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The concept of entrepreneurial orientation (EO) attracts considerable attention in the organizational literature. Focusing on issues related to measurement of EO and using a three-pronged framework to organize the growing diversity of EO measures, the authors conduct a systematic literature review on how EO is captured and assessed in the empirical literature. Specifically, the authors classify 551 empirical works according to the approach to measurement (i.e., managerial perceptions, content analysis, and resource allocations) which allows the authors to document and critically analyze prevalent measurement practices within the literature. Based on the synthesis, the authors identify key measurement-related tensions that may inhibit cumulative knowledge development in the area of EO, such as ad hoc modification of seminal scales and lack of theoretical clarity with respect to measurement. Additionally, the authors find that research into the antecedents of EO as well as causality and temporality of the phenomenon is underdeveloped, which the authors attribute to scarce use of mixed methods. The authors conclude chapter by discussing the challenges involved in measuring EO and offering possible recommendations for future inquiry.

In this paper, we review the literature on pay variation (e.g., pay dispersion, pay compression, pay range) in organizations. Pay variation research has increased markedly in the past two decades and much progress has been made in terms of understanding its consequences for individual, team, and organizational outcomes. Our review of this research exposes several levels-related assumptions that have limited theoretical and empirical progress. We isolate the issues that deserve attention, develop an illustrative multilevel model, and offer a number of testable propositions to guide future research on pay structures.

The purpose of this paper is to quantitatively and qualitatively analyze library marketing research output using select bibliometric indicators with the aim of identifying top-performing countries, subject subthemes, organizations, authors and journals in the area.

The present study has examined 520 publications in library marketing, as indexed in Scopus database during 2006–2017. The study has statistically assessed processed publications and citations data into tables in order to ascertain research growth rate trends, global publication output and share, citation impact and distribution of library marketing research by country of publication, international-level collaborating country share of publication, by broad subject areas and preferred media of research communication. The study also provides bibliometric profile of top research organizations and authors in terms of their publications and citation indicators.

The body of literature in library marketing research is still very small, highly scattered and has so far registered no growth during the last 12 years, i.e. 2006–2017. Library marketing research is yet to emerge as a popular research area in library and information science. The USA dominates The library marketing research in the world, whereas other top participating countries including India, China, Japan, Canada, etc., are distant cousins. Top research institutions in the area of library marketing across the world include City University of New York, Florida State University, University of Texas at Austin, USA, Loughborough University and University of Sheffield, UK. The major focus of library marketing research was on academic libraries, followed by public libraries and medical libraries. Of late, the use of social media has emerged as an alternative to traditional library marketing techniques.

The scope of study describing the status of library marketing research is limited to the period 2006–2017. The results of the study should interest researchers in finding solutions to formulating future library strategies and programs for achieving sustainable growth in this area of study.

The present study in library marketing research should be of interest to researchers in evolving, and formulating theories/best practices/and policies/programs for outreach to current and potential users for promoting library products and services.

We currently know little about how transferring can be accomplished when source- and target environments only have little in common. This chapter utilizes the case of EuroCo and AsiaCo to account for how a transfer of interrelated routines across multiple boundaries unfolds. A pragmatic and flexible approach to transferring, where coordinating actors attended to replication and adaptation as means rather than ends, is illuminated. Notably, coordinators split their work into smaller chunks by focusing on artifacts, people, and actions. As pressures to progress the transfer increased, they conceived of new ideas for performances and put the ideas to use along three trajectories focused on embedding, embodying, and enacting routines. Eventually, they blended performances from each trajectory back together into a new overarching notion of what was to be transferred. In elaborating on and discussing these findings, the chapter contributes to literature on routine transfer. Boundary conditions and avenues for future research are discussed.

This paper deals with the estimation of the stress–strength reliability R = P(X < Y), when X and Y follow (1) independent generalized gamma (GG) distributions with only a common shape parameter and (2) independent Weibull random variables with arbitrary scale and shape parameters and generalize the proposal from Kundu and Gupta (2006), Kundu and Raqab (2009) and Ali et al. (2012).

First, a closed form expression for R is derived under the conditions (1) and (2). Next, sufficient conditions are given for the convergence of the infinite series expansions used to calculate the value of R in case (2). The models GG and Weibull are fitted by maximum likelihood using Broyden–Fletcher–Goldfarb–Shanno (BFGS) quasi-Newton method. Confidence intervals and standard errors are calculated using bootstrap. For illustration purpose, two real data sets are analyzed and the results are compared with the existing recent results available in the literature.

The proposed approaches improve the estimation of the R by not using transformations in the data and flexibilize the modeling with Weibull distributions with arbitrary scale and shape parameters.

The proposals of the paper eliminate the misestimation of R caused by subtracting a constant value from the data (Kundu and Raqab, 2009) and treat the estimation of R in a more adequate way by using the Weibull distributions without restrictions in the parameters. The two cases covered generalize a number of distributions and unify a number of stress–strength probability P(X < Y) results available in the literature.

Reverse supply chain (RSC) is an extension of the traditional supply chain (TSC) motivated by environmental requirements and economic incentives. TSC management deals with planning, executing, monitoring, and controlling a collection of organizations, activities, resources, people, technology, and information as the materials and products move from manufacturers to the consumers. Except for a short warranty period, TSC excludes most of the responsibilities toward the product beyond the point of sale. However, because of growing environmental awareness and regulations (e.g. product stewardship statute), TSC alone is no longer an adequate industrial practice. New regulations and public awareness have forced manufacturers to take responsibilities of products when they reach their end of lives. This has necessitated the creation of an infrastructure, known as RSC, which includes collection, transportation, and management of end-of-life products (EOLPs). The advantages of implementing RSC include the reduction in the use of virgin resources, the decrease in the materials sent to landfills and the cost savings stemming from the reuse of EOLPs, disassembled components, and recycled materials. TSC and RSC together represent a closed loop of materials flow. The whole system of organizations, activities, resources, people, technology, and information flowing in this closed loop is known as the closed-loop supply chain (CLSC).

In RSC, the management of EOLPs includes cleaning, disassembly, sorting, inspecting, and recovery or disposal. The recovery could take several forms depending on the condition of EOLPs, namely, product recovery (refurbishing, remanufacturing, repairing), component recovery (cannibalization), and material recovery (recycling). However, neither the quality nor the quantity of returning EOLPs is predictable. This unpredictable nature of RSC is what makes its management challenging and necessitates innovative management science solutions to control it.

In this chapter, we address the order-driven component and product recovery (ODCPR) problem for sensor-embedded products (SEPs) in an RSC. SEPs contain sensors and radio-frequency identification tags implanted in them at the time of their production to monitor their critical components throughout their lives. By facilitating data collection during product usage, these embedded sensors enable one to predict product/component failures and estimate the remaining life of components as the products reach their end of lives. In an ODCPR system, EOLPs are either cannibalized or refurbished. Refurbishment activities are carried out to meet the demand for products and may require reusable components. The purpose of cannibalization is to recover a limited number of reusable components for customers and internal use. Internal component demand stems from the component requirements in the refurbishment operation. It is assumed that the customers have specific remaining-life requirements on components and products. Therefore, the problem is to find the optimal subset and sequence of the EOLPs to cannibalize and refurbish so that (1) the remaining-life-based demands are satisfied while making sure that the necessary reusable components are extracted before attempting to refurbish an EOLP and (2) the total system cost is minimized. We show that the problem could be formulated as an integer nonlinear program. We then develop a hybrid genetic algorithm to solve the problem that is shown to provide excellent results. A numerical example is presented to illustrate the methodology.

The purpose of this paper is to find industry wise differences in relationship between onboarding experience (OE) and turnover intention (TI). An attempt has been made to find the intervening role of psychological variables such as locus of control and self-efficacy.

An online questionnaire was administered to 596 newcomers in five industrial sectors: fast-moving consumer goods, information technology (IT), pharmaceuticals, automobile manufacturing, and hospitality. The questionnaire measured the constructs of locus of control, self-efficacy, perceived OE, and TI.

Inverse relationship was found between perceived OE and TI. There is a significant positive relationship between motivation-based self-efficacy and TI which is mediated through OE. Affective self-efficacy moderates the impact of OE on TI, such that for individuals with low efficacy the inverse relationship between OE and TI is strong, but for individuals with high self-efficacy the relationship between the two variables is direct. The OE in automobile manufacturing industries is significantly higher than other industries. TI is significantly higher in IT and hospitality industries. Exploratory factor analysis of the instrument on OE led to extraction of four factors. Based on socialization resource theory, they were termed as orientation, socialization, task characteristics and leadership. Aspects of socialization and leadership are most significant factors in determining TI across industrial sectors, whereas in case of the hospitality and automobile manufacturing sector it was found that better the task characteristics higher is the chance of TI.

There are limited studies linking various aspects of OE with TI across industries, especially in the Indian context. So, this will be the unique contribution of this research.

The purpose of this paper is to propose an interpretive structural modeling (ISM) model which highlights the relationships between the identified just-in-time (JIT) elements useful for the implementation of JIT in maintenance and understand mutual influences of these identified JIT elements on JIT implementation in maintenance. Further, this paper seeks to identify dependence power and driving power of identified JIT elements using an ISM and Matrice d’Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis.

The methodology used in the paper is ISM with a view to evolving mutual relationships among JIT elements. The identified JIT elements have been further classified, based on their dependence power and driving power using MICMAC analysis.

This paper has developed the relationships among 16 identified JIT elements using the ISM methodology. Further, this paper analyses the driving power and dependence power of identified JIT elements with the help of MICMAC analysis. The incorporated approach is developed here, as the ISM provides only binary correlation among identified JIT elements. The MICMAC analysis is adopted here as it is useful in specific examination related to driving and the dependence power of identified JIT elements. The ISM developed model and MICMAC analysis finding are validated with the help of industrial experts.

The weightage and validation for the ISM and MICMAC analysis are obtained throughout the opinion of academics and industry experts. Further hypothesis may be conducted to examine the validity of the planned model, and structural model may also be validated statistically with the help of structural equation modeling.

The ISM model development and MICMAC analysis of identified JIT elements provide academics and maintenance managers a macro picture of the profits gained by the organizations by the implementation of JIT in maintenance of an organization.

The results will be useful for maintenance managers to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance of an organization.

The purpose of this paper is to identify and quantify the key components of the overall cost of software development when warranty coverage is given by a developer. Also, the authors have studied the impact of imperfect debugging on the optimal release time, warranty policy and development cost which signifies that it is important for the developers to control the parameters that cause a sharp increase in cost.

An optimization problem is formulated to minimize software development cost by considering imperfect fault removal process, faults generation at a constant rate and an environmental factor to differentiate the operational phase from the testing phase. Another optimization problem under perfect debugging conditions, i.e. without error generation is constructed for comparison. These optimization models are solved in MATLAB, and their solutions provide insights to the degree of impact of imperfect debugging on the optimal policies with respect to software release time and warranty time.

A real-life fault data set of Radar System is used to study the impact of various cost factors via sensitivity analysis on release and warranty policy. If firms tend to provide warranty for a longer period of time, then they may have to bear losses due to increased debugging cost with more number of failures occurring during the warrantied time but if the warranty is not provided for sufficient time it may not act as sufficient hedge during field failures.

Every firm is fighting to remain in the competition and expand market share by offering the latest technology-based products, using innovative marketing strategies. Warranty is one such strategic tool to promote the product among masses and develop a sense of quality in the user’s mind. In this paper, the failures encountered during development and after software release are considered to model the failure process.