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This research explores the differential effects of pixel-level and object-level visual complexity in firm-generated content (FGC) on consumer engagement in terms of the number of likes and shares, and further investigates the moderating role of image brightness.

Drawing on a deep learning analysis of 85,975 images on a social media platform in China, this study investigates visual complexity in FGC.

The results indicate that pixel-level complexity increases both the number of likes and shares. Object-level complexity has a U-shaped relationship with the number of likes, while it has an inverted U-shaped relationship with the number of shares. Moreover, image brightness mitigates the effect of pixel-level complexity on likes but amplifies the effect on shares; contrarily, it amplifies the effect of object-level complexity on likes, while mitigating its effect on shares.

Although images play a critical role in FGC, visual data analytics has rarely been used in social media research. This study identified two types of visual complexity and investigated their differential effects. We discuss how the processing of information embedded in visual content influences consumer engagement. The findings enrich the literature on social media and visual marketing.

The purpose of this paper is to examine how users on social media view brands with a heritage. Consumers commonly post opinions and accounts of their experiences with brands on social media. Such consumer-generated content may or may not overlap with content desired by brand managers. Drawing from “The medium is the message” paradigm, this study text-mines user narratives on Twitter1 to shed light on the role of social media in shaping public images of brands with heritage through the lens of the stereotype content model.

The study uses a data set of almost 80,000 unique tweets on 12 brands across six categories, compares brands high versus low in heritage and combines dictionary-based content analysis with sentiment analysis.

The results indicate that both user-generated content and sentiment are significantly more positive for brands low rather than high in heritage. Regarding warmth, consumers use significantly more positive words on sociability and fewer negative words on morality for brands low rather than high in heritage. Regarding competence, tweets include more positive words on assertiveness and ability for low-heritage brands. Finally, overall sentiment is more positive for brands low rather than high in heritage.

Important from co-creation and integrated marketing communication perspectives, the findings provide brand managers with actionable insights on how to more effectively use social media.

To the best of the authors’ knowledge, this research is among the first to examine user-generated content in a brand heritage context. It demonstrates that heritage brands, with their longevity and strong links to the past, need to be aware of how contemporary social media can detract from their image.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

The authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.

The authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.

Implications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.

Since AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.

The intention of consumers to behave sustainably is not a reliable predictor of sustainable hospitality choices. This intention-behaviour gap represents one of the biggest challenges for marketers and environment-friendly businesses. To address this issue, this study aims to draw upon the intention-behaviour gap. The authors revise the sustainable hospitality literature to identify the limitations, to evaluate the extent to which the intention-behaviour gap is embedded in the hospitality literature and to provide practical guidance on how to move research forward in the sustainable hospitality field.

The authors adopted a five-step process to review and analyse 71 scientific papers published in 14 Hospitality Journals. The authors developed a descriptive overview of the literature showing the publications in this field over the years, the sustainability practices implemented by companies and consumers and the setting of the studies. Finally, the authors conducted a critical analysis of research in sustainable hospitality adopting the intention-behaviour gap lens.

Leveraging the descriptive overview and critical analysis, the authors offer four directions for future research to address the existing literature limitations. The authors encourage scholars to expand the scope of the research setting, investigate diverse sustainability practices, integrate existing knowledge on the intention-behaviour gap into sustainable hospitality research and combine traditional research methods with emerging technologies.

This study exposes the theoretical challenge of applying conventional behaviour theories to sustainable hospitality, prompting a call for framework re-evaluation. It offers practical insights, empowering researchers, marketers and policymakers to navigate and mitigate the intention-behaviour gap in sustainable hospitality.

The originality of this paper is underscored by its distinctive focus on the unique intention-behaviour gap within sustainable hospitality, coupled with a compelling call to re-evaluate traditional behavioural frameworks. It provides a roadmap for future research in sustainable hospitality, benefiting researchers, policymakers and marketers in promoting sustainable initiatives.

This study aims to investigate the role of social media in increasing trust, self-perceived creativity and millennial entrepreneurial satisfaction.

The empirical analysis was conducted using a sample of 385 millennial entrepreneurs that were recruited for online survey. After conducting reliability and validity tests, the data were analyzed using partial least squares structural equation modeling.

The results showed that the quality of social media information had a direct and indirect positive and significant effect on trust and satisfaction. Social media marketing activities had a direct and indirect positive and significant effect on trust and self-perceived creativity. System Quality of social media also has a significant direct influence on trust. However, there is no direct relationship to satisfaction. Finally, social media marketing activities have a significant direct effect on trust and satisfaction.

This research can contribute to marketing experts and millennial entrepreneurs in improving the quality of advertising information and the credibility of social media used to support creativity, trust and satisfaction. In addition, marketing experts and millennial entrepreneurs with online-based communities should optimize their marketing activities on social media.

This study has shown a more comprehensive model of the relationship between information quality, system quality, social media marketing activities, self-perceived creativity, trust and satisfaction. This study also reveals a significant direct and indirect effect of social media marketing activities on satisfaction.

This paper aims to use the cognitive appraisal theory to investigate the effects of brand engagement and materialism on the positive emotions of joy and excitement evoked by a product at different stages of the purchase process.

Two complementary studies were conducted to achieve the research objectives. Study 1 used a longitudinal design, examining the pre-purchase stage and a moment shortly after the purchase. Study 2 adopted a cross-sectional approach, focusing on the post-purchase stage. Relationships among variables were analyzed through hierarchical regression and moderation analyses.

The findings indicate that materialism is not significantly associated with consumers’ positive product-evoked emotions (joy and excitement) before and shortly after purchase. At these two stages, brand engagement predicts positive emotions. In the post-purchase stage, where materialism is weakly related to joy and excitement, a robust positive link exists between brand engagement and positive product-evoked emotions. Moreover, higher levels of materialism reduce the experience of joy and excitement among participants with high levels of brand engagement.

Experiencing joy and excitement is crucial in consumer behavior. However, little is known about the determinants of these emotions in the consumption context, especially concerning internal consumption-related factors and different stages of the purchase process. This paper contributes to the field by shedding light on the role of brand engagement and materialism in the experience of positive product-evoked emotions. The findings provide evidence that brand engagement significantly enhances consumers’ positive product-evoked emotions, highlighting its importance in consumer behavior research.

Images and caption descriptions serve as important visual stimuli that influence consumer preferences; therefore, the current study focuses on property images and captions illustrated on the home pages of accommodation-sharing platforms. Specifically, this study investigates the relative importance of hue, brightness and saturation of a property image and caption description styles on potential consumers’ preferences.

A mixed-method approach was used, and a total of 293 valid responses were collected through a discrete choice experiment approach. Interviews were conducted for additional analyses to explore the detailed explanations.

The utility model demonstrated that the image’s saturation was the most critical attribute perceived by the respondents, followed by caption description style, hue and brightness.

This is one of the first studies to investigate the display of attributes on a digital accommodation platform by exploring potential customers’ stated preferences. This study focuses explicitly on images and captions illustrated on the home page of an accommodation booking platform. Detailed image investigation is also a new research area in sharing economy-related research.

This study aims to test the complaints of tourists who visit five-star hotels in Antalya, Turkey, on those same hotels’ websites.

In the study, the data were collected with qualitative methods but analyzed with the mixed analysis method. In this context, the authors collected 1,012 comments on the website between 2016 and 2019.

According to the results of the study, the most intense complaints were found to be concentrated in categories such as ambience, food and staff.

First of all, it is thought that it will make an important contribution to the literature, since different methodologies are adopted in the study. In addition, online shares, evaluations and comments produce positive or negative results for the destination or business in question. It is necessary to closely monitor such activities in electronic environments, as they may have negative consequences, thus revealing the need to take corrective or preventive measures. For this reason, the research is important in terms of not having such a large-scale study in the literature and contributing to the hospitality industry.

The development of the digital age has made data and information more transparent, enhancing the strategic perspectives of both buyers (strategic waiting) and sellers (price fluctuations) in their decision-making. This research investigates the optimal dynamic pricing strategy of the content product developer in relation to their consideration of consumer fairness concerns to elucidate the impact of consumer fairness concerns on the dynamic pricing strategy of the developer.

This paper assumes that monopolistic content developers implement a dynamic pricing strategy for the content product. Through constructing a two-period dynamic pricing game model, this research investigates the optimal decisions of the content developer, contingent upon their consideration or disregard of consumer fairness concerns. In the extension section, the authors additionally account for the influence of myopic consumers on these optimal decisions.

Our findings reveal that the degree of consumer fairness concerns significantly influences the developer’s optimal dynamic pricing decision. When a developer offers content products with lower depth, there is a propensity for the developer to refrain from incorporating consumer fairness concerns into a dynamic pricing strategy. Conversely, in cases where the developer offers a high-depth content product, consumer fairness concerns benefit the developer. Furthermore, our analysis reveals a consistent benefit for the developer from the inclusion of myopic consumers.

Few studies have delved into the conjoined influence of consumer fairness concerns and strategic behavior on dynamic pricing strategy. Our findings indicate that consumer fairness concerns can enhance the efficiency of the value chain for content products under specific conditions. This paper not only enriches the existing literature on dynamic pricing by incorporating consumer fairness concerns theoretically but also offers practical insights. The outcomes of this research can guide content product developers in devising optimal dynamic pricing strategies.