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Understanding customers is critical for service researchers and practitioners. Today, customers are increasingly active online, and valuable information about their opinions, experiences and behaviors can be retrieved from a variety of online platforms. Online customer information creates new opportunities to design personalized and high-quality service. This paper aims to review how netnography as a method can help service researchers and practitioners to better use such data.

A systematic review and analysis were conducted on 321 netnography studies published in marketing journals between 1997 and 2017.

The systematic review reveals that netnography has been applied in a variety of ways across different marketing fields and topics. Based on the analysis of existing netnography literature, empirical, theoretical and methodological recommendations for future netnographic service research are presented.

This paper shows how netnography can offer service researchers unprecedented opportunities to access naturalistic online data about customers and, hence, why it is an important method for future service research.

Netnographic research can help service firms with, for example, service innovation, advertising and environmental scanning. This paper provides guidelines for service managers who want to use netnography as a market research tool.

Netnography has seen limited use in service research despite many promising applications in this field. This paper is the first to encourage and support service researchers in their use of the method and aims to stimulate interesting future netnographic service research.

The purpose of this paper is to investigate the impact of customer-focus on small medium enterprise (SME) performance from the perspective of a resource-based view (RBV).

This research study implemented a survey strategy to gather data from 255 respondents on the registered list of Ghana Enterprise Agency (GEA) in the eastern region of Ghana. Scales used to gather data were operationalized from previous research studies. A structural equation modeling (SEM) path analysis was used to estimate the impact of customer-focus on the performance of SMEs.

The outcomes of this study indicate that customer-focus has a significant positive impact on SME performance, hence backing the current demand for investigating the distinct influence of customer-focus on SME performance. The results show that customer-focus has a positive and significant relationship with financial performance, customer performance, internal business process performance and learning and growth performance, thus supporting the literature on the positive impact of customer-focus on SME performance. Therefore, customer-focus determinants used in this study, including co-creation, networking ties, customer insight and artificial intelligence marketing (AIM), are critical to the optimization of SME performance.

Notwithstanding the importance of this research study mentioned earlier, the study has limitations. Notably, the sample size of this study can be increased to capture SME respondents in other geographical zones that were not included in this study. Future research studies may address how business environment conditions moderate the relationship between customer focus and performance, and also the cause-effect of the relationship between customer focus and business environment conditions on SME performance.

The practical implications consist of two main items. First, this study empowers SME owners and managers to develop a customer focus technique as a central strategic goal in their quest for SME performance optimization. Second, SME owners and managers should progressively exploit the four determinants of customer focus which include co-creation, networking ties, customer insight and (AIM in order to accrue important resources for effective utilization of their customer focus competences as a way to enhance their performance.

This study is targeted at the sound development of SMEs to bring about poverty alleviation and employment. Poverty, unemployment and poor living standards are recognized as vital social challenges in most emerging economies. The establishment of customer focus as an important strategic capability provides opportunities for SME survival, profitability and growth.

Generally, the findings of this research study provide a strong backing to RBV perspective and the proposition that customer-focus and its determinants (i.e. co-creation, networking ties, customer insight and AIM) should be acknowledged as a vital strategic resource for optimizing the performance of SMEs. This research study also provides new knowledge contribution to the present body of knowledge on customer-focus orientation and management literature, particularly in the context of an emerging economy.

This article explores the emergence of organizational digital transformation in the rapidly advancing technological era. It discusses the origins, driving forces, strategies, challenges and broader implications.

The article employs a scoping review methodology that synthesizes knowledge from the existing literature, research, case studies and other relevant sources.

The findings underscore the pivotal role that organizational digital transformation plays in an era of relentless technological advancement. Leadership, organizational culture and technological enablers are identified as crucial drivers of innovation and competitiveness within organizations. The article also emphasizes ethics as a crucial element of digital transformation, focusing in particular on concerns about data privacy and the morality of artificial intelligence. Additionally, the author talks about anticipated future trends that are anticipated to influence the future of digital transformation, such as the growing influence of artificial intelligence, the trend toward hyper-personalization and the emergence of quantum computing.

The assessment has failed to provide recommendations for the actual implementation because it has mainly concentrated on conceptual and strategic aspects. Furthermore, it does not clearly define the criteria for choosing real-world examples, which limits the representation of the different industries, size ranges of organizations and outcomes associated with digital transformation.

The article stresses the significance of paying attention to the forces driving digital transformation while navigating ethical and societal concerns. In addition to highlighting the importance of anticipating future trends for strategic planning in the rapidly changing digital landscape, it emphasizes the advantages as incentives for organizations to invest in digital initiatives.

The investigation demonstrates how technology contributes to progress while posing complex ethical and change management issues. In light of increased connectivity, data analytics and artificial intelligence, it highlights the crucial need for societal adaptability and highlights the crucial role that cooperative human–machine coexistence plays in responsible development and transformative societal evolution.

The article stands out because it examines organizational digital transformation in-depth while considering its historical roots, ethical implications and future prospects. It is a priceless contribution to the field because real-world case studies and a scoping review provide a distinctive viewpoint and a comprehensive view of the effects of digital transformation on organizations and society.

The purpose of this study is to address two issues relevant to those managing product returns: (1) how customers perceive the returning process and assessing the extent that these perceptions have on satisfaction with the organization, loyalty and word-of-mouth (WOM) and (2) are these outcomes moderated by whether customer returns were planned or unplanned?

The data consisted of 21 semi-structured interviews (pilot study) and a quantitative survey (n = 384; main study) targeted at consumers who had bought fashion items online.

Qualitative insights revealed that perceptions of the returning experience are driven by monetary costs, convenience, stress and guilt. Quantitative findings showed that the returning experience explains return satisfaction for both planned and unplanned returners, and returning satisfaction explains overall satisfaction and WOM. The noteworthy difference concerns loyalty: although customers that planned to return items are more loyal to the organization, it is the unplanned returners whose loyalty can be significantly increased by better managing the returning process.

Returning products online is increasingly common and thus forms an important part of the customer's overall experience with an organization. Returns management can therefore drive key customer outcomes. Understanding the dynamics between the product return experience, return satisfaction and customer outcomes will help practitioners design and implement more informed returns management strategies. Measures are also presented that assess the cognitive and emotional aspects associated with returning products.

Returning products is an increasingly important challenge for online retailers. Understanding what kinds of returning behaviors occur allows companies to design and execute better informed decisions to manage this phenomenon, not only for the sake of firm performance but also for societal and environmental benefits – the triple bottom line.

While scholars have investigated the relationship between return policies (e.g. free vs fee) and profitability, no prior literature has examined the returning experience: how consumers perceive the returning process; motivations for their returns (whether returns were planned or not) and subsequent customer outcomes.

The purpose of this study is to provide an in-depth understanding of the B2B customers’ perspective regarding salespeople’s social media use.

The study adopts a qualitative approach based on semi-structured interviews with 26 key informants performing their job in customer role in various industries.

The authors inductively identify five themes regarding the B2B customers’ perspective of social media use in B2B selling. These themes allow for valuable implications for social selling activities and expected outcomes.

Against a growing body of literature on drivers, best practices and outcomes of social media use by B2B salespeople, less attention has been paid to the customer’s side. The authors extend current research by providing a more complete picture of social selling activities and expected outcomes.

This exploratory study examines and comprehends the relative importance of mobile app attributes from a consumer perspective. Both quantitative and qualitative analysis approaches explore users' behavior and attitudes toward the priorities of mobile app attributes and preferences, identifying correlations between attributes and aggregating individual attributes into groups.

Online convenience sampling and snowball sampling resulted in 417 valid responses. The numerical data are analyzed using the relative to an identified distribution (RIDIT) scoring system and gray relational analysis (GRA), and qualitative responses are investigated using text-mining techniques.

This study finds enhanced nuances of user preferences and provides data-driven insights that might help app developers and marketers create a distinct app that will add value to consumers. The latent semantic analysis indicates relationship structure among the attributes, and text-based cluster analysis determines the subsets of attributes that represent the unique functions of the mobile app.

This study reveals the essential components of mobile apps, paying particular attention to the consumer value component, which boosts user approval and encourages prolonged use. Overall, the results demonstrate that developers must concentrate on its functional, technical and esthetic features to make an app more exciting and practical for potential users.

Most scholarly research on apps has focused on their technological merits, aesthetics and usability from the user's perspective. A post-adoption multi-attribute app analysis using both structured and unstructured data is conducted in this study.

As the tourism industry becomes more vital for the success of many economies around the world, the importance of technology in tourism grows daily. Alongside increasing tourism importance and popularity, the amount of significant data grows, too. On daily basis, millions of people write their opinions, suggestions and views about accommodation, services, and much more on various websites. Well-processed and filtered data can provide a lot of useful information that can be used for making tourists' experiences much better and help us decide when selecting a hotel or a restaurant. Thus, the purpose of this study is to explore machine and deep learning models for predicting sentiment and rating from tourist reviews.

This paper used machine learning models such as Naïve Bayes, support vector machines (SVM), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (BiLSTM) for extracting sentiment and ratings from tourist reviews. These models were trained to classify reviews into positive, negative, or neutral sentiment, and into one to five grades or stars. Data used for training the models were gathered from TripAdvisor, the world's largest travel platform. The models based on multinomial Naïve Bayes (MNB) and SVM were trained using the term frequency-inverse document frequency (TF-IDF) for word representations while deep learning models were trained using global vectors (GloVe) for word representation. The results from testing these models are presented, compared and discussed.

The performance of machine and learning models achieved high accuracy in predicting positive, negative, or neutral sentiments and ratings from tourist reviews. The optimal model architecture for both classification tasks was a deep learning model based on BiLSTM. The study’s results confirmed that deep learning models are more efficient and accurate than machine learning algorithms.

The proposed models allow for forecasting the number of tourist arrivals and expenditure, gaining insights into the tourists' profiles, improving overall customer experience, and upgrading marketing strategies. Different service sectors can use the implemented models to get insights into customer satisfaction with the products and services as well as to predict the opinions given a particular context.

This study developed and compared different machine learning models for classifying customer reviews as positive, negative, or neutral, as well as predicting ratings with one to five stars based on a TripAdvisor hotel reviews dataset that contains 20,491 unique hotel reviews.

This paper aims to develop a theoretical framework for the conceptualization of customer experiential knowledge (CEK) by logically combining its different dimensions into one coherent explanatory concept. Drawing on the integration of the literature on customer experience, customer knowledge management and customer insights acquisition, supported by adequate empirical evidence, the framework provides a systematic, comprehensive and accurate understanding of CEK which, could contribute to the identification of relevant customer experience insights useful for customer knowledge management.

The analysis follows an inductive/deductive interpretative approach and it is based on a netnography of specialty coffee bloggers’ narratives in relation to their sustainability practices.

The paper identifies the following six types of CEK: normative, subcultural, epicurean, transcendental, subcultural and symbolic. Accordingly, CEK is defined as the knowledge tacitly possessed by customers in relation to how they live their consumption experiences according to a body of heterogeneous socio-cultural contextual factors (ethos, norms and symbols) and subjective influences (emotions, ingenuity, instincts and senses) deeply embedded into the narrative of a consumption experience.

While CEK has been largely observed and acknowledged, it has not been yet adequately addressed by existing research. The provision of a conceptual definition of CEK which emphasizes its different dimensions will be of use to both academics and practitioners to better identify and categorize the different manifestations of CEK when undertaking empirical observations or managerial decisions.

The purpose of this paper is to examine how deeper psychosocial structures can be examined utilising a contemporary provocative theory within workplace reflection to generate more radical insights and innovation.

This paper outlines a provocative theory and then presents case examples of how deeper structures can be examined at the micro, meso and macro levels.

Deeper psychosocial structures are the forces that keep the status quo firmly in place, but deeper examination of these structures enable radical insights and therefore the possibility of innovation.

Deep psychosocial structures shape and constitute daily action, and so work-based and practitioner researchers can be tricked into thinking they have identified new ways of working, but may be demonstrating the same workplace behaviours/outcomes. Workplace behaviours, including emotional responses to apparent change, are key indicators of deeper structures.

Ideas and processes for examining deeper structures can be integrated into daily reflective practices by individuals, within organisational processes, and wider, system processes. However, because deeper structures can appear in different forms, we can be tricked into reproducing old structures.

Examining deeper structures increases the possibilities for more radical insights into workplace structures, and therefore, how to potentially mobilise innovations which may better serve people and planet.

This paper is the first to examine the work of Slavoj Žižek in the context of work-based learning.

Due to the recent development of Big Data and artificial intelligence (AI) technology solutions in customer relationship management (CRM), this paper provides a systematic overview of the field, thus unveiling gaps and providing promising paths for future research.

A total of 212 peer-reviewed articles published between 1989 and 2020 were extracted from the Scopus database, and 2 bibliometric techniques were used: bibliographic coupling and keywords’ co-occurrence.

Outcomes of the bibliometric analysis enabled the authors to identify three main subfields of the AI literature within the CRM domain (Big Data and CRM as a database, AI and machine learning techniques applied to CRM activities and strategic management of AI–CRM integrations) and capture promising paths for future development for each of these subfields. This study also develops a three-step conceptual model for AI implementation in CRM, which can support, on one hand, scholars in further deepening the knowledge in this field and, on the other hand, managers in planning an appropriate and coherent strategy.

To the best of the authors’ knowledge, this study is the first to systematise and discuss the literature regarding the relationship between AI and CRM based on bibliometric analysis. Thus, both academics and practitioners can benefit from the study, as it unveils recent important directions in CRM management research and practices.