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This paper aims to introduce the experience-dominant (Ex-D) logic model, which synthesizes the creation, perceptions and outcomes of Ex-D logic. It is designed to offer valuable insights for strategic managerial applications and future research directions.

Employing a qualitative approach by using eight selected product launch events from reviewed 100 event videos and 55 in-depth interviews with industrial managers to develop an Ex-D logic model, and data were coded and analysed via NVivo.

Results show that the firm’s Ex-D logic is operationalized as the mentalizing of the three types of customer needs (service competence, hedonic excitations and meaning making), the materializing of three types of customer experiences and customer journeys (service experience, hedonic experience and brand experience) and the moderating of three types of customer values (service values, hedonic values and brand values).

This study has implications for adding new insights into existing theory on dominant logic and customer experience management and also offers actionable recommendations for managerial applications.

This study sheds light on the importance of Ex-D logic from a strategic point of view and provides an organic view of the firm. It distinguishes firm perspective from customer perspective, firm experience from customer experience and firm journey from consumer journey.

This research seeks to understand whether employer branding (EB) and internal marketing (IM) are fundamental to the challenge of attracting and retaining talent and how these strategies can help companies to overcome the difficulties related to the talent shortage, from the perspective of a SME Portuguese metallurgical industry.

The research resorts to a case study of a qualitative nature, through a semi-structured interview with the head of the human resources (HR) training and development area of the Navarra Group, and quantitative, through surveys to its employees. Based on the literature, a conceptual model was constructed, whose application allowed us to perceive the relationships between the practices of EB and IM; satisfaction, motivation and commitment; attraction and retention.

The exploratory interview concluded that organizations consider EB and IM essential for an effective talent management strategy. The quantitative results demonstrate that IM and EB practices implemented in the organization contribute to the satisfaction, motivation and involvement of employees, which results in a decrease in the intention to leave. It is also noted that these practices promote an increase in the perception of organizational attractiveness, which represents a positive impact on its ability to attract.

From a theoretical perspective, the research contributes to the development of knowledge about IM, EB and talent management, providing relevant data that can help define the best strategies for attraction and retention, from the point of view of IM and EB.

The research presents preliminary insights that can be an auxiliary tool for HR managers and professionals in the context of industrial SMEs.

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

The study investigates the mediated moderation impact of Memorable Religious Experience (MRE) and Religiosity (REL) on the relationship between Memorable Tourism Experience (MTE) and Attitude towards Pilgrimage (ATT) finally driving Recommend Intention (RCI) of visitors to a religious site. It suggests visitors' incentive variable religiosity can influence their decision to recommend visiting a religious destination.

The research uses a quantitative cross-sectional approach wherein a self-administered survey was used for data collection from 223 pilgrims who visited a popular pilgrimage site. Partial least squares-structural equation modeling (PLS-SEM) was employed for analysis.

The results showed that MTE has a significant influence on ATT which further influences RCI (a dimension of behavioral intention-BI) of visitors towards a religious destination. Further, MRE mediates the relationship between MTE and ATT. Nevertheless, REL illustrated a significant moderation influence on the relationship between MRE and ATT, further verifying the mediated moderation impact of MRE and REL in the model.

Recommendation of existing customers is one of the most powerful indicators of customer loyalty and usually leads to revisit. The research provides destination managers/tourism planners of pilgrimage sites to formulate appropriate marketing strategies to develop RCI and sustainable branding.

This study adds to the empirical studies conducted on REL by constructing a composite picture of the memorable tourism experience within a pilgrimage tourism context. The uniqueness lies in the attempt to investigate the mediated moderation impact of MRE and REL using a symmetric (PLS-SEM) approach.

This paper aims to investigate the role of field-configuring events (FCEs) and situational context in the institutionalisation of sustainability reporting (SR) in Pakistan.

This paper uses insights from the institutional logics perspective and qualitative research design to analyse the interplay of the institutional logics, FCEs, situational context and social actors’ agency for the institutionalisation of SR among leading corporations in Pakistan. A total of 28 semi-structured interviews were carried out and were supplemented by analysis of secondary data including reports, newspaper articles and books.

The emerging field of SR in Pakistan is shaped by societal institutions, where key social actors (regulators, enablers and reporters) were involved in the institutionalisation of SR through FCEs. FCEs provided space for agency and were intentionally designed by key social actors to promote SR in Pakistan. The situational context connected the case organisations with FCEs and field-level institutional logics that shaped their decision to initiate SR. Overall, intricate interplay of institutional logics, FCEs, situational context and social actors’ agency has contributed to the institutionalisation of SR in Pakistan. Corporate managers navigated institutional logics based on situational context and initiated SR that is aligned with corporate goals and stakeholder expectations.

For corporate managers, this paper highlights the role of active agency in navigating and integrating institutional logics and stakeholders’ expectations in their decision-making process. For practitioners and policymakers, this paper highlights the importance of FCEs and situational context in the emergence and institutionalisation of SR in developing countries. From a societal point of view, dominance of business actors in FCEs highlights the need for non-business actors to participate in FCEs to shape logics and practice of SR for wider societal benefits.

From a societal point of view, dominance of business actors in FCEs highlights the need for non-business actors to participate in FCEs to shape logics and practice of SR for wider societal benefits.

This paper focuses on the role of FCEs and situational context as key social mechanisms for explaining the institutionalisation of SR.

ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

The purpose of this study is to depict the value added by digital transformation to intellectual capital (IC) by virtue of the studies reached by the literature review on different databases are examined.

Journal of Intellectual Capital, which has the highest number of records from the resources included in the “Web of Science” content and covering the title of “intellectual capital” has been selected in this study. Research using bibliometric analysis has been conducted and it has been determined that the terms “digital transformation” and “intellectual capital” should be searched for simultaneously in each and every article published in the journal between the years 1975 and 2022.

A bibliometric analysis and citation mapping process are carried out considering all dimensions to reach the results and interpretation of findings. VOSviewer is used to visualize the bibliometric networks of results and findings in the form of scientific mapping, as well as to visualize the co-authorship analysis of keywords, co-authorship analysis and citation networks.

Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. This circumstance is elaborately described as a limitation of this study. Bibliometric analysis is a method that can be used to evaluate the performance of a single journal. However, it is important to note that bibliometric analysis has some limitations when it comes to assessing the validity of a single journal. One limitation is that bibliometric analysis is based on quantitative metrics, such as citation counts, which do not take into account the quality of the research. Therefore, bibliometric analysis alone may not provide a complete picture of the validity of a single journal. In addition, bibliometric analysis is based on the number of times a paper is cited, which can be influenced by factors such as the prestige of the journal, the field of research and the time since the publication. In conclusion, bibliometric analysis can be used to evaluate the performance of a single journal, but it is important to consider its limitations.

This study identified contributions, gaps and limits based on the results of a bibliometric analysis. Italy is the most influential country and the issue is structured around four clusters: IC; digital transformation; human capital; and knowledge management. As previously unexplored issues are addressed in an innovative manner, it is acceptable to underline the paper’s originality.