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The aim of this study is to empirically investigate the impact of marketing analytics capability on business performance from the perspective of RBV theory.

This study used a survey method to gather information from 225 food processing SMEs registered with the Ghana Enterprise Agency (GEA) in Ghana’s eastern region. A structural equation modeling (SEM) path analysis was used to assess the impact of marketing analytics capability (MAC) on the performance of SMEs.

The results of the study show that MAC significantly and positively affect the financial performance (FP), customer performance (CF), internal business process performance (IBPP) and learning and growth performance (LGP) of Ghanaian SMEs. The findings of this study also illustrated the significance of MAC determinants, including marketing analytics skills (MAS), data resource management (DRM) and data processing capabilities (DPC), in achieving SME success in Ghana.

The research’s conclusions give RBV theory strong credence. The results of this study also provide credence to previous research finding that SMEs should view MAC and its determinants (i.e. DRM, DPC, MAS) as a crucial strategic capability to improve their performance (i.e. FP, CF, IBPP, LGP). With regard to its contribution, this study broadens the body of knowledge on MAC and SME performance, particularly in the context of an emerging economy.

Smartphone apps collect users' personal information, which triggers privacy concerns for app users. Consequently, app users restrict apps from accessing their personal information. This may impact the effectiveness of in-app advertising. However, research has not yet demonstrated what factors impact app users' decisions to use apps with restricted permissions. This study is aimed to bridge this gap.

Using a quantitative research method, the authors collected the data from 384 app users via a structured questionnaire. The data were analysed using AMOS and fuzzy-set qualitative comparative analysis (fsQCA).

The findings suggest privacy concerns and risks have a significant positive effect on app usage with restricted permissions, whilst reputation, trust and perceived benefits have significant negative impact on it. Some app-related factors, such as the number of apps installed and type of apps, also impact app usage with restricted permissions.

Based on the findings, the authors provided several implications for app stores, app developers and app marketers.

This study examines the factors that influence smartphone users' decisions to use apps with restricted permission requests. By doing this, the authors' study contributes to the consumer behaviour literature in the context of smartphone app usage. Also, by explaining the underlying mechanisms through which the principles of communication privacy management theory operate in smartphone app context, the authors' research contributes to the communication privacy management theory.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

This study aims to reveal the knowledge structure of social media influencer marketing literature by performing science mapping analysis through a state-of-the-art bibliometric approach to determine the current and future trends. Social media influencer marketing is one of the most effective approaches to presenting a brand and offering value to consumers via social media.

This study evaluates the knowledge structure to uncover the emerging trends and future predictions in social media influencer marketing through bibliographic coupling and co-word analysis. In total, 917 journal publications were retrieved from the Web of Science database and analyzed using VOSviewer software.

The central theme in social media influencer marketing reflects digital engagement between influencers and followers and communication between influencers and followers. The theoretical and managerial implications are discussed.

This study unleashes the knowledge structure according to the fundamental literature of social media influencer marketing and the underlying themes related to the phenomenon.

High failure rates characterise the experience of new entrepreneurial ventures in Nigeria and other emerging economies. Reliance on strategic tools such as entrepreneurial orientation (EO) is critical to the growth and survival of new ventures. This empirical study aims to deepen the understanding of the relationship between EO and performance of new venture logistics firms in Nigeria. It further explores the contingent effects of social capital and marketing capabilities on the hypothesised direct relationships from a transport industry perspective.

Managers of 650 new venture logistics service providers in selected Nigerian cities were Web-surveyed. Exploratory and confirmatory factor analyses were performed. Regression analysis was further performed. Common method variance and other validity checks were assessed.

The 469 valid responses showed a positive relationship between EO and new venture performance (NVP). Social capital and marketing capabilities positively moderate the direct relationship between EO and NVP. Managerial implications suggest that context-specific dynamics must be considered when making strategic EO decisions to aid firm growth and survival.

This study directly responds to the contingency approach recommendation of past studies (Anwar et al., 2022; Van Stel et al., 2021; Covin and Wales, 2019) using the logistics service and emerging economy context. It also introduces social capital and marketing capabilities as moderators.

The study investigates the influence of ChatGPT on the labor market dynamics, aiming to provide a structured understanding of the changes induced by generative AI technologies.

An analysis of existing literature serves as the foundation for understanding the impact, while the supply and demand model helps assess the effects of ChatGPT. A text-mining approach is utilized to analyze the International Standard Occupation Classification, identifying occupations most susceptible to disruption by ChatGPT.

The study reveals that 32.8% of occupations could be fully impacted by ChatGPT, while 36.5% might experience a partial impact and 30.7% are likely to remain unaffected.

While this study offers insights into the potential influence of ChatGPT and other generative AI services on the labor market, it is essential to note that these findings represent potential implications rather than realized labor market effects. Further research is needed to track actual changes in employment patterns and job market dynamics where these AI services are widely adopted.

This paper contributes to the field by systematically categorizing the level of impact on different occupations, providing a nuanced perspective on the short- and long-term implications of ChatGPT and similar generative AI services on the labor market.

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 paper is to provide an integrative picture of the state of the art of the literature on digitalisation of small and medium-sized enterprises (SMEs) as an enabler for their internationalisation process and as a comprehensive view of the specific domains impacted by digital technologies as well as their repercussions on the international outreach.

A systematic review which leverages a descriptive analysis of extant literature and an axial coding technique has been conducted to shed light on the current knowledge and to identify primary research areas and future research lines.

The research indicates that digitalisation impacts the internationalisation of SMEs in three specific domains: (1) internationalisation through the adoption of information and communication technologies (ICT) technologies and e-commerce platforms; (2) international expansion through the digitalisation of value chain activities and (3) international outreach through knowledge acquisition on digital platforms.

The value of this study is threefold. First, the authors attempt to systematically review the literature on SMEs digitalisation and internationalisation and provide a holistic perspective on the intertwining of these two research streams. Second, the authors propose a novel conceptualisation on the dimensions of SMEs digitalisation as enablers to internationalisation. Third, the authors put forward promising future lines of research.

1. Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

2. Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

3. Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

4. Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

5. SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

Digitalisation represents a pivotal strategy that allows companies to build new strategic capabilities and is a propeller for SMEs internationalisation.

Through e-commerce, SMEs could compete at the same level of multinational companies but enduring lower costs of expansion.

Digital platforms allow SMEs to enhance the learning processes about international markets through an immediate access to relevant information.

Digital entrepreneurship has enabled SMEs to develop new configurations of value chain activities, evolving their business model or reaching new markets.

SMEs are changing the “business as usual” paradigm offering digital tools to build modular architectures that are scalable and agile in their evolution ability.

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.