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This paper aims to discuss the firm productivity premium for servitized firms. It discusses servitization across the product value chain and estimates the effects of the range and extent of servitization on productivity premium in manufacturing firms.

This paper develops a conceptual framework and tests the hypotheses on the effects of servitization on productivity premium using linear regression models with a sample of 20,837 Russian manufacturing firms gathered from the Ruslana Bureau van Dijk database and the Russian customs service.

Servitized firms exhibit higher total factor productivity and labor productivity. The labor productivity premium increases with the number of services offered. However, the impact of services on productivity varies along the product value chain: postmanufacturing and postsales services enhance productivity premium, while manufacturing and back-office services diminish them. The effect of establishment services remains ambiguous.

This paper offers an analytical framework for firms to assess their servitization strategies. These strategies should be gradual, focused on enhancing firm efficiency rather than being an end goal. Firms should initiate the process by introducing services at the postproduction and postsales stages of the product creation chain to achieve productivity premium.

The paper extends the evidence on firm-level productivity drivers and contributes to the servitization theory. A servitization strategy should be portfolio-based, considering both the potential gains and losses in productivity resulting from the implementation of specific services.

This paper aims to explore whether firms’ performance determines innovation using a sample of Indian manufacturing firms. The impact of innovation on firms’ performance across specific countries has been discussed in the literature. However, the effect of firms’ performance on innovation output, especially for a developing country like India, remains an open question. Against this backdrop, this paper investigates whether firms’ performance determines innovation in Indian manufacturing firms.

The authors use patent filing information to instrument innovation and total factor productivity to instrument firms’ performance. The patent data are collected from the Patent Search and Analysis Software database and firm-level data from the Centre for Monitoring Indian Economy’s Prowess database. The study uses a sample of 309 Indian manufacturing firms from 2005 to 2021. Given the count nature of the data set used in this study coupled with over-dispersion issues, the authors have used the negative binomial regression to estimate the empirical specification of the models. There could be a possible problem of endogeneity due to the contemporary nature of innovation and firms’ performance. Therefore, to address the possible issues of endogeneity in the model, the authors have used the Generalized Method of Moments (GMM) estimators for more robustness checks of the empirical results.

The empirical results exhibit a positive and significant impact of firms’ performance on the innovation output, validating that firms’ performance determines innovation in Indian manufacturing firms. The posterior estimation results using GMM estimation also corroborate that firms’ productivity is a determining factor for the innovation output of Indian manufacturing firms. Furthermore, empirical results exhibit that the ex ante innovativeness of the firms substantially affects the current innovation. This validates that the firms’ prior experience, learning by doing and past innovative efforts are more likely to precipitate more innovation in the current period.

This paper’s main contribution is empirically estimating whether firms’ performance determines innovation, which is hardly discussed in the existing innovation literature, specifically using Indian manufacturing industries. Further, it adds to the existing literature in two other prominent ways. First, this paper investigates whether firms require ex ante expertise to innovate or if a firm starting from scratch can innovate significantly without any hindrances. Second, it enriches the literature by instrumenting innovation in output terms with the patent application against input measures of innovation, such as research and development expenditures, acquisition of machinery and equipment, while discussing the relationship between firms’ performance and innovation, specifically in the context of a developing economy like India.

This study investigates the optimum size for manufacturing firms and the impact of subcontracting on firms' likelihood of achieving their optimal scale in Vietnam.

Using data from the enterprise census in 2017 and 2021, the paper first estimates the production function to identify the optimum firm size for manufacturing firms and then applies the logit model to investigate factors associated with the optimal firm size.

The study reveals that medium-sized firms exhibit the highest level of productivity. Nevertheless, a consistent trend emerges, indicating that nearly 90% of manufacturing firms in Vietnam operated below their optimal scale in both 2017 and 2021. An analysis of the impact of subcontracting on firms' likelihood to achieve their optimal scale emphasizes its crucial role, especially for foreign firms, exerting an influence nearly five times greater than that of the judiciary system.

The paper's findings offer crucial policy implications, suggesting that initiatives aimed at enhancing the overall productivity of the manufacturing sector should prioritise facilitating contract arrangements to encourage firms to reach their optimal size. These insights are also valuable for other countries with comparable firm size distributions.

This paper provides the first empirical evidence on the relationship between firm size and productivity as well as the role of subcontracting in firms' ability to reach their optimal scale in a country with a right-skewed distribution of firm sizes.

This paper aims to analyse the pattern of structural transformation and productivity growth of 15 major Indian states at a ten-sector level of disaggregation from 1983 to 2017.

The analysis has been carried over in a ten-sector disaggregated level through construction of the labour and output data from various micro data sets.

The majority of Indian states have bypassed the stage of industrialization, wherein labour previously engaged in agriculture has transitioned directly into the modern services sector while skipping the manufacturing. There are no sign of convergence of sectoral productivities and the heterogeneity among Indian states persists throughout the time period. The growth performance of states are not positively associated with the movement of labour across sectors as measured by the structural transformation index (STI). This goes against the narrative that structural transformation help reduce the misallocation of factors. Despite an increase in educational attainment of workers across all sectors, more than one-third of agricultural workers still remain either illiterate or lack formal schooling. Among sectors, construction (C) and trade, hotels and restaurants (THR) have absorbed the majority of workers who have left agricultural jobs. Finance, insurance, real estate and business services (FIRB), electricity, gas and water supply (EGWS) and mining and quarrying (MQ) are the three sectors that have seen significant gains in labour productivity during the study period.

To the best of the authors’ knowledge, this is the first attempt to analyse structural change and productivity growth in the Indian economy using Indian states as critical geographical marker. The results are new and add value to the literature.

This paper aims to overcome the inability of both comparing loss costs and accounting for production resource losses of Overall Equipment Effectiveness (OEE)-related approaches.

The authors conducted a literature review about the studies focusing on approaches combining OEE with monetary units and/or resource issues. The authors developed an approach based on Overall Equipment Cost Loss (OECL), introducing a component for the production resource consumption of a machine. A real case study about a smart multicenter three-spindle machine is used to test the applicability of the approach.

The paper proposes Resource Overall Equipment Cost Loss (ROECL), i.e. a new KPI expressed in monetary units that represents the total cost of losses (including production resource ones) caused by inefficiencies and deviations of the machine or equipment from its optimal operating status occurring over a specific time period. ROECL enables to quantify the variation of the product cost occurring when a machine or equipment changes its health status and to determine the actual product cost for a given production order. In the analysed case study, the most critical production orders showed an actual production cost about 60% higher than the minimal cost possible under the most efficient operating conditions.

The proposed approach may support both production and cost accounting managers during the identification of areas requiring attention and representing opportunities for improvement in terms of availability, performance, quality, and resource losses.

Effective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.

The basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.

The study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.

The study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.

The RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

The lean manufacturing (LM) approach is a highly effective method that can be implemented in any industry to streamline production processes, meet customer demand and eliminate any unnecessary waste. This paper aims to propose and evaluate a generic project-based framework grounded on the LM approach for reducing lead time in foundry processes.

Using design science research (DSR), we developed a generic LM project-based framework for reducing lead time in foundry processes.

The developed framework provides an alternative method to implement LM projects to reduce lead time and nonvalue activities in foundry factories.

The findings of this research can guide better lean practitioners for lead time reduction in foundry processes.

This paper contributes to the operational excellence literature when discussing the impact of the LM approach on foundry processes. In addition, the paper provides a roadmap for reducing lead time in a foundry company.

The development of prefabrication into full-scale offsite manufacturing processes in the construction industry is paradigm-shifting. Moreover, Building Information Modelling (BIM) is becoming the primary mode of communication and integration in construction projects to facilitate the flow of information. Although research has been performed on BIM and Offsite Construction (OSC), integrating these two concepts remains ambiguous and complex and lacks documentation and structure, especially in New Zealand. Therefore, this paper develops a robust framework for OSC and BIM integration. The study focusses on identifying integration challenges and proposes strategies for overcoming these challenges.

This study applied scientometric analysis, a systematic literature review (SLR) and semi-structured expert interviews to investigate OSC and BIM integration challenges. Multiple themes were investigated and triangulation conducted in this research supports the creation of applicable knowledge in this field.

Multiple gaps, research trends and the pioneer countries in the paper's scope have been identified through scientometric analysis. Then, a classified cluster of challenges for OSC and BIM implementation and integration strategies of OSC and BIM were demonstrated from the findings. The interviews provided comprehensive and complementary data sets and analyses. The findings from the Systematic Literature Review and interview structured the integration framework.

The contribution of this paper to existing knowledge is a developed framework that serves as a guideline for the OSC stakeholders. This framework can assess OSC's alignment with BIM and consolidate strategies for incorporating OSC into a BIM-based project delivery process. The framework consists of 23 strategies categorised into 8 clusters: a policy document, training and professional development, documentation, technology management, governmental development, contract development, accurate definition and detailing and communication. The proposed strategies will streamline integration by reducing potential challenges, thus enhancing project productivity.

This study aims to investigate the effect of monetary policy transmission on the use-based classification of manufacturing industries in India, an integral aspect influencing the overall economic growth of the nation.

The empirical study applies a panel autoregressive distributed lag model to examine the relationship/association between monetary policy transmission mechanism and the output of manufacturing industries in the long run and short run.

In the long run, the findings reveal a negative association between money supply and manufacturing industries’ output, indicating that an increase in money supply corresponds to a decrease in manufacturing output. Conversely, a positive relationship is observed between manufacturing industries’ output and banks’ credit, indicating that an increase in bank credit leads to a corresponding increase in manufacturing output. In the short run, the results highlight a significant positive relationship between manufacturing output and monetary policy transmission variables, including money supply, statutory liquidity ratio, real exchange rate and foreign direct investment. The use-based classification of manufacturing industries such as primary goods, capital goods and intermediate goods exhibits greater responsiveness to monetary policy shocks than consumer durables and non-durables goods.

Policymakers are advised to regulate credit expansion to support the industry without risking financial instability, with key recommendations including stimulating consumer demand and adopting sector-specific policies to promote sustainable growth across diverse manufacturing sectors.

India, being a developing economy, efficient monetary policy transmission is crucial for boosting manufacturing output and employment. Nevertheless, there has been a scarcity of research concentrated on this pivotal intersection. This study aims to fill that gap, providing fresh insights into how monetary policy affects the growth of the manufacturing industry.

This research highlights the ongoing concern about organizational productivity and the lack of focus on designing an optimal model. The authors aim to create a comprehensive model for managing organizational productivity, considering its impact on profitability, customer satisfaction, and employee morale. They use qualitative research methods, including Systematic Literature Review and Interpretive Structural Modeling (ISM).

In this research using the qualitative research method of Systematic Literature Review, 57 variables affecting productivity were identified. These variables were placed in 16 layers by using the ISM method, which were classified analytically in four sections: INPUTS, OUTPUTS, OUTCOMES and IMPACTS. By determining the relationship between the sections, the research model was designed.

The potential model for organizational productivity management provides a comprehensive framework addressing critical factors like technology adoption, employee empowerment, organizational culture, and more. It identifies Linkage, Dependent, and independent variables. The lower layers consist of INPUTS such as Technological Tools, Organizational Values, and more. In the highest layer, impactful variables like Enhanced competitiveness, Improved decision-making, and Improved organizational culture are labeled as IMPACTS. Middle layer variables are categorized as OUTPUTS and OUTCOMES.

In this study, the concept of productivity management was redefined for the first time, and a multi-layered model for productivity management was creatively explicated using the structural equation modeling method.