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Drawing on insights from social influence theory, the MAIN model of digital media affordances and the literature on the attention economy, this study aims to investigate how social media influencers’ community and content strategy contribute to follower engagement behavior in the presence of competition.

This study used a web-scraped data set of Instagram influencers. It measured community strategy using influencers’ number of followers, number of following and breadth of interest; content strategy using modality type and number of posts; competition using number of influencers operating in the same primary domain of interest; and follower engagement behavior using number of likes and comments. A negative binomial regression model was estimated to test the proposed hypotheses.

The findings reveal that elements of influencers’ community and content strategies, such as number of followers, modality type and number of posts, influence follower engagement behavior. Similarly, competition significantly influences follower engagement behavior both independently and by interacting with influencers’ community and content strategy.

The findings offer insight for brands to identify suitable influencers for partnerships. For instance, brands can judge influencers’ suitability for partnership based on how many followers they have, how many posts they share and how many competitors they have. Further, the findings offer insight for influencers on how they can drive follower engagement behavior by managing their social media community and content.

This study develops an integrated model of factors that determine follower engagement behavior for social media influencers. The findings emphasize influencers’ strategy as the primary driver of follower engagement behavior. Extant studies focus on followers’ motivation and perception to explain follower engagement behavior while the role of influencers’ strategy is underplayed.

This study aims to propose an experiential model of consumer engagement focusing on Facebook brand pages. Building on the brand experience literature, the study synthesizes the experiential affordances of Facebook brand pages along perceptual, social, epistemic and embodied dimensions and tests their impact on consumer engagement.

The study operationalized key variables of the proposed model at the brand page level and assembled pertinent data, using systematic content analysis, on a sample of Facebook brand pages (n = 85). Poisson regression tested the proposed model.

The findings indicate that brands that facilitate greater number of experiential affordances on their Facebook brand pages generated higher levels of consumer engagement. For both brand post likes and brand post shares, the contributions of experiential affordances were significant and positive.

The findings offer actionable managerial insights for brands seeking to implement an experiential model of consumer engagement on their fan pages.

This study contributes to the literature by proposing and testing an experiential model of consumer engagement in the context of Facebook brand pages. To date, the experiential value of Facebook brand pages has rarely, if at all, been tested in an empirical study.

The purpose of this study is to investigate the contribution of social media teams to firm social media performance. Although social media teams are tasked with planning, executing and optimizing the social media marketing effort of firms, little systematic research has examined their roles. Drawing on social cognitive theory, the present study develops collective social media efficacy as a key mechanism to explain the contribution of social media teams to firm social media performance.

The study tested a conceptual framework in which social media team members' previous experience, short-term training and online resources use contribute to collective social media efficacy. In turn, collective social media efficacy is hypothesized to enhance firm social media performance. The study employed primary data and PROCESS macro to test its proposed hypotheses.

The findings revealed that previous social media experience, short-term training and online resources use contributed to firm social media performance by enabling social media teams to build strong collective social media efficacy.

The findings offer novel insights into how firms can optimize their social media marketing effort by systematically managing their social media teams. The findings add to the nascent literature on the organizational influences of social media performance.

This study aims to examine how message strategy influences consumer behavioral engagement in social media. To this end, the study develops a comprehensive typology of branded content in social media and tests for its effect on consumer behavioral engagement.

A sample of brand posts derived from the official Facebook pages of top corporate brands was double-coded using an elaborate coding instrument. Message strategy was operationalized using three main message strategies (i.e. informational, transformational and interactional) and their paired combinations. Consumer behavioral engagement was operationalized using consumer actions of liking and sharing brand posts. Proposed relationships were tested with MANCOVA and univariate ANOVAs.

Results indicate that the transformational message strategy is the most powerful driver of consumer behavioral engagement, while no significant difference is observed between the informational and the interactional message strategies. Further, complementing the informational and interactional message strategies with the transformational message strategy markedly enhances their effectiveness.

Useful managerial guidance to develop effective message strategies is offered. In particular, the importance of transformational messages, both as a standalone and a complementary message strategy, is underscored. By mastering and deploying transformational messages more frequently in their social media communication, marketers could improve their effectiveness.

Drawing on a theory-driven typology, this study sheds light on how message strategy shapes consumer behavioral engagement in a social media context. Importantly, the study documents pioneering empirical evidence regarding the effect of combined message strategies on consumer behavioral engagement.

YouTube's vast and engaged user base makes it central to firms' digital marketing effort. With extant studies focusing on viewers' post-view engagement behavior, however, research into what motivates viewers to click on and watch YouTube videos is scarce. This study investigates the implications of marketers' video optimization practices for video views on YouTube.

The study employed a data set of videos (N = 4,398) gathered by scraping YouTube's trending list. Using a combination of text and sentiment analysis, the study measured four video optimization practices: information content of video titles, emotional intensity of video titles, information content of video descriptions and volume of video tags. It then analyzed the effect of these video optimization practices on video views.

The study finds that greater availability of information in video titles is negatively associated with video views, whereas intensity of negative emotional sentiment in video titles is positively associated with video views. Further, greater availability of information in video descriptions is positively associated with video views. Finally, an inverted U-shaped relationship is found between volume of video tags and video views. Up to 17 video tags can contribute to more video views; however, beyond 17 tags, the relationship turns negative.

This study investigates the effect of marketers' video optimization practices on video views. While extant studies mainly focus on viewers' post-view engagement behavior, such as liking, commenting on and sharing videos, this study examines video views. Similarly, extant studies investigate videos' internal content, while this study investigates elements of the video metadata.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The purpose of this study is to integrate divergent discussions located within the marketing, economic geography and economic sociology literature about trade fairs and their functions, around an exchange-based view of trade fairs.

Following an integrative review approach and drawing on thematic discussions found in social exchange theory, a large corpus of research articles on trade fairs was systematically reviewed and integrated.

Trade fairs facilitate five major exchange functions: transactional, informational, social, symbolic and cultural. Each of these functions is characterized by a distinctive combination of exchange structures, exchange rules, exchange resources and organizational outcomes.

The study offers practical guidelines regarding how exhibitors, visitors and organizers can develop and benefit from an exchange-based view of trade fairs.

The current study integrates three divergent literature streams into an exchange-based view of trade fairs. In so doing, the study offers a common conceptual foundation upon which future trade fair research can intersect and cross-fertilize.

The purpose of this paper is to analyse Facebook brand posts along dimensions of vividness, interactivity, novelty, brand consistency and content type and tests how these characteristics influence audience response in terms of liking and sharing brand posts.

The sample comprised 191 brand posts sourced from the Facebook brand pages of five top selling automotive brands in the UK. Audience response was operationalised using brand post likes and brand post shares, while brand post characteristics were operationalised according to relevant theory. Poisson regression models were tested to measure the effect of brand post characteristics on audience response.

The findings indicate that brand post vividness has a significant positive effect on brand post shares, but not on brand post likes. Brand post interactivity has a significant negative effect on both brand post likes and brand post shares. Brand post novelty and brand post consistency have a significant positive effect on both brand post likes and brand post shares. Finally, brand post content type has a significant positive effect on brand post likes, but not brand post shares.

The findings underscore the need for marketers to develop a systematic content strategy for Facebook brand pages. With this in mind, the study proposes several evidence based suggestions.

This study contributes to the literature first by synthesising and testing brand post characteristics that were overlooked in prior research and second by developing theoretically consistent operationalisation for already familiar brand post characteristics. These enhancements resulted in a final model with a superior explanatory power.

The purpose of this study is to examine how the deployment of market-based resources influence trade show (TS) organizers’ performance effectiveness as measured with exhibitor and visitor attendance levels. To this end, the study synthesizes several market-based resources including TS longevity, TS webpage interactivity, industry association support, exhibition duration and exhibition area, and investigates how their deployments affect TS attendance levels.

A cross-sectional dataset was compiled on 79 TSs by searching a variety of online sources. Organizers’ performance effectiveness was measured using TS attendance levels. The extent to which organizers deployed market-based resources was likewise quantified using hard data. A series of regression models was estimated to isolate the effect of the market-based resources on TS attendance levels.

The results demonstrate the existence of positive relationships between market-based resources and TS attendance levels which are characterized by diminishing returns. More specifically, the authors found that as the amount of the deployment of the market-based resources increases, their incremental contributions to TS attendance levels become smaller.

The findings offer comprehensive managerial insights about how organizers can improve their attendance levels by carefully deploying market-based resources.

The few available works that try to address the question of why some TSs are well attended while others are not rely on the opinions of exhibitors and visitors. This study attempts to address the same question from the organizers perspective, and, in so doing, it contributes to the spares literature on TS management from the organizers perspective.

The purpose of this paper is to investigate the underlying dimensions of trade show performance in an emerging market context. Firms in industrial and emerging markets typically differ in terms of access to firm level resource endowments. Such differences make attempts to generalize the trade show performance dimensions proposed for industrialized country exhibitors to emerging market exhibitors problematic. This motivates the need for understanding the dimensionality of trade show performance in an emerging market context.

Data obtained from firms that partake in an emerging market trade show that takes place in an emerging market are used to investigate the dimensionality of trade show performance. By subjecting several trade show performance items into principal component analysis, a multidimensional performance construct, applicable to emerging market exhibitors, is introduced.

The empirical findings show that trade show performance, in an emerging market context, is multidimensional. The findings suggest that emerging market exhibitors tend to utilize trade shows somewhat differently from their industrialized market counterparts.

The findings imply the need for exhibit managers in emerging markets to set multiple objectives for trade show participations. The findings also suggest that exhibit managers need to staff the trade show booth with individuals possessing different sets of expertise.

The paper clarifies the dimensionality of trade show performance in the context of emerging markets and sheds light on the tactical and the strategic roles that trade show participations play in emerging markets.