Indian Institute of Management Udaipur

This paper provides an interdisciplinary critical integrative review of research on precarious work. Based on a review of 311 records, we develop an integrated framework that brings together the antecedents, outcomes and responses to precarious work found in the literature. We also explain the discrepancy between the ideas of key influential thinkers about the existence of political potential of precarity, and the lack of fieldwork evidence that would suggest that this potential is coming to fruition. We highlight that prevailing theorisations do not take appropriate account of the historico-cultural embeddedness, or the intersectional experiences, outcomes of and responses to precarious work in different locations. We outline a pathway for future research, arguing for: (1) shifting the empirical focus of studies towards greater inclusion of members of currently under-represented geographical contexts, occupations and social groups, and towards appreciation of the different, context-specific forms, impacts and responses to precarious work; (2) developing a nuanced understanding of the experiences and outcomes of precarious work as an intersectional phenomenon; (3) decolonising our thinking about precarious work through engagement in reflexivity about the assumptions underlying the extant knowledge. Finally, we put forward policy recommendations for addressing the prevalence and impacts of precarious work worldwide.

In many real-life experiments with human subjects, missing data are common. Multiple imputation is widely used to handle unobserved data points. In statistical research, selecting important variables from multiple imputed datasets can be challenging, as each imputed dataset may yield different sets of variables. Over the last decade, stacking imputed datasets and analyzing the resulting integrated data has gained attention. In this article, we consider both horizontal and vertical stacking approaches. The horizontal stacking approach in conjunction with different group penalties is discussed alongside the recently proposed vertical appending method, for identifying predominant variables under time-to-event data. The proposed methods are investigated numerically. Finally, the methods are illustrated in two real-world oncology experiments.

Why do some multinational enterprises (MNEs) persist with non-equity foreign operating modes despite transaction cost concerns? Building on an inductive multiple case study of nine Indian firms operating across 62 emerging markets, we theorize “high involvement–low investment” as a distinct foreign entry and operating mode, where MNE managers augment foreign partners’ operations—through co-selling, marketing, capability building, and end-user services—without equity-based governance control. High involvement fosters partners’ trust in MNEs, enabling the bundling of MNEs’ consultative selling skills with partners’ relational networks. While subsequent market expansion moves, such as adding new partners, risk eroding existing partners’ trust, consistent relational engagement through transparency, long-term orientation, and empathy preserves trust and sustains partnerships, enabling the persistence of a high-involvement, low-investment mode. Counterintuitive to transaction cost economics logic, our model explains how high involvement in both pre- and post-entry stages, together with the sustained use of relational assets, enables a persistent non-equity pathway for internationalization. We contribute to entry and operating mode research by identifying people-centric involvement as a dimension of foreign market commitment, alongside capital-centric investment. Further, we extend the asset-bundling view by highlighting managerial involvement as a mechanism for cross-border interorganizational trust and the bundling of intangible assets.

Rice (Oryza sativa) is a staple food crop for more than half of the world's population. Besides high gluten-free nutritional contents, it has high economic value supporting livelihood of millions of farmers. That is why a lot of research is being carried out to derive new varieties of rice and improve its yield, stress tolerance, and grain quality. It remains a central goal in agricultural research. Genome-wide association studies (GWAS) provide a powerful framework for linking genetic variation to complex phenotypic traits, but the high dimensionality of genomic data presents significant challenges for model selection and prediction. Using rice genotype and phenotype data, we compared the performance of several frequentist and Bayesian modeling approaches: multiple linear regression (OLS: Ordinary Least Squares), LASSO (Least Absolute Shrinkage and Selection Operator), Ridge, Bayesian LASSO, Bayesian Sparse Linear Mixed Model (BSLMM), and a Bayesian spike-and-slab prior model. Phenotypic traits were transformed where necessary to approximate normality, and predictive performance was evaluated through cross-validation using mean squared error and predictive correlation. The spike-and-slab prior model often outperformed the classical methods, yielding superior prediction and effective variable selection. Our findings demonstrate the value of Bayesian model selection frameworks for plant GWAS and trait prediction, and highlight the effectiveness of Bayesian methods in identifying informative markers in rice. Such approaches hold promise for accelerating genetic improvement and supporting marker-assisted selection in crop breeding programs. Rather than emphasizing biological interpretation of individual loci, our results highlight differences in predictive behavior, stability, and inferential characteristics across models.

The first of three special issues focusing on international business in Africa, this issue addresses topics related to trade and foreign direct investment in Africa. Within this context, individual articles focus on various factors related to the African Continental Free Trade Area, access to capital and finance in Africa, foreign direct investment versus entrepreneurial growth, moving beyond commodity-based growth, better utilizing Africa’s agricultural advantages, and overcoming data accuracy issues pertaining to African development.