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The chapter examines the sources of total factor productivity growth (TFPG) of the 2-digit manufacturing industries as well as total manufacturing industry of Gujarat during the period from 1981–82 to 2010–11, using a stochastic frontier approach. The empirical finding clearly states that although factor accumulations as well as resource allocations in most of the 2-digit manufacturing industries of the state have been improved during the postreform period, technological progress (TP) and technical efficiency change (TEC) of the same have deteriorated in most industries of the state during that period. As a result TFPG in the major manufacturing industries as well as total manufacturing industry of the state have declined because the combined effect of their improvement in scale effect (SC) and allocation efficiency effect (AEC) could not offset the declining effect of both the TP and TEC of the same during that period. In this context, the government should take some policy initiatives to improve productive efficiency of the organized manufacturing industries in Gujarat. Once efficiency increases, it enhances competitiveness, thereby increasing productivity growth and its different sources of organized manufacturing industries of the state.

One of the most hyped concepts in the manufacturing industry is ‘Industry 4.0’. The ‘Industry 4.0’ concept is grabbing the attention of every manufacturing industry across the globe because of its immense applications. This phenomenon is an advanced version of Industry 3.0, combining manufacturing processes and the latest Internet of Things (IoT) technologies. The main advantage of this paradigm shift is efficiency and efficacy in the manufacturing process with the help of advanced automated technologies. The concept of ‘Industry 4.0’ is contemporary, so it falls under exploratory study. Therefore, the research methodology is thematic narration grounded on secondary data (online) analysis. In this light, this chapter aims to explain ‘Industry 4.0’ in terms of concepts, theories and models based on the Web of Science (WoS) database. The data include research manuscripts, book chapters, blogs, white papers, news items and proceedings. The study details the latest technologies behind the ‘Industry 4.0’ phenomenon, different business intelligence technologies and their practical implications in some manufacturing industries. This chapter mainly elaborates on Industry 4.0 frameworks designed by (1) PwC (2) IBM (3) Frost & Sullivan.

The unorganised manufacturing sector contributes one third share of overall manufacturing employment and one fifth share of gross value added of the manufacturing sector. Despite its important role in large-scale employment generation, this sector is neglected by the researchers as well as by the policy makers as compared to the focus given on the organised manufacturing sector. The issues of energy intensity, environment emissions and growth of unorganised manufacturing enterprises (UMEs) remain unexplored. The present chapter attempts to estimate the CO₂ emission and emission intensity (EI) across UMEs on the basis of NSSO Unit Level data of 62nd, 67th and 73rd rounds. It also analyses the growth of UMEs in relation to CO₂ emission and EI. The nature of the sector is very much dispersed. Our study reveals that a portion of unorganised enterprises did not use any energy in their production activities and used manually operated instruments like – handlooms, weaving machines, hand-operated oil and rice mills, etc. The main energy inputs of UMEs are electricity and fuel & lubricants. The CO₂ emission is relatively less in UMEs compared to organised manufacturing enterprises. Across the unorganised manufacturing industries, the higher CO₂ emission are observed in manufacturing of food product industry and other non-metallic mineral industry. The study found that CO₂ EI of UMEs depends on firm-level characteristics like perennial nature, establishment type, urban location and expanding growth status. However, capital intensive UMEs are more polluting.

The unseen benefit of the COVID-19 pandemic in Nigeria is that it presented an insight into the contributions of the manufacturing industry to the socio-economic development of the nation. During the pandemic, the unavailability of Personal Protective Equipment (PPE) have demonstrated the low production capability of the Nigerian manufacturing industry to provide essential materials and equipment required by the health sector. This availability of these facilities is vital for the sustenance of public health and lives of frontline health workers. Consequently, this challenge resulted in an increase in infection and deaths of frontline health workers which could have minimized if there were adequate provision of PPE local production. Hence, this study postulates that the COVID-19 pandemic is an entrepreneurial opportunity in Nigeria, especially for the mass production of PPE to service the health sector of Nigeria and sub-Saharan Africa. The study therefore investigated the challenges of entrepreneurship in Nigeria's manufacturing industry and examined how the strategic partnership between educational institutions, corporate bodies and the government can be optimized. Utilizing a qualitative research methodology based on scholarly journals and interview sessions, the study revealed that Nigeria's manufacturing capability is low due to Nigeria's age-long protectionist tendency, lack of integration of entrepreneurship skill in the industry, inadequate knowledge of financial knowledge, lack of synergy in the industry, among others. The study recommended a strategic framework for Nigeria's manufacturing industry for the production of PPE in form of a helix model after an overhauling of the production process of the industry.

Purpose – The purpose of the chapter is to sketch the historical and evolutionary development of the Wichita Aircraft Manufacturing Cluster from inception to present and provide a descriptive narrative of aircraft industry knowledge spillovers currently driving effort to establish a Medical Device Manufacturing Cluster. The chapter illustrates how carbon-fiber composite materials knowledge and technology developed for use in the aviation industry is facilitating the creation and growth of medical device manufacturing.

Methodology/approach – We use an historical case study approach to trace the development of the aircraft cluster in the Wichita, KS metropolitan area. A number of technologies are identified that had initially been adopted by one firm but eventually diffused through other firms in the local cluster and ultimately throughout the industry.

Findings – In addition to providing examples of within industry knowledge spillovers, we provide an example of technology-based knowledge that is diffusing through the aircraft manufacturing industry and is now being used as the basis for establishing an unrelated industry manufacturing cluster. The use of carbon-fiber composites in aircraft manufacturing has diffused from one manufacturer to many in the industry. Subsequently, the knowledge base surrounding carbon-fiber composite materials is being used in a local R&D effort to create a second manufacturing cluster producing medical devices ranging from surgical instruments to joint-replacement implants.

Originality/value of paper – The chapter illustrates a unique example of a manufacturing cluster, intra-industry knowledge spillovers, and inter-industry knowledge spillovers to create a new manufacturing cluster.

This study consists of an examination of productivity growth following three major technological breakthroughs: the steam power revolution, electrification and the ICT revolution. The distinction between sectors producing and sectors using the new technology is emphasized. A major finding for all breakthroughs is that there is a long lag from the time of the original invention until a substantial increase in the rate of productivity growth can be observed. There is also strong evidence of rapid price decreases for steam engines, electricity, electric motors and ICT products. However, there is no persuasive direct evidence that the steam engine producing industry and electric machinery had particularly high productivity growth rates. For the ICT revolution the highest productivity growth rates are found in the ICT-producing industries. We suggest that one explanation could be that hedonic price indexes are not used for the steam engine and the electric motor. Still, it is likely that the rate of technological development has been much more rapid during the ICT revolution compared to any of the previous breakthroughs.

This study attempts to analyze energy intensity, capacity utilization (CU), output and productivity growth of aggregate manufacturing sector in India during the period 1980–1981 to 2016–2017. A decadal analysis as well as a comparison between pre- and post-liberalization period of productivity growth is also made. Total factor productivity growth (TFPG) is also adjusted with CU to obtain adjusted TFPG. The trend in energy intensity is also analyzed to answer the question of sustainability. Results shows that TFPG declined in the post-reform period, highlighting the fact that liberalization process has its adverse impact on productivity growth. From the study it is observed that a declining trend in adjusted TFPG in the post-reform period, but the rate of decline is higher. Energy intensity and CU of the Indian manufacturing industries is found to be increasing over the study period. Increasing energy intensity, quite significantly, would increase the level of pollution generated by the manufacturing industries. So, interestingly enough, this may lead to conclude that the growth of the manufacturing industries is not in line with the basic essence of sustainable development.

Purpose – The broader aim of this research is twofold. First, we aim to better understand how the business computer was conceptualized and used within U.S. industry. Second, this research investigates the role of social factors such as relational structure, institutional entrepreneurs, and position in the formation of conceptualizations of new technologies.

Design/methodological/approach – This paper is theoretically motivated in the sense that it responds to the lack of attention to the failure of institutional entrepreneurs to change belief systems. Through detailed archival, network, and descriptive statistical analysis, the paper shows how the failed institutional entrepreneur fits conventional explanations for success. The paper then analyzes two matched cases, comparing the insurance industry's rejection of the institutional entrepreneur with manufacturing's acceptance, in order to identify what is missing in current explanations of institutional entrepreneurs.

Findings – Our analysis reveals that the role of the audience structure in interpreting the institutional entrepreneur's message influences the change outcome. In our case, the institutional entrepreneur's view of the computer as a brain that supported decision-oriented applications did not fit with views of the insurance groups who had centralized authority over interpreting the computer. Because manufacturing had less centralized control in its discourse around the computer, there were fewer constraints on assimilation, allowing the entrepreneur's views to resonate with some of the occupational groups.

Research limitations/implications – This paper develops a theoretical approach to institutional entrepreneurship that situates the entrepreneurial efforts of individual actors within a system characterized by the structure of its audience and subject to distinct historical macro-structural processes that present significant obstacles to the realization of their entrepreneurial projects.

This paper attempts to resolve the conflicting results on sticky cost behavior in prior literature. Large sample studies find that selling, general, and administrative costs (SG&A) and cost of goods sold (CGS) are sticky, that is, costs are less likely to decrease when activity decreases than to increase when activity increases. In contrast, studies limited to one industry find little or no sticky cost behavior.

We investigate whether SG&A and CGS sticky cost behavior differ across/ four major industry groups (manufacturing, merchandising, financial, and services) characterized by different production, operational, and economic environments. In addition, we study whether sticky cost behavior arises for all changes in activity level (as measured by revenue changes) or for only large changes in activity level. Finally, we investigate whether determinants of sticky cost behavior vary across industries.

Our results suggest that costs in the manufacturing industry are the “stickiest,” while costs in the merchandising industry are the “least sticky,” with financial and service industries exhibiting some level of sticky cost behavior. Further, we find that sticky cost behavior is industry-specific, both in the magnitude of activity changes that give rise to sticky cost behavior and in the determinants that drive the behavior.

Our investigation of 20 distinct sub-industries within the “stickiest” manufacturing industry finds that while some sub-industry groupings show significant sticky behavior, most do not. This result may explain why, contrary to large sample studies, single industry studies find little or no sticky behavior in costs.

Our research is the first to try and reconcile the conflicting results on sticky cost behavior. Understanding the pervasiveness of stickiness is necessary to move research forward in this domain.

The study estimates total factor productivity growth (TFPG) and its components of the 4-digit manufacturing industries of chemical and chemical products in India from 1998–1999 to 2017–2018, pre-economic crises period (from 1998–1999 to 2007–2008) and post-economic crises period (from 2008–2009 to 2017–2018) using frontier approaches, that is, data envelope analysis DEA and stochastic frontier approach (SFA). The components of TFPG are technological progress (TP), technical efficiency change (TEC) and economic scale change (SC). It is found that the growth rates of total factor productivity (TFP) in most of the 4-digit industries of chemical and chemical products in India increased during the post-economic crises period (from 2008–2009 to 2017–2018) and the increase in TFPG of them during that period is mainly accounted for by the increase in TP of the same during that period. The TEC of almost all the industries remains the same, however, declined during the post-economic crises (from 2008–2009 to 2017–2018) and SC of them remains very low or even negative during the aforementioned study periods.