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In response to recent calls for deeper understanding of value co-creation between multiple actors, this article explores co-creation in collective consumption contexts. These are defined as settings within which multiple consumers, and optionally multiple other actors such as service personnel, are co-present (physically and/or virtually) and coordinate with one another during product/service consumption. To understand co-creation in such contexts, this article argues for an integration of practice-based and experience-based perspectives, because while collective coordination occurs via social practices, the value that results is by definition an individual experience. By studying an orchestral music context in which multiple consumers and service providers participate, the authors develop a framework dialectically relating co-creation practices to value. Four variables emerge influencing the relationship between co-creation practices and value: role rigidity, consumer heterogeneity conflict, participation access, and signposting. Value can be constrained by role rigidity and by consumer heterogeneity conflict between consumers of differing competence; mitigating this requires that service providers pay attention to participation access and signposting (guiding consumers to select and combine practices in line with their skills and competences). Overall, the findings show how practices shape not just coordination among consumers, but also social learning. Implications for service organizations include how to facilitate social learning between novices and experts so as to optimize value for all.

Word of mouth disseminates across Twitter by means of retweeting; however the antecedents of retweeting have not received much attention. This study uses the CHAID decision tree predictive method (Kass, 1980) with readily available Twitter data, and manually coded sentiment and content data, to identify why some tweets are more likely to be retweeted than others in a (political) marketing context. The analysis includes four CHAID models: (i) using message structure variables only, (ii) source variables only, (iii) message content and sentiment variables only and (iv) a combined model using source, message structure, message content and sentiment variables. The aggregated predictive model correctly classified retweeting behavior with a 76.7% success rate. Retweeting tends to occur when the originator has a high number of Twitter followers and the sentiment of the tweet is negative, contradicting previous research (East, Hammond, & Wright, 2007; Wu, 2013) but concurring with others (Hennig-Thurau, Wiertz, & Feldhaus, 2014). Additionally, particular types of tweet content are associated with high levels of retweeting, in particular those tweets including fear appeals or expressing support for others, whilst others are associated with very low levels of retweeting, such as those mentioning the sender's personal life. Managerial implications and research directions are presented. The study makes a methodological contribution by illustrating how CHAID predictive modelling can be used for Twitter data analysis and a theoretical contribution by providing insights into why retweeting occurs in a (political) marketing context.

Commonly used methods of evaluating the impact of marketing communications during political elections struggle to account for respondents' exposures to these communications due to the problems associated with recall bias. In addition, they completely fail to account for the impact of mediated or earned communications such as newspaper articles or television news, that are typically not within the control of the advertising party, nor are they effectively able to monitor consumers' perceptual responses over time. This study based on a new data collection technique using cell-phone text messaging (called real-time experience tracking or RET) offers the potential to address these weaknesses. We propose an RET-based model of the impact of communications and apply it to a unique choice situation: voting behavior during the 2010 UK general election, which was dominated by three political parties. We develop a Bayesian zero-inflated dynamic multinomial choice model that enables the joint modeling of: the inter-play and dynamics associated with the individual voter's choice intentions over time, actual vote, and the heterogeneity in the exposure to marketing communications over time. Results reveal the differential impact over time of paid and earned media, demonstrate a synergy between the two, and show the particular importance of exposure valence and not just frequency, contrary to the predominant practitioner emphasis on share-of-voice metrics. Results also suggest that while earned media have a reducing impact on voting intentions as the final choice approaches, their valence continues to influence the final vote: a difference between drivers of intentions and behavior that implies that exposure valence remains critically important close to the final brand choice.