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This study proposes “genuine small talk” in hospitality settings, particularly in coffee shops and its impact on enhancing guest experiences. This study aims to delineate how genuine small talk, characterized by sincerity, mutual respect, truthfulness and empathy, differs from traditional conversational engagements and influences service outcomes.

The study adopts a case research approach, focusing on the global coffee shop industry, particularly in high-context cultural settings. Using an abductive research paradigm, it intertwines theoretical concepts with empirical data gathered from face-to-face interviews with coffee shop visitors and managers. Data analysis involved qualitative coding techniques to synthesize and interpret findings related to genuine small talk.

Genuine small talk in hospitality, marked by sincerity, mutual respect, truthfulness and empathy, significantly enhances customer experiences. It transforms service encounters, turning negative experiences into positive ones and fostering customer loyalty. The study finds that genuine small talk is a strategic tool for emotional resonance and repeat patronage, yet its effectiveness depends on the staff’s ability to discern and adapt to customer moods and preferences.

This study highlights that genuine interpersonal interactions are key to enhancing customer experiences in hospitality. These genuine exchanges, characterized by sincerity, mutual respect, truthfulness and empathy, not only improve the immediate service encounter but also foster long-term customer loyalty. By transforming transactional interactions into meaningful connections, genuine small talk serves as a strategic tool in the hospitality industry, potentially reshaping service dynamics and elevating the perceived value of customer service. This research underscores the importance of staff training in emotional intelligence and adaptability to customer preferences, crucial for implementing genuine small talk effectively.

This research contributes to the hospitality literature by elucidating the nuanced role of genuine small talk in service encounters. It extends existing discourses of service interactions by highlighting the potential of genuine small talk in fostering connections and enhancing guest experiences.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.