machine’s actions. Agency may differ across various service encounters and with it, the type of perceived risks associated with human-AI interactions. Future research may use the shared agency power scale to better understand the nature and impact of customer-AI interactions in a service context on traditional marketing factors.

Many retailers and food service providers offer programs as part of their loyalty programs in which customers are given stamps for each purchase of a qualifying product or service and redeemed for a reward once a certain number of stamps have been accumulated. We identify which stimuli in such goal-directed motivational promotions induce customers to participate in the program.

Adding new stores close to incumbent franchisees, or franchise encroachment, has been a contentious issue in the marketplace. Prior studies on the encroachment effect focus mainly on the store-level cannibalization, lacking the understanding of customer-level post-encroachment outcomes. The authors investigate individual-level transaction data from a cluster of bakery franchise stores to quantify the franchise encroachment effect at store- and individual-levels with store and customer heterogeneity. Our results show that incumbents experience a decrease in sales, and the extent of the lost sales is attenuated by the distance between incumbent and new stores and the size of incumbents’ product assortment. The individual-level analysis reveals that three customer segments exhibit different post-encroachment shopping behaviors: (1) patronizing incumbents only, (2) patronizing both incumbent and new stores, and (3) completely switching to new stores. While the second segment increases the post-encroachment overall spending, the third segment contributes to both cannibalization to incumbents and higher sales to the franchisor. Our findings offer managerial implications of how to manage franchise encroachment with context-dependency and customer segmentation in terms of maximizing overall franchise performance.

To reduce food waste at the retail and personal consumption stages, discounts are offered in retail channels to encourage consumers to buy goods that are less attractive or close to their expiry dates. While discounts can encourage consumers to accept and purchase suboptimal foods, previous studies find that low prices or price discounts will make consumers produce positive or negative perceptions of product values. Consumers may increase their purchase intentions due to price concessions, but will think that price reductions are caused by quality degradation and thus reduce their purchase intentions. Additionally, the literature rarely explores the interaction between original prices of suboptimal products and discount presentation modes. This study thus contends that the interaction between original prices of goods and discount types will lead to significant differences in consumers' attitudes and purchase behavior toward suboptimal products. For research goals, this study employed a full factorial between‐subjects experiment designed with 2 original prices (High and Low) × 2 discount presentation modes (Discount percentage and Discounted price). An anonymous web-based questionnaire posted on the popular PTT forum and in Facebook and Instagram related communities is used to collect the data, and then a total of 328 valid questionnaires were finally collected. The findings indicate that attitudes and purchase intentions toward suboptimal food with a low original price is significantly higher than that of a high original price. Among the interaction effects, the means of attitudes and purchase intentions on the level of the low original price of suboptimal foods presented by discount percentage are higher the other three types. For the high original prices of suboptimal foods, the means of attitudes and purchase intentions on the level of discounted price are higher than for discount percentage. Based on these findings, this study demonstrates that consumer attitudes and purchase decision-making toward suboptimal foods are shaped by original prices and discount presentation modes. In Asia-Pacific countries such as Taiwan, consumers are price-sensitive but once a food item belongs to the category of suboptimal foods, their perceptions of discounts become different. Consumers’ attitudes toward that food and their willingness to buy may be moderated by the high or low original price of suboptimal food with the levels of discount presentation mode. Thus, based on the analysis and results of this study, we offer fresh findings and make both theoretical and managerial contributions to the related field of suboptimal food marketing and price discounts.

Since time immemorial company’s interaction with its customers plays a vital role in co-creation of values and shared lifestyle. Similarly, for Japanese department stores passing through the declining stage of the life cycle, strategy based on maintaining relationships is important for renovating their business models. This study examines the importance of “Retail Brand Community” for Japanese department stores, considering from the perspective of Brand community and Social Identity Theory.

Green consumption behavior (GCB) is desirable for a better world. The trend of GCB is expected to rise in the coming years. As such, it is imperative to understand the enablers of GCB. A significant majority of the investigated drivers of GCB are consumer-level factors. Studies focusing on the consumer-level showed that factors such as values, intentions, and personal norms could influence GCB. However, it is argued that compared to values or intentions, self-determined motivation can better predict GCB. The effect of self-determined motivation types (i.e., autonomous and controlled motivation) on GCB remains unclear due to prevailing gaps and contradictory findings. Furthermore, it is posited that people exhibit more self-determined behavior if they have strong self-awareness. Higher self-awareness can be achieved through mindfulness; therefore, differences in mindfulness level could affect the motivation-behavior relationship.

Compared to the prevalence of advertising targeted at teens, our understanding of their vulnerability to advertising has been limited due to the cognitive/developmental view adopted by most previous research. However, cognitive development is not the most significant aspect that differentiates adolescents from adults. Adolescence is when teenagers start to take on more responsibility in defining themselves and become more skilled at using consumption to construct and signal their identity. On one hand, teens have a growing desire to express their unique identity as autonomous and distinctive individuals, separate from their family and differentiated from others. On the other hand, they are nearly obsessed with what others think about them, striving to belong to a group and feeling devastated by signs of disapproval from peers. This conflict between the need for assimilation and the need for differentiation is especially pronounced during adolescence when teenagers increasingly seek the approval of their peers while expressing their uniqueness. As a result, their sense of self is in a constant state of flux. This "shaky" self-identity has been shown in previous research to coincide with low self-esteem, which is associated with a high level of materialism.

The growing concerns about sustainable consumption encourage more consumer research on determinants that influence consumers’ green behavior. Although green consumption has risen for several decades, green adoption rates are often overestimated. This study rationalizes that developing strategies that follow human prehistorical roots may be effective in encouraging green consumption. This notion is supported by Miller (2009) regarding the reversal of direction to explaining consumer behaviour through human evolution and individual differences. Green strategies should focus on determining attributes that display specific characteristics favoured by respective peer groups. The current study introduces two evolutionary-focused priming stimuli as an ancestral motive to understand consumer behaviour. The selected evolutionary-focused stimuli represent the foundational modes in the evolutionary psychology of (1) mating (i.e. short-term mating) or (2) parenting (i.e. long-term parenting) modes. These different distinct modes are predicted to trigger different individual decisions based on each Sexual Selection strategy to pursue (Buss & Schmitt, 1993). Following Griskevicius’ et al. (2010) assertion that status signaling through pro-sociality is one of the primary green signaling mechanisms, this study further attempts to investigate the moderating effect of costly signaling. Therefore, this study aims to provide a conceptual framework with propositions on the role of evolutionary psychology in consumer decision-making in the green product context. The study proposes that viewing family-focused stimuli will induce higher buying intention on green products than viewing mating-focused stimuli. However, this effect may be moderated by costly signals.

This study explores the effectiveness of marketing data analytics learning and outcomes for marketing students in courses with data analysis components at a U.S. business school. The study considers various moderating factors, such as software adaptability, grades, class type, data interest, statistical analysis method, and perceived time- and cost-effectiveness. The findings have implications for marketing education in data analytics.

The Corona crisis has led to serious changes in teaching in MBA programs in recent years. Within a very short time, teaching at business schools was changed from face-to-face to online. While younger undergraduate students have sometimes had problems with this form of teaching, it has been very well received by typical MBA-students, , which are older, some of whom are working and often have families. This paper shows how the MBA market in Germany is developing and how Darmstadt Business School is positioning itself for the future in this challenging market environment.

Synced advertising (SA) has grown in popularity due to its ability to facilitate multitasking and personalize ads to the media content users consume. Research on the effects of SA has been limited, but it has been suggested that repetition and personalization with SA will lead to a stronger, lasting impression. However, there are concerns that SA could be perceived as intrusive and lead to privacy issues. This study seeks to explore how the consumer paradox between perceived relevance and privacy concerns may affect the outcome of the advertisements.

Emotion AI, a subset of AI that measures, understands, responds to, and elicits human emotions, is an emerging area that has great potential for advertising research and practice. Studies on the applicability of emotion AI in advertising and marketing have been growing in academic journals. This rapidly burgeoning scholarship creates a need for advertising scholars to comprehend the current status of the research on emotion AI in advertising as well as opportunities and challenges that this new technological development will bring to. Thus, this study aims to offer an overview of research on emotion AI in advertising to identify the scope of existing research, gaps in knowledge, and opportunities and challenges that lie ahead.

A typical consumer is exposed to more than 5,000 advertisements per day (Story 2015) by exposure to around 500 advertising messages before ending breakfast (Marshall, 2015), and it is impossible for consumers to remember most of the advertisement images and messages. Thus, one consistent yet not thoroughly investigated question for advertisers is how advertisers draw consumers' attention by differentiating their brand from competitors' brands. One suggestion from academia is making more "creative" advertising (Dahlén et al., 2008; Lehnert et al., 2014; Rosengren et al., 2013; Smith et al., 2008). However, it is still questionable the exact meaning of "advertising creativity," and the effects of creative advertising on consumer evaluation have not been fully investigated the effects of creativity in advertising evaluation by considering various boundary conditions. The objective of this research is to redefine advertising creativity, to understand how advertising creativity shapes consumers' evaluation, and how these effects are moderated by the different types of boundary conditions, such as industry category, by analyzing more than 100,000 advertising images and copies using a cutting-edge transfer learning technique. The results of the transfer learning algorithm indicate that both cognitive dimensions (e.g., novelty of image) and affective dimensions (e.g., awe and coolness) simultaneously affect the consumers' perception of the advertising creativity, and the current algorithm enables to detect of creative advertising image with 92% accuracy rate.

Previous studies offered inconsistent empirical results for the influence of customer participation on service satisfaction. One possible explanation for this inconsistency is that existing conceptualizations of customer participation do not clearly differentiate the distinct roles of customer participation in service. To address this gap, Dong and Sivakumar (2015) have proposed an updated classification for customer participation based on “output specificity,” which refers to the degree to that the nature of the output is influenced by the person who provides the resource. The output of the customer participation can either be “specific” or “generic”. The “specific output” is defined as the expected service outcome can be idiosyncratic depending on whether the service is provided by the customer or the employee. In contrast, “generic output” refers to expected service outcome is well defined regardless of whether it is delivered by the service provider or the customer. How output specificity of customer participation influences service satisfaction still lacks of empirical examination.

We are living in a world that is increasingly digital and undergoing dramatic changes as a result. In particular for luxury fashion, growing numbers of online customers as well as fast changing business environment, luxury retailers face the challenge of differentiating themselves by offering a better online customer experience (Chen et al. 2021). By doing so, luxury fashion retailers are increasingly deploying chatbots in their service encounters to enhance customer experience (Roy & Naidoo, 2021). Chatbots are powered by Artificial Intelligence (AI) (Hoyer et al. 2020) and are an example of AI robot that can provide human-computer interactions on a retail website (Lee et al. 2017). Intended to enhance the online customer experience, chatbots have the potential to provide a better understanding of the product performance, enable efficient use of customer time, and help build crucial customer relationships (Rese et al. 2020; Wilson-Nash et al. 2020; Xu et al. 2022). Therefore, chatbots’ potential has been highly valued by fashion retail industry and academia (Jiang et al. 2022).