We propose that the literature on customer engagement has emphasized the benefits of customer engagement to the firm and to a large extent ignored the customers' perspective. By drawing upon co-creation and other literature, this paper attempts to alleviate this gap by proposing a strategic framework that aligns both the customer and firm perspectives in successfully creating engagement that generates value for both the customer and the bottom line. Design/methodology/approach: A strategic framework is proposed that includes the necessary firm resources, data, process, timeline and goals for engagement, and captures customers' motives, situational factors, and preferred engagement styles. Findings: We argue that sustainability of data-driven customer engagement require a dynamic and iterative value generation process involving 1) customers recognizing the value of engagement behaviours and 2) firm's ability to capture and passing value back to customers. Originality/value: This paper proposes a dynamic strategic value creation framework that comprehensively captures both the customer and firm perspectives to data-driven customer engagement.
The literature examining the relationship between corporate social responsibility (CSR) and corporate financial performance (CFP) in the tourism industries is extensive but it has not verified the relationship unambiguously. This has been attributed to the methodological artefacts used, but also to the lack of a solid theoretical foundation. Based on stakeholder theory, this paper proposes the use of two models that explicitly investigate the relationship between stakeholder management, expressed as CSR activities, firm strategy and CFP. The strategic stakeholder model and the intrinsic stakeholder commitment model are evaluated in terms of their descriptive accuracy in four different tourism-related industries (airlines, casinos, hotels and restaurants) using panel regressions for the years 2005 to 2014. The results provide useful theoretical insights into the way in which CSR interacts with firm strategy and CFP, as well as managerial insights into how tourism practitioners can identify which CSR activities may impact CFP.
Complexity surrounding the holistic nature of customer experience has made measuring customer perceptions of interactive service experiences challenging. At the same time, advances in technology and changes in methods for collecting explicit customer feedback are generating increasing volumes of unstructured textual data, making it difficult for managers to analyze and interpret this information. Consequently, text mining, a method enabling automatic extraction of information from textual data, is gaining in popularity. However, this method has performed below expectations in terms of depth of analysis of customer experience feedback and accuracy. In this study, we advance linguistics-based text mining modeling to inform the process of developing an improved framework. The proposed framework incorporates important elements of customer experience, service methodologies, and theories such as cocreation processes, interactions, and context. This more holistic approach for analyzing feedback facilitates a deeper analysis of customer feedback experiences, by encompassing three value creation elements: activities, resources, and context (ARC). Empirical results show that the ARC framework facilitates the development of a text mining model for analysis of customer textual feedback that enables companies to assess the impact of interactive service processes on customer experiences. The proposed text mining model shows high accuracy levels and provides flexibility through training. As such, it can evolve to account for changing contexts over time and be deployed across different (service) business domains; we term it an “open learning” model. The ability to timely assess customer experience feedback represents a prerequisite for successful cocreation processes in a service environment.
Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.
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