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This paper explores critical job resources (CRJRs) as predictors of job crafting and sustainable employability. Using job demands-resources (JD-R) theory as a theoretical lens, the authors examine how job crafting mediates CRJR and sustainable employability and whether work uncertainty as a boundary condition further strengthened these associations using moderated mediation approach.

The authors used a cross-sectional time-lagged research design by collecting data from 483 knowledge workers in Pakistan's healthcare and universities, both public and private. The authors used structural equation modeling using AMOS 25.0 software to examine the proposed relationships’ mediation, moderation and moderated-mediation processes, such as Hayes (2018) process models 1,7,14 and 58. In addition, the authors tested a structural model with self-developed estimands instead of using process macros available in SPSS by computing variables.

The results of this study confirmed that CRJR predicts job crafting and employees' sustainable employability. Furthermore, comprehensive testing suggested that mediation of job crafting between CRJR and sustainable employability further strengthened in the presence of a boundary condition of work uncertainty.

The study uncovers CRJR (job, organizational, social and relational) as a predictor of job crafting. The authors suggest that job design integrating CRJR helps organizations and managers promote job crafting and make employees responsible for their sustainable employability. The proposed CRJR has not been used as a predictor of job crafting, and no such study tested CRJR as a predictor of sustainable employability. The authors made comprehensive testing to examine the boundary condition of work uncertainty while examining the CRJR and sustainable employability relations via job crafting.

The purpose of this paper is to focus on tracing GHG emissions across the supply chain industries associated with the US residential, commercial and industrial building stock and provides optimized GHG reduction policy plans for sustainable development.

A two-step hierarchical approach is developed. First, Economic Input-Output-based Life Cycle Assessment (EIO-LCA) is utilized to quantify the GHG emissions associated with the US residential, commercial and industrial building stock. Second, a mixed integer linear programming (MILP) based optimization framework is developed to identify the optimal GHG emissions’ reduction (percent) for each industry across the supply chain network of the US economy.

The results indicated that “ready-mix concrete manufacturing”, “electric power generation, transmission and distribution” and “lighting fixture manufacturing” sectors were found to be the main culprits in the GHG emissions’ stock. Additionally, the majorly responsible industries in the supply chains of each building construction categories were also highlighted as the hot-spots in the supply chains with respect to the GHG emission reduction (percent) requirements.

The decision making in terms of construction-related expenses and energy use options have considerable impacts across the supply chains. Therefore, regulations and actions should be re-organized around the systematic understanding considering the principles of “circular economy” within the context of sustainable development.

Although the literature is abundant with works that address quantifying environmental impacts of building structures, environmental life cycle impact-based optimization methods are scarce. This paper successfully fills this gap by integrating EIO-LCA and MILP frameworks to identify the most pollutant industries in the supply chains of building structures.

The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.

Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO₂-equivalent, whereas the economic outputs were measured in million dollar economic activity.

The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO₂ intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”

This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

This paper conducts a Systematic Literature Review (SLR) of Machine Learning (ML) in Supply Chain Management through bibliometric and network analysis, the authors are able to grasp key features of the contemporary literature. The study makes use of state-of-the-art analytical framework based on a unified approach to reveal insights from the present body of knowledge and the potentials for future research developments.

Unlike standard literature reviews, in SLR, a structured approach is followed. The approach enables utilizing contemporary tools and software packages such as R-package “bibliometrix” and Gephi for exploratory and visual analytics. A number of clustering methods are employed to form clusters. Later, multivariate analysis methodologies are adopted to determine the dominant clusters for the influential co-cited references.

Using contemporary tools from Bibliometric Analysis (BA), the authors identify in an exploratory analysis, the influential authors, sources, regions, affiliations and papers. In addition, the use of network analysis tools reveals research clusters, topological analysis, key research topics, interrelation and authors’ collaboration along with their patterns. Finally, the optimum number of clusters computed for cluster analysis is decided using a systematic procedure based on multivariate analysis such as k-means and factor analysis.

Modern-day supply chains increasingly depend on developing superior insights from large amounts of data available from diverse sources in unstructured and semi-structured formats. In order to maintain a competitive edge, the supply chains need to perform speedy analysis of big data using efficient tools that provide real-time decision-making insights. Such an analysis necessitates automated processing using intelligent ML algorithms. Through a BA followed by a detailed data visualization in a network analysis enabled grasping key features of the contemporary literature. The analysis is based on 155 documents from the period 2008 to 2018 selected using a systematic selection procedure.

Lean (TQM and JIT) and agile manufacturing (AM) are viewed as strategic capabilities that can help firms to meet diverse set of market demands. However, the question whether lean manufacturing and AM are complementary or competing capabilities is still open to discussion. This research proposes an integrated research framework that draws on complementary theory, theory of systems, and concept of fit to examine this question regarding these two strategic capabilities.

Data are collected from 248 apparel exporting firms, and the proposed model is evaluated using structural equation modeling.

Results show that lean manufacturing, AM, and supporting management and infrastructural practices have positive and complementary effects on firm's performance. Further, results depict that lean manufacturing and AM complementarity is a complete organizational synergistic phenomenon, and piecemeal implementation of these initiatives may lead to suboptimal or unsatisfactory results. Results also indicate that there is no significant direct (correlated and uncorrelated) relationship of management, infrastructure, lean manufacturing, and AM practices with firm's performance and support that lean manufacturing and AM are not competing paradigms.

This research is based on cross-sectional data from one industry. Future research should collect data from diverse sectors in different countries.

This study provides a key insight for manufacturing managers that piecemeal implementation of lean manufacturing and AM does not yield optimal outcomes. In addition, study suggests that lean manufacturing and AM complementarity builds on strong foundation of strategic management and internal and external infrastructure. Therefore, managers should focus on development of skilled and empowered human resources, technological advancements, and learning and virtually integrated organizations for effective implementation of lean manufacturing and AM.

Proposed framework is one of the first, if not the first, that seeks to resolve the question: whether lean manufacturing and AM are complementary or competing capabilities. Complementary effects of lean manufacturing and AM along with management, internal infrastructure, and common external infrastructure practices have positive impact on performance. This study also segregated infrastructure practices into internal and common external infrastructure practices.

Supplier selection and evaluation are arguably one of the most critical functions for the success of an organization. Several approaches exist in the literature to objectively evaluate suppliers, including analytic hierarchy process and total cost of ownership. Analytic hierarchy process provides a framework to cope with multiple criteria situations involving supplier selection, while the total cost of ownership is a methodology and philosophy, which look beyond just the price of a purchase to better understand and manage costs in selecting and maintaining relationships with suppliers. This paper illustrates the two approaches and provides a comparison.

Although an emerging field in work and family literature, organization cross domain intervention managing strategies (CDIMS) is an under-researched area. This study aims to investigate whether organization non-monetary CDIMS (control over work hours and supervisor support) have an impact on employee outcomes via the mediating effect work–family balance (WFB).

Data for the study have been gathered in two distinct surveys from 300 employees working at the managerial level in various manufacturing companies of Pakistan with a seven-week time interval to reduce common method variance. Data were collected during January and March 2020. Confirmatory factor analysis has been performed before testing the mediated model.

Organization non-monetary CDIMS i.e. control over work hours and supervisor support has been associated positively with WFB, which has further mediated the relationship between workplace non-monetary CDIMS and employee professional outcomes.

Both supervisor support and control over work hours improve employee outcomes by experiencing the satisfaction with WFB. It has been recommended that organizations embed cross-domain interventions in their job design to benefit all employees impartially. Thus, all employees can enjoy better WFB and show positive work behaviors.

This study demonstrates the effectiveness of non-monetary organization CDIMS in enhancing the experience of employee’s WFB and improving their professional outcomes.

The purpose of this research is to provide a window into the supply chain practices of the small and medium enterprise sector in Pakistan.

The Small and Medium Enterprise Center (SMEC) at the Lahore University of Management Sciences undertook a survey in 2003 to gauge the health of this sector. A survey of 650 firms in ten districts in the country was conducted. This paper presents the results and analyses of the supply chain practices of these SMEs.

There is dearth of independent data and research in the field of SME's in Pakistan. This paper provides a window to the supply chain practices of SME's in Pakistan and will enable future researchers to use this research as a building block in understanding these practices and the factors that pertain to successful firms.

The implications of this study are far reaching enabling trainers, consultants, donor agencies, and entrepreneurs in the SME sector to learn the practices of successful firms and adopt/help SMEs adopt these in their operations.

This paper we report some of the results of the survey and our analysis of factors related to supply chain management practices that seem to correlate with the health of the enterprise. The analysis shows that successful firms on average had more products, more customers as well as more new customers. SMEs into exports were the healthiest and exhibited most dynamic characteristics, followed by those that sold to OEMs. Another interesting insight is that growing firms sold more directly to end users while firms with higher sales per employee sold the least to the end user.

A major frustration for most policy researchers in the SME area is the virtual non‐existence of scientific data on this sector in the country. This is the first survey of its kind in Pakistan.

A major frustration for most policy researchers in the small to medium‐sized enterprise (SME) area is the virtual non‐existence of scientific data on this sector in the country. This paper aims to provide some data.

A survey of 651 SMEs from the manufacturing sector of Pakistan was conducted in 2003 and statistical analysis was carried out to analyze the data. This paper focuses on the relationship between the health indicators namely, sales/employee, increase/decrease in sales, and investment plans and the different personal characteristics of the owner/entrepreneur.

This analysis suggests that education, generation setting up the business, and number of partners have a significant relationship with the health of SMEs. Health of the firm is also dependent on owner habits like watching television, reading newspapers and using computers for office work. Other factors like caste of the owner and occupation of the owner's relatives were also analyzed, but no significant relationship with the health of an SME was observed.

The implications of this study are far reaching in understanding the profiles of owners of SMEs in Pakistan and how these profiles impact the profitability of the firms.

To date no other such study has been carried out in Pakistan. Similar studies on owner profiles have previously been carried out in other countries such as the UK and USA.

This study aims to identify the factors and propose a conceptual framework for the civil aviation industry from the sustainability perspective with the participation of international entrepreneurs. Based on the results of this study, international decision-makers and entrepreneurs in the civil aviation transportation industry will better understand their decision-making processes. A combination of interpretive structural modelling (ISM) and matrix-based multiplication applied to a classification (MICMAC) was used to classify practical factors to depict a conceptual model based on their level and classification in the sustainable supply chain (SSC) of the civil aviation transportation industry. In this study, special attention has been paid to the issue of sustainability as an essential mechanism for developing international entrepreneurship in the civil aviation transportation industry. The factor of flexibility in service production was identified as the driver factor; the factors of organisational commitment to a SSC were found to have the highest driver-dependent power that can attract international entrepreneurs in this field.