Purpose: The purpose of this study is to explore the negative consequences of human resource analytics applications using the privacy calculus approach.Design/methodology/approach: By using the existing literature and privacy calculus theory, a theoretical model has been developed. This model helps to examine the benefits and risks associated with HR analytics applications. The theoretical model was validated using the partial least square structural equation modeling (PLS-SEM) technique with 315 respondents from different organizations.Findings: HR analytics provides multiple benefits to employees and organizations. But employee privacy may be compromised due to unauthorized access to employee data. There are also security concerns about the uncontrolled use of these applications. Tracking employees without their consent increases the risk. The study suggests that appropriate regulation is necessary for using HR analytics.Research limitations/implications: This study is based on cross-sectional data from a specific region. A longitudinal study would have provided more comprehensive results. This study considers five predictors, including other boundary conditions that could enhance the model’s explanative power. Also, data from other countries could improve the proposed model.Practical implications: The proposed model is useful for HR practitioners and other policymakers in organizations. Appropriate regulations are important for HR analytics applications. The study also highlights various employee privacy and security-related issues emerging from HR analytics applications. The study also discusses the role of leadership support for the appropriate usage of HR analytics.Originality/value: Only a few research studies have explored the issues of HR analytics and its consequences. The proposed theoretical model is the first to consider the negative consequence of HR analytics through privacy calculus theory. In this perspective, the research is considered to be novel.

Purpose: This paper aims to advance the international marketing debate by presenting the results of a structured literature review (SLR) focusing on Big Data implementation in customer relationship management (CRM) strategizing. It outlines past and present literature and frames a future research agenda.Design/methodology/approach: The research analyzes papers published in journals from 2013 to 2020, deriving significant insights about Big Data applications in CRM. A sample of 48 articles indexed at Scopus was preliminarily submitted for bibliometric analysis. Finally, 46 papers were analyzed with content and a bibliometric analysis to identify areas of thematic specializations.Findings: The paper presents a conceptual multilevel framework demonstrating areas of specialization emerging from the literature. The framework is built around four coordinated sequences of actions relevant to “why,” “what,” “who” and “how” Big Data is implemented in CRM strategies, thus supporting the conception and implementation of an internationalization marketing strategy.Research limitations/implications: Implications for the development of the future research agenda on international marketing arise from the comprehension of Big Data in CRM strategy.Originality/value: The paper provides a comprehensive SLR of the articles dealing with models and processes of Big Data for CRM from an international marketing perspective. Despite these issues' relevance and the increasing literature focused on them, research in this area is still fragmented and underexplored, requiring more systematic and holistic studies.

Purpose: This paper explores critical failure factors (CFFs) in the context of knowledge sharing. It provides further insights into what can cause knowledge- sharing failures, inflexible knowledge-sharing strategies and ineffective knowledge- sharing mechanisms. It also examines how practitioners can reduce or even mitigate such dysfunctions.Design/methodology/approach: A case-based inductive approach was conducted. Data were collected from two studies applying mixed methods. The first data set included nine in-depth, semi-structured interviews with highly skilled personnel from an aerospace and defense organization. The second data source included 375 successfully completed questionnaires from participants employed at the same organization.Findings: The paper identifies six CFFs with an impact on knowledge sharing. It also reveals that managing organizational ignorance can play a key role in generating new knowledge and averting failure. Study findings provide insights into the importance of identifying these failures when sharing knowledge and propose relevant mitigation strategies.Originality/value: This paper identifies a range of empirically validated CFFs that complement the extant work on the complexity of knowledge sharing and have hitherto not been seen in the literature. It also provides a more nuanced understanding of why both organizations and their people often fail to share knowledge by exploring the role of organizational ignorance.

The study looks at the impact that the three dimensions of financial inclusion (FI) (i.e., access, usage, quality) may have on poverty alleviation. In doing so, the study relies on demand and supply-side data to measure Nigeria’s FI. The demand-side data were derived from the 2021 Global Findex data, and the supply side data were sourced from the IMF Access survey database (2004–2021). The supply-side data were analysed using the ordinary least squares regression (OLS), while the demand-side data were analysed using the probit regression model. The study outcomes revealed a negative and significant relationship between financial access and poverty rate, further indicating that those who use financial services are less likely to experience poverty. The study recommends that financial service providers tailor their financial products to align with the educational level of the target population to encourage savings.