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Operations management.

This case study is intended for MBA, final year industrial engineering and 1st year PhD students, for use in graduate engineering, post graduate and executive level management programs. The case study illustrates operational and participative management control system in a matrix and flexible organization structure.

Satish Arora (CEO) and Praveen Arora (Director Finance), a husband and wife team, own and operate Go-Goal Hydro Power Ltd (Go-GoalHPL) as a small medium enterprise (SME). Go-GoalHPL renovates hydro power generating machines up to 250 MW rating. Their current renovation/overhauling projects are located at different sites in India. Go-GoalHPL has grown its business by pursuing new avenues that include execution of major renovation projects and construction of new projects on a turnkey basis. Go-GoalHPL's management, despite their on-going successes, are concerned about severe capacity shortages if immediate actions were not taken. They have identified three capacity expansion options: continue current operating practices and obtain additional production space; undertake a make-versus-buy study and consider outsourcing parts; and implement world-class manufacturing techniques through adoption of focused factories. The first two options represented simple incremental changes while the third presents a radical alternative that required a major reorganization of the company operations and support functions.

These include knowledge about competitiveness, corporate survival, sustainable business, operations management, productivity, performance.

Teaching notes are available for faculty. Please consult your librarian.

Technology acquisition is a common phenomenon of acquiring external knowledge, but we have a limited understanding of conditions in which the acquirer integrates the target or not. On one hand, the acquirer may have a policy to integrate the target to benefit from its prior knowledge. On the other hand, the target may face challenges in continuing its knowledge creation and the acquirer may want to provide it autonomy to not disrupt it. This paper aims to identify conditions in which targets tend to be less integrated after acquisitions, allowing them to maintain more autonomy and contribute more to knowledge creation.

We test our arguments in the empirical setting of the global biopharmaceutical industry using a difference-in-difference approach on a longitudinal dataset of matched patents. We examine self-cites received by patents belonging to acquirers and the targets before and after the acquisitions.

We find that, on average, the targets’ prior patents do not receive more self-cites after the acquisition. We conclude that this is because their R&D activities are disrupted, suggesting a higher level of post-acquisition integration. However, more nuanced findings reveal that it may not be the case all the time. When the target has more research experience, is international or is specialized in complementary technologies, prior patents of targets continue to receive more self-cites after the acquisition. It indicates that the targets in such conditions continue knowledge creation, suggesting a lower level of post-acquisition integration.

Our findings contribute to post-acquisition integration research. While post-acquisition integration downside is common, we present conditions in which such a downside may be less likely. We highlight that the context of an acquisition may be an important determinant of the extent of integration of the target. Moreover, we supplement the integration research (cultural, structural and human resource and leadership perspectives of integration) by adding a knowledge-based perspective to it. Such dynamics have important implications for acquirers and targets in deriving value from the acquisition.

Since 2018, there has been a resurgence in initial public offering (IPO) pricing studies. The authors aim to consolidate the knowledge and explore current dynamics, understand knowledge progression, elicit trends, and provide future research directions for IPO pricing research.

The authors conducted a two-stage hybrid review based on 512 high-quality Scopus articles on IPO pricing published over the last decade. The authors deploy bibliometric analysis, and then, based on 61 curated articles, the authors conduct content analysis and offer future research directions.

Four key research streams emerged: information asymmetry, agency problems, legal, regulatory, and social environment, and behavioral finance. Future research may focus on behavioral explanations for IPO underpricing, the role of investor sentiment in IPO pricing, text analytics, machine learning, and big data in alleviating information asymmetry and agency problems. The authors summarize and present content analysis using the classic Theory, Context, Characteristics, Methods (TCCM) framework.

Using different databases, bibliometric analysis tools, sample period or article screening criteria for the study might give different results. However, the study's major findings are robust to alternative choices.

This study serves as a ready reckoner for the research scholars, practitioners, regulators, policymakers, and investors interested in understanding the nuances of IPO pricing.

The study sheds light on the most influential documents, authors, and journals, offers an understanding of knowledge structure, identifies and discusses primary research streams and related implications, and provides future research directions.

Economic robustness is the heart of any country’s prosperity. It is no wonder that economic progression is a crucial force that keeps a nation rolling on its wheels. As economic well-being is such an important piece in the jigsaw of a country’s health, economic shocks must be kept at a bare minimum. India in recent years has experienced various banking failures and scams that put the financial system at stake and crushed investor confidence. There are examples aplenty of banks collapsing because of siphoning and diversion of funds in risky propositions that ultimately put the depositors and financial institutions at the receiving end. As per Reserve Bank of India’s latest annual report, it has been reported that there have been bank frauds worth a whopping Rs 72,000 crore in the financial year 2019. Therefore, this study aims to obtain the perception of Academicians and Practitioners regarding forensic accounting’s suitability in uncovering bank frauds.

A strategically constructed Likert scale questionnaire was designed to obtain perception of Academicians and Practitioners regarding forensic accounting’s suitability in uncovering bank frauds. The analysis was performed by applying the “non-parametric test” on the data gathered from the questionnaire.

By testing the relevant hypotheses, it has been found that insiders working in the bank team up with outsiders in perpetrating fraudulent activities resulted in bank failures, that both forensic accountant and traditional accountant were different from each other and that adoption of forensic accounting in India will aid regulatory authorities in doing their job more efficiently.

This study uses purposive sampling, as it only takes the perception of college and professional course students (chartered accountants/company secretary/certified management accountant) and teachers having knowledge in accounting which made sample size quite small (156). This study only covers frauds that were conjured in the banking industry and hence does not touch upon scams that occurred in other financial institutions. This study is confined to Indian banks only. This study uses an opinion survey to ensure the suitability of forensic accounting in uncovering bank frauds.

The originality of this study has been checked through Turnitin plagiarism software in which the similarity was 8%.

This paper aims to present the edge scattering dominant circuit modeling. The effect of crosstalk on gate oxide reliability (GOR), along with the mitigation using shielding technique is further studied.

An equivalent distributed Resistance Inductance Capacitance circuit of capacitively coupled interconnects of multilayer graphene nanoribbon (MLGNR) has been considered for T Simulation Program with Integrated Circuit Emphasis (TSPICE) simulations under functional and dynamic switching conditions. Complementary metal oxide semiconductor driver transistors are modeled by high performance predictive technology model that drive the distributed segment with a capacitive load of 0.001 fF, V_DD and clock frequency as 0.7 V and 0.2 GHz, respectively, at 14 nm technology node.

The results reveal that the crosstalk induced delay and noise area are dominated by the overall mean free path (MFP) (i.e. including the effect of edge roughness induced scattering), in contrary to, acoustic and optical scattering limited MFP with the temperature, width and length variations. Further, GOR, estimated in terms of average failure rate (AFR), shows that the shielding technique is an effective method to minimize the relative GOR failure rate by, 0.93e-7 and 0.7e-7, in comparison to the non-shielded case with variations in interconnect’s length and width, respectively.

Considering realistic circuit modeling for MLGNR interconnects by incorporating the edge roughness induced scattering mechanism, the outcomes exhibit more penalty in terms of crosstalk induced noise area and delay. The shielding technique is found to be an effective mitigating technique for minimizing AFR in coupled MLGNR interconnects.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

In recent years, instant messaging platforms like WhatsApp have gained substantial popularity in both academic and practical domains. However, despite this growth, there is a lack of a comprehensive overview of the literature in this field. The primary purpose of this study is to bridge this gap by analyzing a substantial dataset of 12,947 articles retrieved from the Dimensions.ai, database spanning from 2011 to March 2023.

To achieve the authors' objective, the authors employ bibliometric analysis techniques. The authors delve into various bibliometric networks, including citation networks, co-citation networks, collaboration networks, keywords and bibliographic couplings. These methods allow for the uncovering of the social and conceptual structures within the academic discourse surrounding WhatsApp.

The authors' analysis reveals several significant findings. Firstly, the authors observe a remarkable and continuous growth in the number of academic studies dedicated to WhatsApp over time. Notably, two prevalent themes emerge: the impact of coronavirus disease 2019 (COVID-19) and the role of WhatsApp in the realm of social media. Furthermore, the authors' study highlights diverse applications of WhatsApp, including its utilization in education and learning, as a communication tool, in medical education, cyberpsychology, security, psychology and behavioral learning.

This paper contributes to the field by offering a comprehensive overview of the scholarly research landscape related to WhatsApp. The findings not only illuminate the burgeoning interest in WhatsApp among researchers but also provide insights into the diverse domains where WhatsApp is making an impact. The analysis of bibliometric networks offers a unique perspective on the social and conceptual structures within this field, shedding light on emerging trends and influential research. This study thus serves as a valuable resource for scholars, practitioners and policymakers seeking to navigate the evolving landscape of WhatsApp research. The study will also be useful for researchers interested in conducting bibliometric analysis using Dimensions.ai, a free database.