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The main purpose of the present study is to develop appropriate construct to benchmark occupational health and safety performance in industrial setting so that deficiencies can be highlighted and possible strategies can be evolved to improve the performance.

Data envelopment analysis (DEA), being a robust mathematical tool, has been employed to evaluate the safety performance of industries. DEA, basically, takes into account the input and output components of a decision‐making unit (DMU) to calculate technical efficiency (TE). TE is treated as an indicator for safety performance of DMUs and comparison has been made among them.

A total of 30 Indian organizations under three industrial categories such as construction, refractory and steel are chosen for comparison purpose. It has been observed that safety performance of construction industries is consistently low as compared to other categories of industries. TE has been calculated using two types of models of DEA such as constant return to scale (CRS) and variable return to scale (VRS). A paired two‐sample t‐test indicates that TEs obtained using two models are significantly different. Mean efficiency of 30 samples is found as 0.898 using CRS model whereas same is calculated as 0.942 using VRS.

The limitations may be number of input and output components considered for each DMU. If different set of inputs and outputs may be considered, the results may be different. Another limitation may be the number of industrial sectors considered in the study.

The method, being a generic one, can be adopted by the managers to assess present safety performance and find a suitable peer to which, it should follow to improve own TE followed by in what respect it has to improve. Industrial safety and occupational health concerns in industries is not only important from government regulation point of view but also essential for enhancing productivity and profitability to become competitive in the marketplace. The practical limitation may be collection data quantitatively from various DMUs because many a time the DMUs are unwilling to share data.

This work proposes use of simple mathematical tool like DEA for benchmarking based on safety performance in Indian industries. In Indian context, safety performance of industrial settings has hardly been assessed. The study provides a simple but comprehensive methodology for improving safety performance. The study also outlines comparative evaluation of safety practices in different industries.

This paper seeks to present an empirical study on organizational learning in Indian educational organizations.

The Learning Organization Profile (LOP) Survey is used as the tool for eliciting responses from the staff regarding the nature and state of organizational learning prevailing in educational settings. The study attempts to highlight the extent of organizational learning in technological institutes of repute in both the public and private sectors in India. Factor analysis and descriptive statistics have been used to analyze data and to make comparisons vis‐à‐vis ownership of organization and employee category.

Results indicate that the extent of organizational learning is below the expected level in both public and private sectors. Significant difference exists between public and private organizations in terms of the extent as well as dimensions of organizational learning. As leadership has emerged as the most valued factor in the private sector institutes and third among eight dimensions in the public ones, the onus lies in leading these institutes with able managers who inspire the employees to learn and adapt. The management has opportunity to enhance the potential of the academic institutes for learning by choosing effective leaders who provide direction and vision for employees. The role of transformational leadership is important in the context of Indian technological institutes.

Development of learning culture is becoming a dominant theme in the strategic plans of many organizations today. Hence, it is vital to investigate the nature and extent of organizational learning as prevailing in the sector of higher education and learning, specifically in the Indian context. The study differentiates organizational learning practices in public and private undertakings. It also examines the dimensions of organizational learning as experienced by different categories of employees constituting the organization.

Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs). These cannot combine and reflect accurately different subjective opinions and cannot be used in a linguistic manner. Risk factors (RFs) are assumed to have the same importance and interrelations between RFs are not considered. This study aims to overcome these disadvantages by combining fuzzy logic with multi-criteria decision-making in a dynamic manner.

This study proposes a novel three-stage fuzzy risk matrix-based RA integrating fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) and fuzzy multi-attributive border approximation area comparison (F-MABAC). At the first stage, importance weights of RFs are computed by F-DEMATEL. At the second stage, risk degrees of RTs are computed via using fuzzy risk matrix. At the third stage, CPMs are ranked by F-MABAC. Finally, a numerical example for RA in a warehouse is given.

Results show that developing instructions for material loading or unloading is the most important CPM and severity is the most important RF for the warehouse.

This study has originality in terms of having fuzzy dynamic structure. At first, RFs are assumed to be criteria sets then, RTs are assumed to be criteria set considering their risk degrees to rank CPMs in a fuzzy manner. Risk degrees of RTs are used for weights of RTs and effectiveness of CPMs are used for performance values of CPMs.

Massive transformations in mobile communication technologies have forced marketers to recognize and emphasize the factors that influence consumers’ perception of advertising value. This paper aims to explore and rank the various antecedents of advertising value as perceived by consumers to offer meaningful conclusions to marketers on mobile platforms.

Responses were collected from 483 consumers using a shopping mall intercept survey and analyzed using SPSS to confirm reliability, validity and data reduction. The Relative to an Identified Distribution (RIDIT) analysis and Grey Relational Analysis (GRA) methods were then applied to prioritize the scale items of the antecedents of mobile advertising value.

Five antecedents of advertising value were found: credibility, entertainment, informativeness, irritation and message relevance. A priority ranking was allotted to the antecedents’ scale items using the RIDIT analysis and was verified via GRA results with a correlation of 98% between the rankings of the two independent methodologies.

The findings provide a roadmap to determine which antecedents of mobile advertising value have a higher or lower impact on consumers’ overall perceptions of the advertisements they are exposed to on mobile platforms.

This study aims to use first-hand data to prioritize the underlying antecedents of mobile advertising value, which has rarely been done to the best of the authors’ knowledge. It also used two different approaches in a single study to rank the dimensions, thus producing more valid results.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

The purpose of this paper is to develop a framework for the safety performance measurement of belt conveyor systems.

A structural methodology of graph theory and matrix approach is used for developing a framework for safety performance measurement of belt conveyor systems.

The development of a framework for safety performance measurement of belt conveyor systems is essential for ensuring plant safety. For this, safety performance factors, including design and operating contextual factors of belt conveyor systems, are identified. The factors along with their interrelations are modeled using digraph. An equivalent matrix of the digraph provided safety performance function (SPF) of belt conveyor systems, leading to the development of a safety performance index (SPI).

The developed framework will enable the designers for evaluating and comparing alternative designs of conveyor systems from the safety viewpoint. The plant operators can make inferences from the SPI to identify the weak contextual factors in the plant and develop action plans for its mitigation.

The paper is novel and employs graph theory and matrix approach for safety performance measurement. The methodology helps in the quantitative evaluation of the safety performance of belt conveyor systems.

The purpose of this paper is to propose a method for performance assessment of organizations based on integrated approach of knowledge management and safety management using data envelopment analysis, and the proposed model is then applied in the car industry in Isfahan province to be checked. Therefore, deficiencies can be highlighted and possible strategies can be evolved to improve the performance.

As data envelopment analysis is a robust mathematical tool, it has been used to evaluate organizational performance. For discovering the organizational performance of knowledge management and safety management by data envelopment analysis (DEA), the first step is to specify proper criteria. To this end, in this method, the indices in both approaches of knowledge management and safety management were identified. Then, inputs and outputs were specified. Knowledge management and safety management were determined as input indices, and customer satisfaction and accident indicators were the output indices. It is noteworthy that each output index was used one time. In the next stage, performance of organizations was assessed based on both determined approaches and via data envelopment analysis. Finally, the organizations were ranked.

The suggested method was implemented in the car industry in the Isfahan province. The obtained results disclosed that among 12 decision-making units, 4 units are efficient when customer satisfaction is the output and 5 units are efficient when accidents indices are the output. In ranking with customer satisfaction as the output, Sepahan Atlas Pump Company was ranked first via super efficiency method, data envelopment analysis and similarity to ideal solution. In ranking with accidents as the output, Sepahan Atlas Pump Company ranked first via strong efficiency method and Sanatgar Company ranked first via data envelopment analysis and similarity to ideal solution.

Knowledge has been recognized as one of the valuable resources, and knowledge management would greatly effect improvement of job quality. Knowledge level increase is led by better performance and less errors. Consequently, it can enhance the organizational health and safety. There are some studies which have been conducted on safety management or knowledge management performance analysis. The organizational performance evaluation based on integrated approach of knowledge management and safety management is an important issue which is less considered in theory and practice. Thus, the authors have proposed a method which is able to evaluate the organization based on this integrated approach with functional indices, which resulted in accurate results, and finally, ranking can show the organization status to determine proper strategies.

Due to liberalization, privatization and globalization, the need of competent technical manpower at an economical cost is increasing rapidly. Many foreign multinationals are focusing on India for employable talents. Many technical institutions with cutting edge technologies and leading edge techniques are being set up by foreign collaboration, national and private initiatives. The objective of this study is to propose a model for performance evaluation and benchmarking of Indian technical institutions from perspective of all stakeholders.

For the proposed framework, a multiple criteria decision‐making tool, distance‐based approach (DBA) methodology is applied for performance evaluation of seven Indian technical institutions taking into account some selected criteria like, faculty strength (FS), student intake (SI), number of PhD awarded (PhD), number of patents applied for (patent), the campus area in acres (CA) and tuition fee per semester in rupees (TF). Consulting the experts in various fields with the help of certain questionnaire and aggregating their views by conducting ameliorated nominal group technique session, we select these evaluation criteria. The subjective weights of the criteria are determined using analytic hierarchy process (AHP). For the analysis, the required data are collected from annual report published by Ministry of Human Resource and Development (MHRD) for the year of 2007‐08.

In this paper, we have chosen seven centrally funded technical institutions for study and the institutions are coded as A, B, C, D, E, F and G. The result of the study reveals that A is the best and F is the worst. The ranking we get is in the order of A≽B≽E≽C≽G≽D≽F. From the result it is understood that A can be considered as benchmark for B, C and E (which form the second group) and this second group can be considered as an improvement target for the rest. At the end a holistic technical education system model (HTESM) is proposed.

This paper is one of the few studies that evaluate the performance of technical institutions in India. The novelty in the approach is that DBA and AHP are being used as a benchmarking technique in a simple methodology which is generic in nature.

The purpose of this paper is to assess the benefits of integrating IT tracking and routing systems into last-mile distribution operations. The paper also demonstrates the role of field experiments as a valid approach for improving the rigour of logistics research.

The study employs a field experiment approach. Data were collected before and after the experimental treatment from 16 participating vehicles, which were used as inputs and outputs to calculate vehicles' efficiencies using data envelopment analysis.

Through employing manipulation and random assignment to investigate causality in naturally occurring contexts, the study results show statistical evidence for the role of vehicle tracking and routing systems in enhancing fleet efficiency. Furthermore, results show that field experiment is an appropriate method for capital budgeting of deploying IT systems in the distribution function.

Distribution managers can use a field experiment setup to assess the potential impact of installing IT solutions prior to large-scale implementation or prior to purchasing.

The study fills a gap in the literature through the application of a field experiment approach to establish causality relationships in distribution and logistics research. This study should encourage new research on the role of field experimentation in evaluating the benefits gained from, and the capital budgeting of, the modern disruptive technologies in supply chains.

An accurate long-term multi-step forecast provides crucial basic information for planning and reinforcing managerial decision-support. However, nonstationarity and nonlinearity, normally consisted of several types of managerial data can seriously ruin the forecasting computation. This paper aims to propose an effective long-term multi-step forecasting conjunction model, namely, wavelet–nonlinear autoregressive neural network (WNAR) conjunction model. The WNAR combines discrete wavelet transform (DWT) and nonlinear autoregressive neural network (NAR) to cope with such nonstationarity and nonlinearity within the managerial data; as a consequence, provides insight information that enhances accuracy and reliability of long-term multi-step perspective, leading to effective management decision-making.

Based on WNAR conjunction model, wavelet decomposition is executed for efficiently extracting hidden significant, temporal features contained in each of six benchmark nonstationary data sets from different managerial domains. Then, each extracted feature set at a particular resolution level is fed into NAR for the further forecast. Finally, NAR forecasting results are reconstructed. Forecasting performance measures throughout 1 to 30-time lags rely on mean absolute percentage error (MAPE), root mean square error (RMSE), Nash-Sutcliffe efficiency index or the coefficient of efficiency (E_f) and Diebold–Mariano (DM) test. An effect of data characteristic in terms of autocorrelation on forecasting performances of each data set are observed.

Long-term multi-step forecasting results show the best accuracy and high-reliability performance of the proposed WNAR conjunction model over some other efficient forecasting models including a single NAR model. This is confirmed by DM test, especially for the short-forecasting horizon. In addition, rather steady, effective long-term multi-step forecasting performances are yielded with slight effect from time lag changes especially for the data sets having particular high autocorrelation, relative against 95 per cent degree of confidence normal distribution bounds.

The WNAR, which combines DWT with NAR can be accounted as a bridge for the gap between machine learning, engineering signal processing and management decision-support systems. Thus, WNAR is referred to as a forecasting tool that provides insight long-term information for managerial practices. However, in practice, suitable exogenous input forecast factors are required on the managerial domain-by-domain basis to correctly foresee and effectively prepare necessary reasonable management activities.

Few works have been implemented to handle the nonstationarity, consisted of nonlinear managerial data to attain high-accurate long-term multi-step forecast. Combining DWT and NAR capabilities would comprehensively and specifically deal with the nonstationarity and nonlinearity difficulties at once. In addition, it is found that the proposed WNAR yields rather steady, effective long-term multi-step forecasting performance throughout specific long time lags regarding the data, having certainly high autocorrelation levels across such long time lags.