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The aim of this study is to provide a further understanding of how and when social media (SM) boosts firm performance (FP) by exploring the mediation role of business innovation capabilities (BI) and firm competitiveness (FC) in the link between SM and FP.

This study uses a quantitative approach using small and medium sized enterprises (SMEs) in emerging market as an empirical field of research. The population of this study is the management level of SMEs and professionals. This study surveyed 425 samples to get primary data for quantitative analysis. This study uses Smart PLS SEM version 3.3.2 to analyze the collected data.

This study found that SM directly and indirectly affects FP through BI and FC. Besides, BI and FC mediated the relationship between SM and FP.

The originality of this study lies in the operationalization of an unexplored integrated framework using SMEs in emerging market as an empirical field of research from the perspective of combination of diffusion of innovations theory and resource advantage theory. This study thus provides a new approach to the potential of SM for business innovation capabilities oriented toward sustainability. Drawing on the findings of this study, theoretical and managerial implications are proposed which may be of great interests to business practitioners, business owners, business directors and managers to operate their organizations efficiently through making optimal use of SM platforms.

This study draws on the systems perspective to study the individual and combined interaction effect of lean management (LM) and sustainability management (SM) on the organization's triple bottom line (TBL) performance.

The study employs structural equation modeling to test the proposed hypotheses using data from the sixth version of the International Manufacturing Strategy Survey (IMSS VI).

The study finds that LM is positively related to all dimensions of the TBL performance. In contrast, SM is positively related to social and environmental performance and negatively related to economic performance. Finally, by finding that the interaction between LM and (SM) is positive for social and environmental performance, this study not only confirms that LM is an enabler for sustainability, but it also supports that the two paradigms are mutually compatible and reinforcing.

The findings imply that practitioners pursuing both LM and SM should leverage their mutual positive effects and balance the unintended effects of implementing isolated bundles by implementing them together as a complete socio-technical system. Their combined impact on the TBL performance will outweigh the sum of their individual effects in the case of isolated implementations.

In contrast with the extant literature, this study proposes that LM and SM make parts of one system as opposed to one correlated with the other or having a positive causal effect on the other. Taking an integrated systems approach, the study empirically verifies the “mutual compatibility” of the lean and sustainability paradigms argument, with regard to their effect on the TBL performance.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

This study aims to examine the moderating role of principle-based entity (PBE) of luxury brands and its effect on perceived corporate social responsibility (CSR) motives, consumer situational scepticism and brand resonance.

Structural equation modelling using multigroup analysis was used. Data were collected through a consumer panel.

Values-driven motives lowered consumer situational scepticism (CSS) significantly more in PBE than non-PBE. However, egoistic-driven motives increased CSS significantly more in PBE than non-PBE. Stakeholder-driven motives and strategic-driven motives did not elicit CSS, contrary to prior studies in non-luxury brands. PBE status also weakens the relationship between CSS and brand resonance more than non-PBE status.

This study is the first to provide empirical insights into PBE status and its effects on perceived motives, CSS of CSR initiatives and its influence in consumer and management outcomes in luxury brands.

This paper examines what facilitates the swift reconfiguration of freight movements across transport modes in the wake of a major disaster.

A qualitative research approach focussing on the New Zealand (NZ) domestic freight transport operations in the wake of the 2016 Kaikōura earthquake is used with data collected through 19 interviews with 27 informants. The interviews are thematically analysed by using the framework method.

The paper provides rich and detailed descriptions of the ability of a freight transport system to recover from a disaster through rapid modal shifts. This paper identifies nine factors enabling modular transport operations and highlights the critical role of physical, digital, operational and inter-organisational interconnectivity in the aftermath of a disaster.

Although the management of freight disruptions has become a prevalent topic not only in industry and policy-making circles, but also in the academic literature, qualitative research focussing on the ability of commercial freight systems to adapt and recover from a disaster through rapid modal shifts is limited. This qualitative study sheds light on the mechanisms underlying the continuity of freight operations in the wake of a disaster and provides a comprehensive understanding of modular transport operations and the ability of freight systems to keep goods moving.

Continued quest for business improvement in terms of enhanced productivity and cost cuts is the most valued strategic function in an organization. Towards this endeavor, there have been evolutions of many problem-solving techniques like Lean, quality control (QC) tools, Six Sigma, total productive maintenance (TPM), etc. This paper introduces a different problem-solving methodology for quality improvement – prepare, measure, define, establish, control and stabilize (PMDECS) approach of Red Bin Analysis (RBA) – and presents empirical evidence of its effectiveness in eliminating the defectives at source (parts per million [PPM]) and improving the process capability (Cp).

An attempt has been made to compare RBA with the Six Sigma methodology in terms of number of defects, defectives, process capabilities, project duration, etc. Data validation with more than 2000 data points was conducted based on empirical data collected over multiple problem-solving projects conducted in six manufacturing industries of India to compare the effectiveness of both the methods. Finally, fuzzy AHP (analytical hierarchy process) model was proposed to identify the Quality Improvement Index for both the methods to address the manager’s dilemma in selecting an appropriate problem-solving method.

The paper provides empirical insights in establishing that the PMDECS approach of RBA is at par and sometimes better for problem-solving if the problem is not chronic and is at the initial stages, it requires less duration than Six Sigma projects and except casting process and it can yield better results in case of PPM rejection or Cp/Cpk improvement in other processes.

Because of the chosen research approach, the research results may lack generalizability. This research has been specifically conducted in automotive manufacturing industries. Therefore, researchers are encouraged to test the proposed propositions further.

The paper includes implications for the usage of alternative problem-solving methods, like PMDECS approach of RBA.

This paper intends to compare how the results of six sigma projects in manufacturing industries are effective against a different methodology, PMDECS approach of RBA.

The construction industry struggles in environmental and economic performance due to waste generation. Several studies have measured this waste in the construction industry of developing countries like India, Brazil, China, Nigeria and Iran, and proposed strategies to enhance the overall efficiency. But no such work exists in Pakistan's context. The construction industry of Pakistan contributes significantly to the country's gross domestic product (GDP). And with several mega projects in progress to overcome the energy crises and improve the infrastructure of the country, the absence of proper waste management policies and plans calls for empirical research. Therefore, this study quantifies and benchmarks material waste in the local context, its impact on project cost and the effect of multiple subcontracting arrangements on waste generation. It also proposes a conceptual waste management plan (WMP) for local conditions which can be generalized for developing countries.

This paper uses a mixed research approach by leveraging Saunders's research onion model. To benchmark the current wastage practices, quantitative data of material waste in the Pakistani construction industry are gathered through document review of accounting systems, inventory and payment records of 40 completed building projects. Using the data, the waste rate of different materials is measured along with their impact on cost. Additionally, the role of subcontracting arrangements in waste generation is also investigated. Also, semi-structured interviews are conducted with project managers of high- and low-performing construction organizations to propose a conceptual WMP for the local industry and developing countries.

The highest wasteful materials by quantity are wood, sand and concrete blocks, and those by cost are wood, bricks and steel. By quantity, 123% more material was used on average than the actual productive work. Also, the labor only (L-O) subcontracting arrangement causes maximum waste. Moreover, the difference between the highest (155%) and lowest wasting company (104%) is 51%, highlighting the proactive and resistive waste management culture and approach by the worst- and best-performing companies. Further, the impact of waste quantities is more than 2% of the project cost. Finally, a conceptual model consisting of measures at the project, industry and national levels is also proposed as a guide for developing countries.

The findings of this benchmarking study can help improve the project planning, execution and monitoring, and control practices by providing a better understanding of the material waste potential. This will help economize the construction industry and improve its sustainability.

This is the first benchmarking study that quantitatively measures material waste in the construction industry of Pakistan. It highlights that costly as well as sustainability-implicating materials are frequently wasted in the local construction projects. Also, this study correlates the wastage with subcontracting arrangements. Additionally, an original conceptual WMP is proposed that could help the industry improve its performance. The findings could help the construction professionals identify the loopholes in their material management practices and not only save money but also ensure better sustainability.

This study aims to address two objectives: to analyse how the benefits of quality management (QM) are perceived on specific productivity indicators of the Colombian dairy industry and to analyse the degree of QM implementation.

The methodology used was a multiple case study and involved four companies in the Colombian dairy industry. The study variables were 32 QM practices and 7 productivity indicators. Data collection instruments were in-depth interviews and checklists.

Findings revealed that all productivity indicators exhibit changes after QM implementation. In addition, cases approaching ISO 9001 have the highest number of practices implemented, as well as evidence of control of all productivity indicators, which allows them to perceive greater benefits.

This research generates novel findings that contribute to the consolidation of theory by analysing changes in specific productivity indicators of the dairy industry after the QM implementation. At the same time, it provides valuable and useful information for managers, practitioners and policymakers.

This study aims to discover a practical and effective way to apply the quality cost concept in Strategic Cost Management (SCM) framework. The interaction of preventive, appraisal and failure (PAF) activities in a company's internal value chain will be the starting point of SCM implementation.

This study begins by establishing value chain and quality costs as the scope of conceptual analysis. Discussions on the interrelationships between activities, quality and costs were gathered to clarify conceptual and practical gaps in the scope of the study. The PAF quality cost model is applied to find viable, practical solutions. The costs of activities will serve as performance indicators.

The PAF quality cost model depicts opportunities to lower costs and increase profit in a business simultaneously; current poor quality costs are the benchmark. Identifying PAF activities and costs in the business value chain and linking it with others is crucial in evaluating SCM applications. These linkages will generate a Quality Cost Chain (QCC). The leading indicator of improvement is a higher ratio between new possible failure costs (FC) and the combination of prevention and appraisal costs (PAC) than the current value, followed by a lower total quality cost (TQC). The subsequent attention is a lower ratio between the appraisal cost (AC) and prevention cost (PC). Mathematically, for assessing the operability of new quality-related activities, ΔPAC_new < ΔFC_new, TQC_new < TQC_current, (FC/PC)_new>(FC/PC)_current and (AC/PC)_new<(AC/PC)_current are proposed as feasible conditional-quantitative improvement criteria.

This study only discusses the relationship between quality costs and activities related to quality management in the PAF quality cost model, not cost behavior. This limitation opens up opportunities for future research that intends to link QCC with cost behavior in the context of managerial accounting and Strategic Cost Management. The use of QCC in certain industrial areas is the next research opportunity. The variety of PAF activities this study addresses originates from a wide range of industrial sectors; QCC research by sector may produce unique industrial quality cost phenomena.

QCC will make it easier for managers to evaluate how strategically their departments or activities contribute to quality costs at the departmental or organizational level, as well as to effectively and efficiently improve quality cost performance.

The quality-related activity and quality cost issues are still rarely treated as subjects of research studies in the field of Strategic Cost Management. Even so, the discussion tends to be very broad, complex and difficult to apply. This study combines a simple diagrammatic and mathematical approach to simplify the discussion and, at the same time, manage the value of strategic quality management.

This paper aims to propose a stepwise method to improve the sustainability of manufacturing processes.

The proposed approach is based on an extensive literature review and research around the environmental, economic and social pillars of sustainability in manufacturing firms. Considering the lean approach, the manufacturing processes are mapped in a value stream and analyzed through the extensive identified sustainability criteria.

The findings reveal the consumption and waste of natural and nonrenewable resources, through going beyond the existing boundaries and focusing on relevant derived production pieces and tracing to their origins. The findings also present the effect of the time value of money on sustainability by using the cost–time profile as a sustainability criterion. This research finds out the employees’ impacts on sustainability improvement through an effective focus on technical, cultural and personal aspects.

The research outcomes provide operations managers and decision-makers in the field of sustainability with a practical platform to comprehend and assess the factors contributing to the manufacturing process sustainability and to plan relevant corrective actions accordingly.

The extended view of sustainability criteria in this research as well as its visual-analytical approach will help practitioners to assess and improve sustainability in their operations in a more holistic way.