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The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”. Big data platforms are shaping the processes of production, labour, the price of products and market conditions. “Digital platforms” and “big data” have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by “platforms” and “big data”.

As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the “Marxist theory of capital accumulation” in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of “Theory Synthesis and Theory Adaptation” as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism.

This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation.

This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future.

Due to its high leverage nature, a bank suffers vitally from the credit risk it inherently bears. As a result, managing credit is the ultimate responsibility of a bank. In this chapter, we examine how efficiently banks manage their credit risk via a powerful tool used widely in the decision/management science area called data envelopment analysis (DEA). Among various existing versions, our DEA is a two-stage, dynamic model that captures how each bank performs relative to its peer banks in terms of value creation and credit risk control. Using data from the largest 22 banks in the United States over the period of 1996 till 2013, we have identified leading banks such as First Bank systems and Bank of New York Mellon before and after mergers and acquisitions, respectively. With the goal of preventing financial crises such as the one that occurred in 2008, a conceptual model of credit risk reduction and management (CRR&M) is proposed in the final section of this study. Discussions on strategy formulations at both the individual bank level and the national level are provided. With the help of our two-stage DEA-based decision support systems and CRR&M-driven strategies, policy/decision-makers in a banking sector can identify improvement opportunities regarding value creation and risk mitigation. The effective tool and procedures presented in this work will help banks worldwide manage the unknown and become more resilient to potential credit crises in the 21st century.

The purpose of this chapter is to cover banking efficiency using the concept of the Meta frontier function and to study group and subgroup differences in the production technology. This study estimates the technical efficiency (TE) and technology gap ratios (TGRs) for banks in Islamic countries. Using the assumption of the convex hull of the Meta frontier production set using the virtual Meta frontier within the nonparametric approach as presented by Battese and Rao (2002), Battese et al. (2004), and O'Donnell et al. (2007, 2008) and after relaxing this assumption, the study investigates if there is a significant difference between these two methods. To overcome the deterministic criterion addressed to nonparametric approach, the bootstrapping technique has been applied. The first part of this chapter covers the analytical framework necessary for the definition of a Meta frontier function and its estimation using nonparametric data envelopment analysis (DEA) in the case where we impose the assumption of the convex production set and follows in the case of relaxation of this assumption. Then we estimated the TE and the TGR in concave and nonconcave Meta frontier cases by applying the Bootstrap-DEA approach. The empirical part will be reserved for highlighting these methods on data bank to study the technical and technological performance level and prove if there is a difference between the two methods. Three groups of banks namely commercial, investment, and Islamic banks in 17 Islamic countries over a period of 16 years between 1996 and 2011 are used.

This paper aims to examine the level of economic efficiency and factors that influence economic efficiency among seed potato producers in South-western Uganda.

The paper analyses the economic efficiency of 499 informal and 137 formal seed producers using primary data collected through a structured questionnaire. A multi-stage sampling technique was used to select the study sites and specific farmers. A one-step estimation procedure of normalized translog cost frontier and inefficiency model was employed to determine the level of economic efficiency and the influencing factors.

The results showed that mean economic efficiencies were 91.7 and 95.2% for informal and formal seed potato producers, respectively. Furthermore, results show significant differences between formal and informal seed potato producers in economic efficiency at a one percent level. Market information access, credit access, producers' capacity and experience increase the efficiency of informal while number of potato varieties, market information access and producers' experience increase economic efficiency for formal counterparts.

Most seed potato producers, especially the informal ones do not keep comprehensive records of their production and marketing activities. This required more probing as answers depended on memory recall.

Future research could explore panel data approach involving more cropping seasons with time variant economic efficiency and individual unobservable characteristics that may influence farmers' efficiency to validate the current findings.

The paper shows that there is more potential for seed potato producers to increase their economic efficiency given the available technology. This has a direct implication on the economy through increased investment in the production and promotion of high yielding seed potato varieties to meet the growing national demand for potatoes.

The paper bridges the gap in literature on economic efficiency among seed potato producers, specifically in applying the normalized translog cost frontier approach in estimating economic efficiency in the context of potato sub-sector in Uganda.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2021-0641

This study investigates the effect of intellectual capital (IC) and its components on the technical efficiency of Indian commercial banks after controlling the influence of bank-specific and macroeconomic variables.

The study selects a sample of 37 listed Indian commercial banks from 2005 to 2019 and uses the two-step data envelopment analysis (DEA) approach. Banks' technical efficiency scores are first estimated, while the relationship between IC and technical efficiency is examined in the second stage using the panel data Tobit model.

This study's findings suggest a fluctuating trend in the technical efficiency of Indian banks. Notably, from 2015 onwards, a declining technical efficiency trend is observed for all banks. However, private-sector banks outperform public-sector banks in terms of technical efficiency. This study's regression analysis indicates a positive relationship between IC and banks' technical efficiency scores. Further, by decomposing IC into its components like human capital, structural capital and capital employed, the study's findings show that human capital and structural capital enhance banks' technical efficiency. Notably, capital employed reduces technical efficiency. Moreover, bank size, diversification, capitalization, net interest margin and the country's growth rate significantly drive Indian banks' efficiency. In contrast, their operating cost ratio and the country's inflation negatively influence the same.

This study makes a novel endeavor to examine the IC and bank's technical efficiency nexus in the Indian context, encompassing a period of landmark banking reforms.

This study investigates the role of market concentration and efficiency in banking system stability during the COVID-19 pandemic. We empirically test the hypothesis that market concentration and efficiency are significant determinants of bank performance and stability during the time of crises, using a sample of 575 banks in 20 countries in the Middle East and North Africa (MENA).

The main sources of bank data are the BankScope and BankFocus (Bureau van Dijk) databases, World Bank development indicators, and official websites of banks in MENA countries. This study combined descriptive and analytical approaches. We utilize a panel dataset and adopt panel data econometric techniques such as fixed/random effects and the Generalized Method of Moments (GMM) estimator.

The results reveal that market concentration negatively affects bank profitability, whereas improved efficiency further enhances bank performance and contributes to the banking sector’s overall stability. Furthermore, our analysis indicates that during the COVID-19 pandemic, bank stability strongly depended on the level of market concentration, but not on bank efficiency. However, more efficient banks are more profitable and stable if the banking institutions are Islamic. Similarly, Islamic banks with the same level of efficiency demonstrated better overall financial performance during the pandemic than their conventional peers did.

The main limitation is related to the period of COVID-19 pandemic that was covered in this paper (2020–2021). Therefore, further investigation of the COVID-19 effects on bank profitability and risk will require an extended period of the pandemic crisis, including 2022.

This study provides information that will enable bank managers and policymakers in MENA countries to assess the growing impact of market concentration and efficiency on the banking sector stability. It also helps them in formulating suitable strategies to mitigate the adverse consequences of the COVID-19 pandemic. Our recommendations are useful guides for policymakers and regulators in countries where Islamic and conventional banking systems co-exist and compete, based on different business models and risk management practices.

The authors contribute to the banking stability literature by investigating the role of market concentration and efficiency as the main determinants of bank performance and stability during the COVID-19 pandemic. This study is the first to analyze banking sector stability in the MENA region, using both individual and risk-adjusted aggregated performance measures.

This study focuses on the banking systems evaluation in Portugal, Italy, Ireland, Greece and Spain (known as the PIIGS) during the financial and post-financial crisis period from 2009 to 2018.

A conditional robust nonparametric frontier analysis (order-m estimators) is used to measure banking efficiency combined with variables highlighting the effects of Non-Performing Loans. Next, a truncated regression is used to examine if institutional, macroeconomic, and financial variables affect bank performance differently. Unlike earlier studies, we use the Corruption Perception Index (CPI) as an institutional variable that affects banking sector efficiency.

This research shows that the PIIGS crisis affects each bank/country differently due to their various efficiency levels. Most of the study variables — CPI, government debt to GDP ratio, inflation, bank size — significantly affect banking efficiency measures.

The contribution of this article to the relevant banking literature is two-fold. First, it analyses the efficiency of the PIIGS banking system from 2009 to 2018, focusing on NPLs. Second, this is the first empirical study to use probabilistic frontier analysis (order-m estimators) to evaluate PIIGS banking systems.

This study examined how economic freedom and its related components, such as open markets, regulatory efficiency, rule of law and the size of government, affect bank risk behavior, focusing on the Japanese context.

The study employs a two-step GMM framework on the annual data of Japanese banks ranging from 2005 to 2020 to empirically test the hypotheses. Furthermore, we also use the ordinary least square method to ensure the robustness of our mainline findings.

The finding suggests that economic freedom increases the banks' risk-taking, thus making them fragile. The results also highlight that out of the four main subcomponents of economic freedom, regulatory efficiency and government size increase bank risk-taking, while the rule of law and open markets decrease banks' risk-taking. Additionally, we examine how the banks' specific characteristics affect the results by creating a subsample based on capitalization and liquidity ratios. Overall, the results are consistent with the baseline findings. Moreover, the results are robust to alternative proxy measures of risk.

The study's findings have several implications for regulators and policymakers. The results suggest that regulators and policymakers should reconsider their strategies for economic freedom to ensure that they promote stability in the banking system and reduce banks' risk-taking inclinations.

Although previous studies have examined the impact of economic freedom on bank stability and risk-taking, this study is the first to do so in the Japanese context, contributing to the literature by providing new insights and empirical evidence.

This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19).

A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities.

Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed.

The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample.

Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach.

This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation.