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The purpose of this paper is to analyze the role that food and beverage (F&B) sensory stimuli play in building non-food-themed touristic experiences, such as wellness tourism experiences.

This paper adopts an asynchronous netnographic approach supported by software (T-Lab, 2021). The study was conducted on a database consisting of 3,141 reviews in English, left by customers of 38 wellness facilities (Spa Retreats) spread across 5 continents.

The analysis reveals that F&B stimuli contribute significantly to the tourist's perception of the wellness experience in a two-fold manner: on the one hand, they support the wellness experience, and on the other, through specific proposals (e.g. wine, vegan, detox, etc.), they qualify and differentiate the wellness experience in a hedonic rather than eudaimonic way.

The present study contributes to managerial literature on the topic of gastronomic tourism and wellness tourism by providing, on an international scale, empirical evidence of (a) the importance and role of F&B touchpoints in hybrid gastronomic experiences; and (b) the presence of a variety of “wellness experiencescapes”.

This study is the first attempt to measure the role of F&B in tourists' perceptions of non-food-themed experiences. The research not only provides new data on the wellness experience through a cross-continental analysis but also offers useful theoretical and managerial insights for the design of wellness tourism experiences.

The purpose of this paper is to propose a research approach to investigate brand alignment by exploiting textual data from online brand communities in the coffee industry. Specifically, consumer brand associations from user-generated content (UGC) and company brand associations from firm-generated content (FGC) are explored to measure the alignment between brand identity and brand image. The selected context of research is the beverage industry wherein companies are called on to develop appropriate digital websites and brand communication strategies to enhance the consumers' brand experience.

The authors introduce a research approach that integrates netnography with text mining analysis. Since brand associations were the basis of the study’s analysis, the authors focused on text mining procedures, providing data (co-occurrences) corresponding to brand associations that consumers perceive and that the company communicates. Data were used to develop the measurements of brand alignment.

The main findings of this research highlight the importance for both scholars and practitioners of determining brand alignment of beverage products in online communities. Knowing the alignment between the way a company communicates its brand identity and how this is perceived by consumers allows for effectively reviewing brand communication.

Although the combined analysis of the alignment between brand image and brand identification has received attention in marketing literature, most scholars have neglected how to measure brand alignment. This is a need for many marketing managers in the coffee industry who are now moving in digital environments where the role of consumers is not that of receivers of brand communication but rather that of cocreators of brand value.

This chapter examines the translation of generic global goals into local action. It first discusses the translation of global goals into national agendas and the challenges of localizing the goals. Localizing the goals is essential for ensuring that the SDGs reflect local needs, norms, and values, thus ensuring that local actors find them relevant and meaningful. The chapter argues that cocreation is a key vehicle for the localization of the SDGs and identifies the key benefits that arise from using cocreation as a localization strategy. Cocreation can foster the will and capacity for local governments and communities to advance the cause of sustainability. Cocreation can help communities integrate the sustainable development goals, identify hidden resources, build support networks, create social accountability, etc.

This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

The purpose of this study is to identify and characterize the role of both original equipment manufacturer (OEM) and module supplier (MS) knowledge in the smartphone industry. In particular, this study aims to evaluate which of the two actors possesses the knowledge that has the greatest impact on the market satisfaction.

This study explores and combines the concepts of modularity and knowledge management by investigating the patent portfolio of 16 leading smartphone OEMs and 144 MSs. The applied methodology is based on the content analysis of patent data to extract information on both OEM’s and MS’s component knowledge.

The results show that, although its components are purchased from external MSs, the OEM should preserve both a general and specific concentration of component knowledge, as well as on the end product, to achieve a greater market satisfaction. Moreover, a positive direct relationship was found for the MS between the general concentration of component knowledge and the market satisfaction.

The novelty of this study is to segment the knowledge of both the OEM and the MS on multiple levels. To the best of the authors’ knowledge, this is one of the first studies that investigates the end product and component knowledge of both actors by filtering patent data using text-mining techniques. The originality of this work is to intercept the relationship between the different shades of knowledge of each actor and the market satisfaction.

With the progress of new technologies of information and communication, more and more producers of data exist. On the other hand, the web forms a huge support of all these kinds of data. Unfortunately, existing data is not proper due to the existence of the same information in different sources, as well as erroneous and incomplete data. The aim of data integration systems is to offer to a user a unique interface to query a number of sources. A key challenge of such systems is to deal with conflicting information from the same source or from different sources. We present, in this paper, the resolution of conflict at the instance level into two stages: references reconciliation and data fusion. The reference reconciliation methods seek to decide if two data descriptions are references to the same entity in reality. We define the principles of reconciliation method then we distinguish the methods of reference reconciliation, first on how to use the descriptions of references, then the way to acquire knowledge. We finish this section by discussing some current data reconciliation issues that are the subject of current research. Data fusion in turn, has the objective to merge duplicates into a single representation while resolving conflicts between the data. We define first the conflicts classification, the strategies for dealing with conflicts and the implementing conflict management strategies. We present then, the relational operators and data fusion techniques. Likewise, we finish this section by discussing some current data fusion issues that are the subject of current research.

Might a country’s economic growth performance differ depending on the evolution of its human capital? This paper aims to consider education as a channel for human capital improvement and then for economic growth. The authors hypothesize the existence of a threshold for education, after which point the characteristics of economic growth change.

To address this question, the authors turn from a linear framework to a nonlinear one by applying smooth transition specifications.

This empirical analysis for Spain points to the existence of nonlinearities in the relationship between education and economic growth at country level, for both secondary and tertiary education. Next, as different patterns emerge in different regions, the authors provide a regional analysis for a number of representative Spanish regions. The results show that both secondary and tertiary education matter for economic growth and that nonlinearities in this relationship should be taken into account.

What is learnt from using Smooth Transition Regression models for the education-economic growth link is that the educational level of the population can be understood as a source of nonlinearities in the economic activity of a country (and of a region). Thus, depending on national and regional educational levels, economic growth behaves differently.

Although the importance of nonlinearities has been identified, linearity is usually assumed in this field of the literature. This paper calls into question the linearity assumption by using time series techniques for 1971-2013 in Spain, an OECD country, and testing whether the results at country level hold for different regions within Spain as a robustness check.