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Marketing professionals are under pressure to implement methods and metrics that demonstrate the value of the function. This paper aims to propose a model to measure marketing performance, focusing on four categories of metrics and two types of factors that influence the effectiveness of the assessment process.

The paper is organized in three parts. The first part includes a synthesis of the theoretical background on the subject. Next, the rationale and architecture of the model are presented, together with an explanation of the elements that compose it. A reflection on the work developed is presented in the last section.

Benefits regarding how to best assess marketing practice are considerable, as organizations with effective performance measurement systems tend to show better results than others. In this context, the choice of metrics is important, but it is also necessary to understand the mechanisms through which the effectiveness of the measurement process can be improved.

Literature has mainly focused attention on the effect of individual programs on specific measures or on conceptual models that do not sufficiently address all major elements in the marketing assessment process. This work extends previous contributions on the subject, presenting a model that combines metrics with factors underlying the measurement process.

The purpose of this study is to revisit brand performance metrics (BPMs) (brand affinity, brand content and knowledge, brand image, brand ethics and brand value) and evaluate the moderated mediation effect of relationship quality (mediator) and relationship duration (moderator) in brand performance and customer loyalty relationship in an Indian banking context.

The research model was tested in the Indian banking sector. The primary data was collected from the 1,000 account holders of five Indian public and private banks. The data was analysed and validated using exploratory factor analysis and confirmatory factor analysis. Structural equation modelling and the Hayes process were used for testing the hypotheses.

The study results established BPMs as a four-dimensional structure comprising brand affinity, brand content and knowledge, brand image, brand ethics and brand value. The BPMs significantly positively impact relational quality (RQ) and customer loyalty. Further results also prove the existence of moderated mediation effect on BPMs and customer loyalty link and portray that the impact of BPMs on customer loyalty is mediated by the RQ and influenced by relationship duration.

The study is confined to the Indian banking sector. It did not examine the dimension-wise impact of brand performance indicators on RQ and customer loyalty. Future research is required to explore their influence in banking and other sectors.

The study findings suggest that to enhance brand performance, banks need to follow excellence in every conduct, take immediate actions against inappropriate behaviour, consistently update their relevant and valuable contents (news, videos, white papers, e-books, case studies, FAQ’s, photos, etc.) on their websites and also introduce loyalty schemes to reimburse customers’ interests with some substantial benefits such as rebates, discounts, annual gifts and extraordinary or additional services. These strategies can pave the way for enhancing long-term quality relationships between customers and their service providers and increasing customer loyalty.

To the best of the authors’ knowledge, the study is a maiden attempt to assess the effect of BPMs on customer loyalty in the presence of RQ and at the value of relationship duration/length. Besides, the study results also prove the existence of moderated mediation effect and portray that the impact of customer equity and relational benefits on customer loyalty is influenced by relationship duration and mediated by RQ.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

This paper aims to examine the direct impacts of marketing resources and marketing activities on several business performance indicators in the banking industry and the indirect effects through customer-based brand equity.

We use a holistic empirical approach based on resource-based view and marketing productivity chain. The main study consists of a secondary analysis using quarterly data of fourteen banks over four years. We analyze the data using fixed-effect panel data regression, namely seemingly unrelated regressions.

We find that customer-based brand equity is one of the most influential factors on business performance. Moreover, the indirect effect through customer-based brand equity should be considered in improving business performance. Marketing-related financial resources positively impact customer-based brand equity and business performance. Regarding marketing activities, pricing strategies affect the bank preferences of customers, which in turn affect the growth of deposit volumes and churn rates. Additionally, the number of bank branches positively impacts business performance. Advertising spending on different media has differentiated impacts on the performance indicators; thus, the allocation of advertising budget and advertising planning are critical.

This study examines the inter-relationships among marketing resources, marketing activities, consumer response through brand equity and marketing performance. This study contributes to the literature by integrating the resource-based view and the marketing productivity chain to analyze the inter-relationships using panel data and several sector-related metrics. This study provides valuable insights to decision-makers in the banking industry.

The research aims to determine the directions for developing marketing strategies of state commercial banks based on quantitative metrics for evaluating the effectiveness of the marketing activities of banks adapted for the banking sector. During the research, the author applied the methods of terminological, structural, and logical analysis, analogy, synthesis, system analysis, methods of generalization and grouping, table and graphic methods, economic and statistical methods, and methods of dynamic and logical analysis. The result of this research is the systematization of existing theoretical and methodological approaches to calculating the effectiveness of marketing activities in a bank, as well as the calculation of the author's methodology for assessing the main, innovative, and total effectiveness of marketing activities in leading Russian banks. The methodology for assessing the quantitative indicators of a bank's marketing effectiveness has been improved, which consists of the indicators of the effectiveness of fixed costs for marketing in the bank and considers unforeseen expenses caused by the bank's innovative activity related to challenges of time and digital transformation.

The study aims to propose an instrument for measuring product-centeredness (i.e. the extent to which comment content is related to a product) using word embedding techniques as well as explore its determinants.

The study collected branded posts from 205 Instagram influencers and empirically examined how four factors (i.e. authenticity, vividness, coolness and influencer–product congruence) influence the content of the comments on branded posts.

Post authenticity and congruence are shown to have positive effects on product-centeredness. The interaction between coolness and authenticity is also significant. The number of comments or likes on branded posts is not correlated with product-centeredness.

In social media influencer marketing, volume-based metrics such as the numbers of likes and comments have been researched and applied extensively. However, content-based metrics are urgently needed, as fans may ignore brands and focus on influencers. The proposed instrument for assessing comment content enables marketers to construct content-based metrics. Additionally, the authors' findings enhance the understanding of social media users' engagement behaviors.

This article aims to provide greater clarity regarding the conceptualization and critical role of digital marketing ecosystems for the global expansion of multinational enterprises (MNEs) and offer novel research directions to prompt future research.

The authors first review the marketing literature related to marketing ecosystems, highlighting the evolution of this body of work across a range of domains such as services, innovation and new product development, communications and marketing strategy more broadly. Next, two case examples of MNEs whose global expansion efforts have been supported by their marketing ecosystems are used to highlight the role of marketing ecosystems in global market expansion. Finally, novel research directions are offered to prompt future research and provide greater insight into this emerging area.

The case examples we examine yield important insights into the role of marketing ecosystems for MNEs expanding from emerging markets (EMs) to developed markets (DMs). EM-MNEs such as TEMU face more communication and payment ecosystem challenges while opening their supply chain to DMs. Contrary to EM-MNEs, DM-MNEs face institutional and sociocultural challenges that require different marketing ecosystem orchestration approaches.

Marketing ecosystems can provide MNEs with greater multinational flexibility, enabling them to adapt their global strategies to navigate increasing complexities in global markets, such as trends toward increased protectionism and geopolitical disruptions. However, there is surprisingly little research addressing this issue.

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.

Technology 4.0 comes with a challenge to understand the degree of users’ willingness to adopt a digital transformation. Metaverse, being a digital transformation, enables real-world activities in the virtual environment, which attracts organizations to adopt the new fascinating technology. This paper thus explores the uses and gratification factors affecting user adoption and recommendation of metaverse from the management perspective.

The study adopts a mixed approach where structural topic modeling is used to analyze tweets about the metaverse, and the themes uncovered from structural topic modeling were further analyzed through data collection using structural equation modeling.

The analyses revealed that social interaction, escapism, convenient navigability, and telepresence significantly affect adoption intent and recommendation to use metaverse, while the trendiness showed insignificance. In the metaverse, users can embody avatars or digital representations, users can express themselves, communicate nonverbally, and interact with others in a more natural and intuitive manner.

This paper contributes to research as it is the first of its kind to explore the factors affecting adoption intent and recommendation to use metaverse using Uses and Gratification theory in a mixed approach. Moreover, the authors performed a two-step study involving both qualitative and quantitative techniques, giving a new perspective to the metaverse-related study.

Considering the emergence of e-commerce platforms and their integration into marketing channels, this paper aims to investigate how artificial intelligence (AI) resources – both technological and human – possessed by e-commerce platforms can enhance their channel power by acquiring market-based assets (relational and intellectual).

Based on resource-based theory and resource orchestration theory, the authors developed a framework tested using survey data gathered from the sellers, which incorporated six key variables: the e-commerce platform’s AI technology resources and human resources, rational and intellectual market-based assets, intraplatform competition and channel power. The analyses are performed using the regression analysis technique.

The empirical findings indicate that both technological and human AI resources are crucial in building channel power. In addition, market-based assets serve as a mediator in this relationship, while intraplatform competition moderates the effect of intellectual market-based assets on channel power negatively.

This study contributes to the existing literature by exploring how e-commerce platforms’ AI resources affect their channel power. The results offer valuable guidance to managers and researchers on optimizing AI resources to improve channel power.