Dr Moji Barari

Purpose: This paper aims to explore the concept of "actor engagement" within the context of the sharing economy, a novel and dynamic business model. Specifically, it investigates the formation of actor engagement and its relationship with value creation within this business model. Design/methodology/approach: Drawing on Storbacka et al. (2016)actor engagement framework and service-dominant logic service ecosystem model, unstructured data (text and images) from the Airbnb platform in seven countries and text- and image-mining techniques such as machine learning are used to measure the research variables and test the model by PLS-SEM. Findings: The results indicate that affective engagement has a more significant impact on behavioural engagement than cognitive engagement for multidimensional actor engagement. Service providers' engagement ¿ directly, and through customer engagement ¿ influences value creation for service providers (i.e. performance). Moreover, national-level moderator (i.e. economic, competitiveness, technology, social and political factors) plays a significant moderating role in our model. Research limitations/implications: This study encourages future research to explore how actor engagement leads to value creation for all actors on the different sharing economy platforms. Practical implications: The findings provide practical insights for service providers to engage their customers and platform managers, especially in an international context, on managing their relationships with both customers and service providers in different countries. Originality/value: This study advances the current literature on actor engagement and its role in value creation by providing a better understanding of the role of the national context in this process through unstructured data analysis.

Computational content analysis (CCA) has experienced a surge in popularity in the field of advertising research. Despite advancements, a comprehensive methodology guide in this area is lacking, presenting challenges for researchers seeking to incorporate these techniques into their study design. This methodology paper aims to provide a thorough overview of CCA applied to different and multiple modalities, including text, images, audio, and video, as a guide for interested researchers. We outline the use of machine learning through CCA in advertising research, covering a wide range of supervised (classification, object detection, emotion analysis, audio sentiment analysis, regression) and unsupervised (topic modeling and clustering) machine learning methods, alongside conventional CCA methods (entity extraction and sentiment analysis). Additionally, we provide a future research agenda that demonstrates how researchers can utilize generative artificial intelligence in CCA.

Purpose: The purpose of this research is to investigate the negative impact of social media influencers (SMIs) (human vs virtual) on customer well-being. Additionally, it aims to explore how the fear of missing out (FOMO) mediates and regulatory focus moderates this relationship from the social comparison theory lens. Design/methodology/approach: In the first study, text mining and machine learning were employed to measure the level of followers' well-being in response to 40 SMIs (human vs virtual) posts on Instagram. In the second study, a randomized between-subjects experiment was conducted with three groups (human vs virtual vs control) and a sample size of 412 participants to confirm the results of the first study and investigate how FOMO mediates and regulatory focus moderates the relationship between SMI beauty product endorsement and consumer well-being. Findings: The findings from text mining indicate that SMIs have a greater impact on consumers well-being, which is higher for virtual than human influencer. Additionally, the result of the experimental study shows the mediating role of FOMO in their relationship between SMIs and well-being. The moderator analysis reveals that there is a moderating effect of regulatory focus in the model. Practical implications: The findings inform marketing managers about the differences between virtual than human influencer in their impact on customer well-being in endorsing beauty product, especially among the younger generation. Originality/value: This paper is among the first research studies that examine the dark side of SMIs, which diminishes their follower's well-being through social comparison theory lenses.

Although omnichannel retailing has received considerable attention from scholars and practitioners in recent years, its impacts on customer experience and relationship outcomes remain unclear. Therefore, this study aims to examine the effects of two components of service integration in omnichannel retailing, namely service consistency and service transparency, on customer experience (i.e. flow and perceived privacy risk), and customer loyalty. Flow theory and hyperbolic discounting theory are employed to underpin these relationships. The data was collected using an online survey with 786 useable responses. Our findings indicate that service consistency has a direct and significant impact on flow and perceived risk while only the effect of service transparency on flow is significant. Moreover, both flow and perceived risk are related to customer loyalty to a retailer. Furthermore, it is found that showrooming behaviour and location-based service usage moderate the relationship between service consistency and privacy risk. The findings of the research provide important implications for a retailer regarding the development, implementation and management of omnichannel strategy.

Customer engagement behaviour has emerged as an influential concept in marketing and refers to customers' behavioural manifestation towards a firm originating from motivational drivers. To provide a comprehensive and generalisable picture of this concept, this study provides a meta-analysis integrating data of 196 effect sizes of 184 publications with a sample of 146,380. The findings reveal engagement through two pathways: organic pathway as relationship-oriented (perceived quality, perceived value and relationship quality) and promoted pathway as firm-initiated (functional and experiential initiatives). Moderator analysis indicates that the influence of the two pathways on engagement depends on engagement context (online vs. offline), industry type (service vs. manufacturing) and product type (hedonic vs. utilitarian) and cultural context. Findings support attitudinal engagement¿loyalty and behavioural engagement¿firm performance linkage. Study results provide new insight into various engagement approaches and their relationship to each other. The authors offer recommendations to help marketers manage their customer engagement process more effectively.

Underpinned by the Bagozzi and Dholakia (1999) goal setting and striving framework this research firstly develops a negative online customer experience model after which regulatory focus theory is used to compare this model with a positive online customer experience model. Analysis of responses from 201 respondents in the first study shows service failure causes negative affective and cognitive experience and has an impact on dissatisfaction and negative word of mouth in the online retailing context. Moreover, results of a second study among 200 respondents indicates that while customer priority in a successful shopping context is affective experience, in a service failure the customer priority moves from an affective to a cognitive experience. Similarly, compared to cognitive experience, affective experience has a higher impact on customer satisfaction and positive word of mouth in a successful shopping context, while in an unsuccessful shopping context cognitive experience has higher impact on dissatisfaction and negative word of mouth. The findings of this study contribute to customer experience management in both successful and unsuccessful shopping situations.

This study focuses on the development of a customer experience ecosystem during a journey which is embedded in meso- and macro-layers. Using the critical incident technique, the author collected in-depth interview data from bank customers in Switzerland and Iran to empirically study this ecosystem, including customer¿company interaction in the micro-layer and social context of the meso-layer. Moreover, in a macro-layer analysis, the Hofstede cultural dimension was employed to show the role of cultural context in this ecosystem. The findings indicate that customer experience in the pre-encounter stage is mostly shaped by customer past experience and social context rather than company touchpoint. The importance of these factors is different in the two cultural contexts. Although customer experience in the encounter stage is mainly the result of customer and company interactions, other people have a role in this stage and cultural differences between the two countries largely explain these differences. Moreover, in the post-encounter stage, customers in different cultural contexts use various factors to evaluate their experiences and the effects on their emotional and behavioral responses. The findings provide key managerial implications for national and international companies with respect to the role of multiple layers in customer experience management.