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The purpose of this paper is to provide a cross-platform recommendation system that recommends the most suitable public Instagram accounts to Facebook users.

We collect data from both Facebook and Instagram and then propose a similarity matching mechanism for recommending the most appropriate Instagram accounts to Facebook users. By removing the data disparity between the two heterogeneous platforms and integrating them, the system is able to make more accurate recommendations.

The results show that the method proposed in this paper can recommend suitable public Instagram accounts to Facebook users with very high accuracy.

To the best of the authors’ knowledge, this is the first study to propose a recommender system to recommend Instagram public accounts to Facebook users. Second, our proposed method can integrate heterogeneous data from two different platforms to generate collaborative recommendations. Furthermore, our cross-platform system reveals an innovative concept of how multiple platforms can promote their respective platforms in a unified, cooperative and collaborative manner.

This paper aims to investigate how recommendations from close- versus distant-others influence consumer preferences. This paper explores how the consumption setting (public vs private) differentially affects the relative weight given to recommendations from these two sources.

Through five scenario-based experiments and an internal meta-analysis, this paper examines whether consumers are more likely to follow recommendations from distant- (vs close-) others in public consumption settings. As a test of the underlying process, this study also investigates the mediating role of distinctiveness-signaling motivation in why consumers overweight recommendations from distant others in public settings, and the moderating role of atypical product design.

The findings of this study support the hypothesis that recommendations from distant-others have a greater impact on consumer preferences in public consumption contexts, as opposed to recommendations from close-others. This result can be attributed to the heightened salience of consumers’ distinctiveness-signaling motives in public consumption contexts, leading them to prioritize exhibiting uniqueness over conforming to close-others’ recommendations. However, this study also reveals that the presence of alternative sources of distinctiveness, such as atypically designed products, can mitigate this effect, leading consumers to seek conformity to close-others’ recommendations even in public consumption contexts.

This research did not look into the possible culture impact on the nonconforming consumption behavior. Previous research indicates that in collectivist cultures, nonconformity and distinctiveness are valued less (Kim and Drolet, 2003). This may imply that even with provoked signaling motives, collectivist consumers may not exhibit divergence from close-others. In fact, they may do the exact opposite and possibly become even more conforming to recommendations from close-others.

This research shed light on the business practice regarding word-of-mouth (WOM). Specifically, this research results suggest that for publicly consumed product, companies may need to seek a nontraditional WOM and use less WOM from consumer’s close-others.

Marketers often use referrals and recommendations from close-others to shape consumers’ preferences. In contrast, this study shows that for publicly consumed products, consumers may diverge from conforming to their close-others.

Textual information about restaurants, such as online reviews and food categories, is essential for consumer purchase decisions. However, previous restaurant recommendation studies have failed to use textual information containing essential information for predicting consumer preferences effectively. This study aims to propose a novel restaurant recommendation model to effectively estimate the assessment behaviors of consumers for multiple restaurant attributes.

The authors collected 1,206,587 reviews from 25,369 consumers of 46,613 restaurants from Yelp.com. Using these data, the authors generated a consumer preference vector by combining consumer identity and online consumer reviews. Thereafter, the authors combined the restaurant identity and food categories to generate a restaurant information vector. Finally, the nonlinear interaction between the consumer preference and restaurant information vectors was learned by considering the restaurant attribute vector.

This study found that the proposed recommendation model exhibited excellent performance compared with state-of-the-art models, suggesting that combining various textual information on consumers and restaurants is a fundamental factor in determining consumer preference predictions.

To the best of the authors’ knowledge, this is the first study to develop a personalized restaurant recommendation model using textual information from real-world online restaurant platforms. This study also presents deep learning mechanisms that outperform the recommendation performance of state-of-the-art models. The results of this study can reduce the cost of exploring consumers and support effective purchasing decisions.

关于餐厅的文本信息, 如在线评论和食品分类, 对于消费者的购买决策产生至关重要。然而, 先前的餐厅推荐研究未能有效利这些文本信息去预测消费者喜好。本研究提出了一种新颖的餐厅推荐模型, 以有效估计消费者对多个餐厅属性的评估行为。

我们从 Yelp.com 收集了来自25,369名消费者对 46,613 家餐厅的 1,206,587 条评论。利用这些数据, 我们通过结合消费者身份和在线消费者评论生成了消费者偏好向量。然后, 我们结合了餐厅身份和食品分类来生成餐厅信息向量。最后, 考虑到餐厅属性向量, 本研究调查了消费者偏好和餐厅信息向量之间的非线性交互关系。

我们发现, 所提出的推荐模型相比于之前最先进的模型表现出更优秀的性能, 这表明结合消费者和餐厅的各种文本信息是预测消费者喜好的基本因素。

据我们所知, 这是第一项利用来自真实在线餐厅平台的文本信息开发个性化餐厅推荐模型的研究。本研究还提出了胜过最先进模型的深度学习机制。本研究的结果可以降低探索消费者行为的成本并支持有效的购买决策。

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts.

The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach.

The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output.

This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014.

The recommendation of the analyst report is not only limited to a small number of ratings, but also biased toward a buy opinion with the absence of sell opinion. As an alternative to this, this paper aims to extract analysts' textual opinions embedded in the report body through text analysis and examine the profitability of investment strategies. Analyst opinion about a firm is measured by calculating the frequency of positive and negative words in the report text through the Korean sentiment lexicon for finance (KOSELF). To verify the usefulness of textual opinions, the author constructs a calendar-time based portfolios by the analysts' textual opinion variable of each stock. When opinion level is used, investment strategy has no significant hedged portfolio return. However, hedged portfolio constructed by opinion change shows significant return of 0.117% per day (2.57% per month). In addition, the hedged return increases to 0.163% per day (3.59% per month) when the opening price is used instead of closing price. This study show that the analysts’ opinion extracted from text analysis contains more detailed spectrum than recommendation and investment strategies using them give significant returns.

This study explores the role of smart voice assistants (SVAs) as purchase recommenders, a phenomenon the authors term “word of voice” (WOV) communication. By integrating human–computer interaction (HCI) literature and electronic word of mouth (eWOM) research, the authors examine what makes consumers trust in SVA-transmitted WOV communication following their initial interactions with their SVAs during a purchase process (i.e. post-trust); and the authors propose that consumers' perceptions of their SVAs' smart capabilities (i.e. cognitive, emotional and social) are critically important for building this trust. Moreover, the study explores the influence of post-trust on consumers' adherence to WOV communication, measured by three types of behavioural intentions.

Data from a survey of 202 United States (US)-based SVA users who employ them to obtain purchase recommendations were collected and analysed. They confirmed the validity of the measurement scales and provided input for the partial least squares modelling (PLS-SEM).

The results demonstrated that post-trust in WOV communication partially or totally mediates the effect of smart capabilities on consumer adherence to WOV communication; identified the key role of cognitive, emotional and social smart capabilities for building consumers' post-trust in WOV and demonstrated the influence of this trust on behavioural intentions.

The present study contributes by examining the employment of SVAs as recommenders during the purchase process; the authors term this type of communication WOV. It analyses consumers with experience of using SVAs in their purchase processes, revealing that post-trust in WOV communication is the psychological mechanism that explains how the smart capabilities of SVAs determine consumer adherence to the recommendations they receive.

This paper aims to theorize and investigate the use of effective color features in artificial intelligence (AI) influencers, an emerging marketing trend in the social media context.

By analyzing 6,132 pictures posted by ten AI influencers on Instagram, this paper examines the effect of warm colors in AI influencers’ social media posts on consumer responses, and how other color features may moderate the effect of warm color. In addition, two experimental studies reveal the underlying process driving the effect of warm color.

Warmer color generated more favorable consumer responses, with brightness significantly moderating the relationship between warm color and favorable consumer responses. Moreover, the results of the experiments establish that perceived warmth and emotional trust mediate the causal effect of warm colors on consumer responses.

There is still little understanding about consumer perceptions of AI influencers and their acceptance of AI influencers’ product recommendations. As such, this research offers theoretical understanding of the color features influencing the effectiveness of recommendations by AI influencers.

Brands have started deploying AI influencers as their brand ambassadors to make product recommendations, representing a new wave of advertising on social media. The findings will thus benefit marketers in developing effective product recommendations using AI influencers.

The present research provides a novel understanding of how visual features, such as color can influence the effectiveness of AI influencers.

In this paper we (a pracademic, a practitioner, and an academic) aim to explore the academic/practitioner gap in social marketing and offer recommendations to close it, while amplifying existing examples of best practice from within the field. We also propose a research agenda to spur dialog and guide further investigations in this area. Insights from prior research, coupled with the co-authors’ experience and observations, indicate that a disconnect does exist between academia and practice within social marketing, though it is admittedly and unsurprisingly not uniform across contexts and disciplinary areas. Given social marketing’s identity as a practice-oriented field, there are many existing examples of academic/practitioner collaboration and the successful linkage of theory and practice that deserve to be amplified. However, the challenges associated with the very different systems and structures affecting both worlds mean the disconnect is problematic enough to warrant systematic change to ensure the two worlds are more aligned.

This paper (a pracademic, a practitioner and an academic) explores the academic/practitioner gap in social marketing and offer recommendations to close it, while amplifying existing examples of best practice from within the field. The authors also propose a research agenda to spur dialog and guide further investigations in this area.

The authors suggest five key reasons that focus should be placed upon closing the academic/practitioner gap in social marketing: demonstrating societal value by contributing to practice; embedding and developing theories in practice; adding to the social marketing literature; contributing to social marketing teaching; and communicating the value and effectiveness of social marketing. To close the gap, the authors propose specific recommendations within four broad areas: marketing the academia and practitioner collaboration offer; building ongoing relationships; creating collaborative partnerships; and changing the publishing model ensuring communications are accessible to all. They also suggest ways for social marketing associations and peak bodies to play a role.

The concept of a disconnect between academia and practice is by no means new; it has been a pervasive issue across disciplines for decades. However, this issue has not been the subject of much discussion within the social marketing literature. Recommendations outlined in this paper serve as a starting point for discussion. The authors also acknowledge that due to long standing “bright spots” in the field, numerous examples currently exist. They place an emphasis upon highlighting these examples while illuminating a path forward.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”