Areas Of Management Research Research Articles

Navigating the PhD journey: a collective consideration of junior academics in qualitative accounting and management research

Purpose The purpose of this paper revolves around the informal coming together of various doctoral students in the area of qualitative accounting and management research and the attempt to learn from their respective experiences. Together, the authors share personal reflections and valuable insights in revealing their vulnerabilities, aspirations and how they make sense of the PhD journey and their becoming as academics. Design/methodology/approach This paper builds on an open discussion and written reflections among the authors, who represent a diverse set of both doctoral students at various levels and recent graduates from different countries, schools and backgrounds. Findings The discussion highlights the struggles the authors experience as doctoral students, how they learn to cope with them as well as how they are socialized throughout their PhD journey. This allows them to take a critical stance towards increased productivity demands in academia and to embrace doctoral students as a powerful collective, whose aspirations may inspire a change of academic reality for the better. Originality/value While guidance on how to succeed as doctoral students is common, we seldom hear about doctoral students as particularly “fragile selves” (Knights and Clarke, 2014) who, as opposed to more established scholars, are more actively experiencing difficulties with finding their ways in academia. The authors are thus motivated to create a rare common voice of a group of doctoral students here by providing a more intimate account of the PhD journey.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Tackling Infectious Diseases with Rapid Molecular Diagnosis and Innovative Prevention.

Infectious diseases (IDs) are a leading cause of death. The diversity and adaptability of microbes represent a continuing risk to health. Combining vision with passion, our transdisciplinary medical research team has been focussing its work on the better management of infectious diseases for saving human lives over the past five decades through medical discoveries and innovations that helped change the practice of medicine. The team used a multiple-faceted and integrated approach to control infectious diseases through fundamental discoveries and by developing innovative prevention tools and rapid molecular diagnostic tests to fulfill the various unmet needs of patients and health professionals in the field of ID. In this article, as objectives, we put in context two main research areas of ID management: innovative infection prevention that is woman-controlled, and the rapid molecular diagnosis of infection and resistance. We also explain how our transdisciplinary approach encompassing specialists from diverse fields ranging from biology to engineering was instrumental in achieving success. Furthermore, we discuss our vision of the future for translational research to better tackle IDs.

Огляд досліджень в напрямку міоелектричного методу керування біонічним протезом

Myoelectric control of bionic prostheses is an important field of research in the field of rehabilitation. Intuitive and intelligent myoelectric control can restore upper limb function. However, much research now focuses on the development of various myoelectrical and biotechnical control methods, limiting research to the complex daily tasks of prosthetic manipulation, such as grasping and releasing. The article examines the latest advances in the research areas of bionic prosthesis management. In particular, attention is paid to the methods of determining movement intentions, classification of discrete movements, estimation of continuous movements, single-channel control, feedback control and combined control. Motor neurons group input signals from the central nervous system that affect muscles and form motor units. The electromyography (EMG) signal, which is obtained by recording motor neuron action potentials, reflects muscle activity. This signal, oscillating within ±5000 μV with a frequency of 6 to 500 Hz, reflects the characteristics of muscle contraction. Depending on the location of the sensors, EMG signals are divided into intramuscular and surface electromyography. Intramuscular electromyography provides an accurate study of muscle activation, but requires the implantation of sensors, which can lead to physical problems. EMG, which captures a signal from the surface of the skin, is easier to use and is widely used in experiments with myoelectric prostheses.

Has the World of Finance Changed? A Review of the Influence of Artificial Intelligence on Financial Management Studies

Artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made. It has the ability to improve accuracy and efficiency while also lowering costs and providing fresh insights into financial markets and investments. This review will look at some of the important areas of financial management research that AI has influenced. One of the most notable effects of AI on financial management research has been in risk management. AI algorithms, with their ability to analyze vast amounts of data, can detect patterns and anomalies that humans may not notice right away and have had a significant impact on financial forecasting and prediction, generating predictions about future market movements and investment opportunities by analyzing historical data, market trends, and other relevant factors. In conclusion, artificial intelligence (AI) has had a tremendous impact on financial management research, revolutionizing how financial data is analyzed and decisions are made, with significant potential to disrupt traditional financial management techniques. AI has the ability to increase accuracy and efficiency, cut costs, and provide fresh insights into financial markets and investments. As AI technology advances, its impact on financial management studies is likely to rise.

Evolutionary-Game-Theory-Based Epidemiological Model for Prediction of Infections with Application to Demand Forecasting in Pharmaceutical Inventory Management Problems

Pharmaceuticals play a critical role in the eradication of infectious diseases. Effective pharmaceutical inventory management is important for controlling epidemics since medical resources such as pharmaceuticals, medical staff, and hospitals are limited. In this study, a novel epidemiological model is proposed to evaluate the resource requirements for pharmaceuticals and is applied to analyze different pharmaceutical inventory management strategies. We formulate the relationship between the number of infected individuals and the risk of infection to account for virus mutation. Evolutionary game theory is integrated into an epidemiological model to represent human behavioral choices. The proposed model can be developed to forecast the demand for pharmaceuticals and analyze how human behavior affects the demand of pharmaceuticals. This study found that making people aware of the risk of disease has a positive impact on both reducing the number of infections and managing the pharmaceutical inventory. The main contribution of this study is to enhance areas of research in pharmaceutical inventory management. This study revealed that the correct recognition of the risk of disease leads to appropriate pharmaceutical management. There are a few studies on the application of infectious disease models to inventory control problems. This study provides clues toward proper pharmaceutical management.

Strategic divestment of emerging market firms: the influence of returnee managers and CEO characteristics

PurposeThis study aims to examine the role of returnee managers that can affect the strategic-divestment decision of emerging-market firms (EM firms). Drawing on arguments from the upper echelons theory and international human resource mobility perspectives, this study aims to propose that returnee managers influence corporate divestitures when the business outlook is negative. In addition, this study aims to examine the interplay between returnee managers and CEOs, whose characteristics can foster or undermine the efforts of returnee managers to engage in corporate divestments.Design/methodology/approachThis study examines 278 firms from nine emerging economies. The negative binomial regression was employed to estimate the model. In the robustness checks, the logistic regression was adopted to confirm the earlier findings.FindingsThe empirical results support the notion that returnee managers strengthen the relationship between firm performance and divestments. Because of the limited liabilities of foreignness and outsidership, returnee managers can gain social trust and credibility through communication and social interaction. Furthermore, the results provide mixed support for the moderating effect of CEO characteristics on the performance–divestment relationship.Practical implicationsThis study reveals that returnee managers are a great asset for EM firms that aim to find synergies and upgrade their capabilities through asset reconfiguration, which is an essential activity of emerging market firms to integrate themselves into the global competition. Meanwhile, CEO characteristics can foster (through their education level) or hinder (due to their age) divestment attempts, influenced by returnee managers.Originality/valueThis study explores an understudied phenomenon in international business (IB): strategic divestment of EM firms. The literature that examines strategic divestment and corporate refocusing in emerging markets is extremely limited. Furthermore, this study explores the novel topic that intersects the international business (IB) and international human resource management (IHRM) research areas. Specifically, this study investigates the impact of returnee managers on strategic divestments.

COVID-19 and the Production of Knowledge on Hotel Management

ABSTRACT The outbreak of COVID-19 has dramatically exposed the vulnerability of the hotel industry, and has inevitably reshaped the production of knowledge about hotel management during the pandemic. Yet, thus far little research has examined how the pandemic has impacted the production of knowledge on hotel management. The present article reports on a bibliometric analysis that reviewed 580 hotel studies published during the evolving course of the pandemic. Four broad areas of research themes were identified, including “COVID-19 anxiety and impacts,” “customers” satisfaction and trust,” “sustainable practices” and “turnover intention and job insecurity of hotel employees.” The findings thus provide an overview of the frontier of research areas of hotel management disrupted by the pandemic. The research further documents the drift in the research focus that took place as the pandemic went through its successive phases.

Evolutionary Study of Three Decades Literatures Related to Disaster Management Agency and Public Policy: A Bibliometric Study

This study is aimed at examining scientific literatures written by international authors focusing on disaster management agency topic that are related to public policy from the first paper published in 1995 to 2022 using bibliometric study employing a database from Scopus. To visualize the results, this study utilizes VOSviewer version 1.6.17. The results showed that this topic emerged in Thailand which focused on roles of local level disaster management agency in carrying out rehabilitation and reconstruction activities in district level. However, Federal Emergency Management Agency, or the United States Disaster Management Agency, was considered to be the most researched disaster management agency. Besides, open-access social science articles dominated the type of publications in this research area. Moreover, the United States and its authors were perceived as the most productive and influential country and authors. Meanwhile, the United Kingdom was recognized as the most influential sources title with Journal “Disasters” served the most productive and influential source title. Besides, the affiliation among Austria, Nigeria, South Africa, and the United States were acknowledged as the most productive and influential affiliations. The analysis also indicated that this topic developed and strongly connected to disaster and disaster management research areas. Considering the comprehensive nature of bibliometric analysis, this study may help scholars get a broad view of the relevant issues, while at the same time identifying reputable publications worth studying for reference.

State of the Art on Relevant Research Areas Connected to Complexity Management

The article is focus on describing the connected or associated concepts and disciplines for managing complexity. The approach will follow: (1) the holistic perspective; (2) the specific approach of complexity management (service-related); (3) the special (nonservice-oriented) research areas of complexity management; (4) characteristics of complexity in outsourcing projects. In the final chapter, relevant conclusions related to the state of the art will be made. The aim of the article is to emphasize the necessity of viewing complexity management as a new organizational challenge and initiatives. The main finding of the research consists of the identification of different knowledge sources related to complexity management connected disciplines that have to be considered by managers in order to define successful strategy, methodologies and processes.

Towards a taxonomy of waste management research: An application of community detection in keyword network

Our global environment is facing significant waste and waste management challenges, despite minor differences in definitions and what constitutes waste. In recent decades, waste has been the subject of a great deal of research since civilized society needs to manage waste efficiently and effectively. As a result of all of these issues, researchers have focused on improving waste management. To do so, a taxonomy based on the research conducted on waste management will help them. Research taxonomies and visualizations help us find the most important areas of research in waste management. There are a few researchers who taxonomize research waste according to their methodology, but there is no quantitative and data-oriented approach. By using community detection and social network analysis, this study aims to quantify and systematize waste area taxonomy.There have been 9095 articles published on waste management since 2000, and these articles were retrieved from the WOS database to create a keyword network made up of 17,808 members (unique keywords) and 79,763 links in this study. To discover the research areas of waste management, the community detection technique was used in the keyword network, and 10 main communities that represent the main research areas of waste management were discovered and named based on waste management characteristics and waste types. These 10 communities include more than 90% of all waste management research.In the next step and to achieve a hierarchical taxonomy of waste management studies, the community detection algorithm was implemented in ten communities and by discovering sub-communities, a waste management taxonomy with 10 main branches and 26 sub-branches was presented.

Open strategy as a catalyst for innovation: Evidence from cross-sector social partnerships

A principal advantage of open strategy as a decision-making paradigm is the magnification of both the accumulation and heterogeneity of knowledge. Since a key to innovation is the coalescing of dispersed knowledge, open strategy has been an attractive area of scholarly management research over the past two decades. Despite this attention, there has been a paucity of empirical studies on open strategy. Its predictions have not been adequately tested, its boundaries have not been enumerated, nor is the science settled on its application in organizational decision-making. To address this lacuna and to further explore the links between open strategy and innovation, we develop and refine the construct of open strategy and assess its relationship with a common predictor of innovation, absorptive capacity. Within this research, we assess the convergent, discriminant, and predictive validity of the open strategy construct to theoretically refine, validate, and measure it for use in future analysis.

Business and management research trends of sustainability assessment in the food sector

PurposeThis study aimed to identify research trends and topics in sustainability assessment in the food sector within the scope of economics, business and management research.Design/methodology/approachThe authors conducted a bibliometric analysis by applying a rigid and systematic research protocol, employing bibliometric techniques and a keyword co-occurrence network. Further, the Visualization of Similarities viewer software was used to analyse publications between 1994 and 2021.FindingsThe knowledge trends regarding “sustainability assessment” in the research area of business economics demonstrated a notable evolution of the topics analysed, ranging from indicators and policy analyses in agriculture and fishery sectors, to the topic of sustainability life cycle assessment (LCA) and management systems. In business and management research areas, the main theoretical frameworks used for sustainability assessment in the food sector included the triple-bottom line (TBL) and the LCA, and the main research topics were food chain logistics, bio-based products, retailers, consumption patterns and crop-based biodiesel options.Research limitations/implicationsThis study employed only the WoS database and future studies could incorporate other scientific databases. Regarding future research, more emphasis could be laid on food retailers given their prevalence as demonstrated by the study and past research.Practical implicationsThe findings can help all participants in the food global value chain to make better decisions to guide their sustainability efforts and assessment. Moreover, this research reveals that companies need to be actively engaged with their stakeholders and pay special attention to consumer patterns and perceptions.Originality/valueThe bibliometric research focus on business and management research areas using the Web of Sciences categories, starting from the research area of business economics to the analysis of the food sector's sustainability assessment.

A Review on the Applications of PSO-Based Algorithm in Demand Side Management: Challenges and Opportunities

The increase in energy consumption, environmental pollution issues, and low-carbon agenda has grown the research area of demand side management (DSM). DSM programs provide feasible solutions and significantly enhance the efficiency and sustainability of electrical distribution systems. This paper classifies and discusses the broad definition of DSM based on the comprehensive literature study considering demand response and energy efficiency. The implementation of Artificial Intelligence algorithms in DSM applications has been employed in many studies to help researchers make optimal decisions and achieve predictions by analyzing the massive amount of historical data. Owing to its simplicity and consistent performance in fast convergence time, Particle Swarm Optimization (PSO) is widely used as a part of the swarm AI algorithm and has become a prominent technique in the optimization process to exploit the full benefit of the demand-side program. The variants of PSO have been developed to overcome the limitations of the original PSO and solve the high complexity and uncertainty in the DSM optimization process. The proposed PSO-based algorithm can optimize consumers’ consumption curves, reducing the peak demand and hence minimizing the electricity cost when integrated with the DR programs or EE measures. The research works of the PSO algorithm in DSM have seen an increasing trend in the past decade. Therefore, this paper reviewed the application of the PSO-based algorithm in DSM fields with some constraints and discussed the challenges from the previous work. The potential for new opportunities is identified so that PSO methods can be developed for future research.

Topic Discovery in Operations Management Research Area Based on LDA Topic Modeling

Topic Discovery in Operations Management Research Area Based on LDA Topic Modeling

Industrial Performance Measurement Systems Coherence: A Comparative Analysis of Current Methodologies, Validation and Introduction to Key Activity Indicators

This paper discusses and integrates the concept of complexity in the industrial performance measurement and management systems (PMM) theory, providing a comprehensive overview of the different methodologies used within the decision systems research area. It also discusses the importance of introducing Key Activity Indicators (KAI) within PMM, specifically related to the Operations and Supply Chain management research and industrial areas. Moreover, it provides validation of the methodology through a case study concerning the production environment of a multinational pharmaceutical company. The main research objective is to design appropriate industrial PMM systems with the aim of increasing the industrial efficiency and effectiveness of manufacturing and service organizations. An analysis of the central industrial performance measurement systems design methods is conducted, classifying them into macro-categories and conducting a comparative study. Based on the analysis of the different proposed methods, organisations will be able to choose the best one based on their needs to design effective decision systems. The research work allows organisations to evaluate, assess, and design effective industrial performance measurement systems. Moreover, the proposed methodology can be easily integrated within an Industry 4.0 context, and benefit from the digitalization environment to obtain continuous feedback on the effectiveness of the industrial PMM.

Executives in Politics

We study the involvement of corporate executives in U.S. politics over the last 40 years. First, we document that the share of business politicians in federal elected office increased from 13.3% in 1980% to 22.6% in 2018, with most of the increase occurring over the last two decades. Second, we find that business politicians enjoy an early fundraising advantage over their opponents, both because they are more likely to self-fund their campaigns and because they receive more campaign contributions from their firms. Third, the election of business politicians benefits their industries and firms, which experience positive abnormal stock returns when their executives win political office. We also show that business politicians, once elected, vote for policies that shift the balance of power toward corporate interests. Using close elections for identification, we show that this policy shift cannot be attributed solely to the changes in the underlying preferences of the electorate. Overall, our results indicate that corporate executives have become more involved in U.S. politics and that this involvement has benefited business interests and affected aggregate legislative outcomes. This paper was accepted by Gustavo Manso, finance. Funding: The authors thank the Digital Age Management Research Area at the China Europe International Business School (CEIBS) and Centre for Applied Research on International Markets, Banking, Finance and Regulation (BAFFI CAREFIN) at Bocconi University for financial support. The Area and the Center played no role in study design, data collection and analysis, or preparation of the manuscript. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4595 .

Social impact operations at the global base of the pyramid

In recent years, our field has seen an increase in research that explicitly emphasizes an objective of social impact in the most unprivileged parts of the world—the so‐called global base of the pyramid (BOP; referring to the 2.7 billion people living on less than $2.50 per day, the largest but most resource‐poor economic group globally). This trend seems to cut across the traditional application areas of operations management (OM) and operations research (OR), and it coincides with an increased emphasis on environmental, social, and corporate governance (ESG) values in industry, a greater prominence of the United Nations' Sustainable Development Goals (UN SDGs), and increases in social impact research in other academic fields. In this paper, we pull together representative examples from our field of what we consider as social impact research aimed at improving living conditions at the BOP. We first examine the scale and scope of work published in Production and Operations Management over the last 25 years, and then provide a broader summary of the spectrum of research within OM and OR that constitute this stream of literature. We adopt the stance that OM and OR should embrace the current societal emphasis on social responsibility and positive social impact—and strive to contribute to the most pressing problems for those living at the BOP. Although our field has produced a body of work addressing such problems, individual research projects of this type are usually not viewed as falling under the broad umbrella of social impact operations (SIO), but rather are classified as part of the closest application area. By providing an initial overview of this work, we wish to celebrate the contribution of our field to this area, highlight common themes, catalyze a dialogue across application areas among researchers with a common perspective, and identify opportunities for future research.

The Possibilities and Limitations of Using Google Books Ngram Viewer in Research on Management Fashions

Google Books Ngram Viewer (GNV) is an analytical tool that uses quantitative methods to analyze digitized text. This paper looks at the possibilities and limitations of using GNV in management fashion research, an area of management research that examines the lifecycle and evolution of management concepts and ideas. GNV provides a historical big picture of the lifecycle and popularity of specific terms and phrases in books. It is argued that this tool could have a natural application in the study of management fashions, since books are a medium through which popular management concepts and ideas have traditionally been diffused. The paper provides several illustrative examples of how GNV can be applied to study management fashions and identifies the tool’s main possibilities and limitations. Although GNV has obvious advantages such as accessibility and user-friendliness, researchers should exercise caution, as it only provides a partial picture of the impact of management fashions.

Employee Engagement In Organizations: A Study Done On The Operational Employees Of The 5-Star Hotels In Kolkata

Background: Employee engagement is one of the most recent areas of research in management. It is the basis for any organization’s success. The engaged workforce shows its physical and mental commitment, generates energy on the roles to be performed, and gives productivity to a maximum extent which helps in achieving overall business goals. Objective: The major objective of this study was to determine the level of employee engagement of the operational staff. Methodology: The questionnaire was prepared to test the knowledge, employee engagement levels, and organizational culture levels of the employees. Responses were recorded via the online medium from 100 samples (50 males and 50 females), the majority of them were in the age group of 20 to 25 years and were operational employees of 5-star hotels in Kolkata. A 5-point Likert scale was used. Result: The average knowledge score of the sample was 5.45, which indicated great awareness of the concept. The sample achieved an average score of 39.92 in employee engagement, 24.2 in job involvement, 40.59 in empowerment, and 24.78 in cognition, all of which were in the ‘high’ range. The average organizational culture score achieved by the sample was 89.57. Pearson’s correlation coefficient was calculated for the two variables, employee engagement, and organizational culture, and the correlation coefficient was, 0.534 among males and 0.569 among females. The values of the correlation coefficient indicated a positive correlation among the variables. Conclusion: The findings of this study contributed to the knowledge and understanding of the selected factors, which leads to a better understanding of organizational practices that will lead to employee engagement.