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It is crucial for the Indian micro, small and medium enterprises (MSMEs) to implement a few of the most important Industry 4.0 (I4.0) technologies and reap maximum benefits of sustainability. This paper aims to prioritize I4.0 technologies that can help achieve the sustainable operations and sustainable industrial marketing performance of Indian manufacturing MSMEs.

I4.0-based sustainability model was developed. The model was analyzed using data collected from MSMEs by deploying analytic hierarchy process and utility-function-based goal programming. To have a better understanding, interviews were conducted.

Predictive analytics, machine learning and real-time computing were found to be the most important I4.0 technologies for sustainable performance. Sensitivity analysis further confirmed the robustness of the results. Business-to-business sustainable marketing is prioritized as per the sustainability need of operations of industrial MSME buyers.

This study uniquely integrates literature and practitioners’ insights to explore I4.0’s role in MSMEs sustainability in emerging economies. It fills a research gap by aligning sustainability goals of industrial buyers with suppliers’ marketing strategies. Additionally, it offers practical recommendations for implementing technologies in MSMEs, contributing to both academia and industry practices.

This report provides a framework to better understand how constraints faced by companies and their customers in emerging markets can be converted into competitive advantage and thereby into factors for growth.

Based on a literature review, a framework is developed to understand how companies can generate competitive advantage from constraints that affect companies, their customers or both. The framework is explained with case studies of Indian companies and multinational organizations (MNCs) operating in India.

A multitude of constraints affect companies operating in emerging markets such as India. India is an emerging market where MNCs would like to have a presence but are deterred by constraints. Some of these constraints affect companies, while others affect customers. Although many multinational organizations are struggling to meet their business goals in India, many local companies and some MNCs have successfully navigated the constraints and managed to grow by building these constraints into their business models. They gained competitive advantage by either differentiating their products and services or modifying their processes.

We provide a framework that will guide executives on how companies can generate competitive advantage through products and processes by working around constraints. We also provide guidelines on how to sustain these competitive advantages generated from constraint‐driven strategies. Failure to include these constraints into business models will adversely affect the growth plans of companies operating in India and other emerging markets.

The paper makes a unique contribution in articulating how competitive advantage can be generated from the very constraints that threaten the growth of business. This study also makes a distinction between constraints that can affect companies and those that affect customers of goods and services. This understanding will guide executives on where to focus their efforts while navigating constraints. Further, this paper shows how executives can consider a parallel stream of innovation for growth while operating in emerging markets.

This paper examines Aman Ullah’s contributions to robust inference, finite sample econometrics, nonparametrics and semiparametrics, and panel and spatial models. His early works on robust inference and finite sample theory were mostly motivated by his thesis advisor, Professor Anirudh Lal Nagar. They eventually led to his most original rethinking of many statistics and econometrics models that developed into the monograph Finite Sample Econometrics published in 2004. His desire to relax distributional and functional-form assumptions lead him in the direction of nonparametric estimation and he summarized his views in his most influential textbook Nonparametric Econometrics (with Adrian Pagan) published in 1999 that has influenced a whole generation of econometricians. His innovative contributions in the areas of seemingly unrelated regressions, parametric, semiparametric and nonparametric panel data models, and spatial models have also inspired a larger literature on nonparametric and semiparametric estimation and inference and spurred on research in robust estimation and inference in these and related areas.