The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal…
Abstract
Purpose
The purpose of this manuscript, a state feedback gain depends on the optimal design of fractional order PID controller to time-delay system is established. In established optimal design known as advanced cuttlefish optimizer and random decision forest that is combined performance of random decision forest algorithm (RDFA) and advanced cuttlefish optimizer (ACFO).
Design/methodology/approach
The proposed ACFO uses the concept of crossover and mutation operator depend on position upgrading to enhance its search behavior, calculational speed as well as convergence profile at basic cuttlefish optimizer.
Findings
Fractional order proportional-integrator-derivative (FOPID) controller, apart from as tuning parameters (kp, ki and kd) it consists of two extra tuning parameters λ and µ. In established technology, the increase of FOPID controller is adjusted to reach needed responses that demonstrated using RDFA theory as well as RDF weight matrices is probable to the help of the ACFO method. The uniqueness of the established method is to decrease the failure of the FOPID controller at greater order time delay method with the help of controller maximize restrictions. The objective of the established method is selected to consider parameters set point as well as achieved parameters of time-delay system.
Originality/value
In the established technique used to evade large order delays as well as reliability restrictions such as small excesses, time resolution, as well as fixed condition defect. These methods is implemented at MATLAB/Simulink platform as well as outcomes compared to various existing methods such as Ziegler-Nichols fit, curve fit, Wang method, regression and invasive weed optimization and linear-quadratic regression method.
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Yashwin Anand, Benny Godwin J. Davidson, Jossy P. George and Peter V. Muttungal
The primary purpose of this paper is to examine the role of perceived trust, information quality, positive word of mouth and societal norms toward real estate purchase intention…
Abstract
Purpose
The primary purpose of this paper is to examine the role of perceived trust, information quality, positive word of mouth and societal norms toward real estate purchase intention. The study also examines how pro-environmental self-identity mediates the relationship between positive word of mouth and real estate purchase intent, as well as between societal norms and real estate purchase intention. This research aims to delve into these intricate dynamics through a multidimensional lens.
Design/methodology/approach
The research employs existing scholarly works and measurable variables evaluated through a five-point Likert scale, hypothesis testing and mediation analysis to examine the proposed framework. A structured survey comprising six sections was administered, yielding 385 valid responses. The data analysis process included the use of confirmatory factor analysis and structural equation modelling techniques.
Findings
The analysis indicates that pro-environmental self-identity has the most significant influence on real estate purchase intention, closely followed by positive word of mouth. Incorporating eco-friendly themes in marketing campaigns significantly boosts purchase intentions. However, perceived trust does not significantly impact purchase intentions. Other factors, such as information quality and societal norms, also play significant roles, underscoring the importance of understanding the complex dynamics shaping consumer decisions in the real estate market.
Research limitations/implications
This research exclusively targets responses from young consumers in specific regions of India. Future studies should aim for a more extensive geographic scope, encompassing a diverse global population for a broader understanding of the subject.
Originality/value
Based on previous literature, this study is the first to identify the elements influencing the inclination to buy environmentally friendly real estate through social commerce.
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Ishika Pradeep, Jossy P. George and Benny Godwin J. Davidson
This study aims to determine website quality, young adult socialization and dark triad personality as the factors influencing the real estate purchase decision. In addition, this…
Abstract
Purpose
This study aims to determine website quality, young adult socialization and dark triad personality as the factors influencing the real estate purchase decision. In addition, this study also measures the mediating effects of young adult socialization on real estate purchase buying behavior.
Design/methodology/approach
Related literature, quantifiable variables with a five-point Likert scale, hypothesis testing and mediators are used to study the model. A systematic questionnaire that was divided into four sections was used. A total of 336 valid responses were collected and analyzed through a structural equation model.
Findings
The results suggest that dark triad personality and young adult socialization considerably affect real estate purchase decisions. The development proves website quality does not significantly impact real estate purchase behavior.
Research limitations/implications
This study is limited to a few young consumers’ responses. Future studies could be more widespread globally and should include more variables and offline methods of purchasing behavior.
Originality/value
As per the review of existing literature, this research is the first, to the best of the authors’ knowledge, to determine the factors affecting the real estate purchase decision with factors like website quality, dark triad personalities and young adult socialization involving it.
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Aakanksha Uppal, Yashmita Awasthi and Anubha Srivastava
This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing…
Abstract
Purpose
This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance.
Design/methodology/approach
In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employee’s performance meets expectations or needs improvement.
Findings
All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce.
Research limitations/implications
The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the model’s effectiveness across various contexts.
Practical implications
The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment.
Social implications
Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance.
Originality/value
This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach.
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Thomas T.H. Wan and George J. Wan
This commentary presents the analytic development of patient classification, health resource use and outcome research and identifies opportunities to perform longitudinal research.
Abstract
Purpose
This commentary presents the analytic development of patient classification, health resource use and outcome research and identifies opportunities to perform longitudinal research.
Design/methodology/approach
The authors use a transdisciplinary framework to formulate multilevel models for ascertaining the causal relationship between hospital efficiency and effectiveness in panel data analysis.
Findings
The longitudinal design of organization research enables to delineate the relationship between hospital performance and quality of care in future research.
Research limitations/implications
The inclusion of multivariates in health organization research and modeling is pivotal to the identification of a comprehensive set of predictor variables. The authors signify the need to build a systems-oriented theoretical framework to integrate micro- and macro-level predictor variables in conducting data analysis.
Practical implications
The authors signify the need to build a theoretical framework to integrate micro- and macro-level predictor variables in conducting data analysis.
Social implications
Health organization research is essential to broaden the scope of health services research and policy development, particularly related to global health as noted in the promotion of sustainable development and health goals.
Originality/value
Health organization research should include a complex set of exogenous and endogenous variables in designing and modeling the determinants of hospital performance and patient care outcomes.
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Barbara Deladem Mensah, George Tweneboah, Simplice Asongu and Thomas Elorm Tagbotor
This study aims to examine the growth effects of foreign direct investment (FDI) inflows, financial development and institutional quality in emerging economies. The research…
Abstract
Purpose
This study aims to examine the growth effects of foreign direct investment (FDI) inflows, financial development and institutional quality in emerging economies. The research covers 24 years of panel data of 33 selected emerging economies for the period spanning from 1996 to 2020.
Design/methodology/approach
The Pedroni and Westerlund panel cointegration tests were performed to ascertain a long run relationship among the studied variables while the panel quantile regression approach was applied to account for the heterogeneous effect of the exogenous variables on economic growth.
Findings
The study revealed among other things that FDI inflows, financial development and institutional quality all have positive effects on economic growth in the selected emerging economies. It further revealed that the growth effects of these explanatory variables are evidently heterogeneous.
Research limitations/implications
The implications of this study include the need to increase FDI inflows, especially environmentally friendly FDIs, establish a well-developed financial sector and improve institutional quality so as to accelerate growth in the selected emerging economies.
Originality/value
This paper contributes to extant literature by answering the question of whether the growth effects of FDI inflows, financial development and institutional quality can differ in sign and or magnitude depending on the performance of a country’s growth. The findings of this study may help governments and policymakers to develop very good growth-promoting policies in accordance with the behavior of productivity growth.
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This paper aims to use the origin story of Dalhousie’s Faculty of Management as a foil for unpacking the tensions between deep disciplinary specialization and liberal education in…
Abstract
Purpose
This paper aims to use the origin story of Dalhousie’s Faculty of Management as a foil for unpacking the tensions between deep disciplinary specialization and liberal education in business schools in Canada and the USA. Ultimately, the paper reveals that those tensions are not irreconcilable, and that through the fortunes of historical contingencies and deliberate decision-taking, a faculty can embrace the benefits of both breadth and depth.
Design/methodology/approach
The paper proposes a critical organizational history of management education through a case study. By drawing on secondary literature and archival sources, the authors focus on moments in business education, such as the founding of the Wharton School of Business, the release of the Carnegie and Ford Reports and the trend towards increased specialization to situate a case study of Dalhousie University’s Faculty of Management.
Findings
The authors find that the evolution of business education in North America from its broad, liberal origins towards narrow, specialization has come at a cost to some of the benefits of business and management education. An alternative approach, one reflected in the design of Dalhousie’s Faculty of Management, its programme offerings and its interconnection with other disciplines, enables the advantages of deep disciplinarity to co-exist (and cross-inform) with the advantages of liberal approach to knowledges.
Originality/value
The Dalhousie model offers business schools an example of a faculty that balances the rich insights of liberal interdisciplinarity with the need for sophisticated approaches to more granular, often disciplinary, topics. In addition, the paper offers the story of a multidisciplinary management faculty, some explanation for how that faculty was maintained despite pressures towards specialization; and in doing so, contributes to the limited historical research of management education, particularly in Canada, post-2000.
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Ying Yang, Biao Yang, Hung Nguyen and George Onofrei
Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for…
Abstract
Purpose
Data mining has been well-applied by maintenance service providers in identifying data patterns and supporting decision-making. However, when applying data mining for analytics-driven maintenance, maintenance service providers often adopt data mining with unstructured “trial-and-error” approaches. In response, we have followed design science to develop a comprehensive approach to diagnosing the problems with the existing data mining processes model for analytics-driven maintenance service.
Design/methodology/approach
This study conducted an in-depth case study with Siemens in the UK for data collection in order to apply a two-cycle build-and-evaluate design process. Based on the literature, the preliminary model is built. It is evaluated through the case company in the first cycle. In the second cycle, the model is refined based on the comments from the case company and then re-evaluated from both business management and information technology perspectives to ensure the applicability of the designed model in a real business environment.
Findings
Firstly, this study identifies three main shortcomings in the existing data mining process models for analytics-driven maintenance. Secondly, this study develops the “Gear-Wheel Model”, with a customer-oriented cycle, a project planning cycle and a machine comprehension cycle, to overcome all these shortcomings simultaneously and provide improvement solutions. Thirdly, this study highlighted that the data mining processes for analytics-driven maintenance service need interactions from different functional departments and supports of successive data collection.
Originality/value
The study expands data mining analysis beyond a single business function to include interactions with other internal functions and external customers. It contributes to existing knowledge by focusing on the managerial aspects of data mining and integrating maintenance service providers with their business customers.
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This qualitative study explores the career trajectories of women of color (WOC) leaders through the “From Assimilation to Alienation” framework, building upon Thomas et al.’s…
Abstract
Purpose
This qualitative study explores the career trajectories of women of color (WOC) leaders through the “From Assimilation to Alienation” framework, building upon Thomas et al.’s (2013) “Pet to Threat” work and incorporating intersectionality and the Stereotype Content Model. By examining 71 WOC leaders across various industries and racial groups (Black, East Asian, Latina and South Asian), this study aims to uncover the challenges, coping strategies they employ and the nuanced variations in their career experiences. The findings seek to inform organizational practices and DEI interventions in workplaces and leadership positions by considering the complex interplay of race, gender and stereotypes in corporate environments.
Design/methodology/approach
This qualitative study employs an interpretivist paradigm, featuring semi-structured interviews with 71 WOC leaders (18 Black, 18 East Asian, 17 Latina and 18 South Asian) from various industries, including Fortune 10 to 500 companies. Participants were purposively sampled based on leadership roles and racial backgrounds. Virtual interviews lasted approximately 60 min each. Data were analyzed using thematic analysis, focusing on predefined themes from the “From Assimilation to Alienation” framework while allowing for new themes to emerge. This approach enabled the exploration of participants' experiences, challenges and coping strategies within their corporate environments.
Findings
The findings reveal that WOC leaders experience a trajectory “From Assimilation to Alienation,” with nuanced variations across racial groups. Initially, they face tokenism, overlooked competencies and patronization. As they challenge the status quo, they encounter alienation, professional legitimacy challenges and insufficient recognition. Black women leaders face the most adverse experiences, followed by South Asian, Latina and East Asian leaders. Isolation emerges as a persistent theme across racial groups and career tenures. WOC leaders employ coping strategies such as demonstrating high work proficiency, relying on merit, utilizing diplomacy and setting boundaries when facing adversity.
Research limitations/implications
This study’s limitations include a focus on specific racial groups (Black, East Asian, Latina and South Asian), excluding white women and other diverse groups. It also does not explore intersectionalities such as religion, sexual orientation and age. However, the “From Assimilation to Alienation” framework developed in this study provides a foundation for future research to examine how multiple intersectionalities impact work and leadership experiences across a broader range of diverse groups.
Practical implications
The findings of this study provide valuable insights into the unique challenges faced by WOC leaders, highlighting the need for organizations to develop targeted interventions that address the “From Assimilation to Alienation” trajectory. Leadership training programs should incorporate modules that raise awareness about the experiences of WOC leaders and provide tools to assess and mitigate the adverse effects of tokenism, isolation and professional legitimacy challenges. These modules should also emphasize the importance of recognizing and rewarding the contributions of WOC leaders. By fostering a deeper understanding of the experiences of this group and providing practical strategies for support and inclusion, organizations can create a more equitable and inclusive leadership landscape that harnesses the full potential of diverse talent.
Originality/value
This study extends the Pet to Threat theory (Thomas et al., 2013) by exploring the “From Assimilation to Alienation” experiences of women of color leaders in corporate environments, integrating intersectionality and the Stereotype Content Model. Examining leaders from four racial groups (Black, East Asian, Latina and South Asian) provides a nuanced understanding of their challenges and coping strategies. The findings offer insights for organizations promoting DEI in leadership, highlighting the need for targeted interventions. This research contributes to the limited literature on career trajectories of this underrepresented group and lays the foundation for future studies on intersectionality of race, gender and leadership in the workplace.
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Harindranath R.M., George Alex Johan and Kavita Chavali
Our study aims to investigate how the fear of COVID-19 affects job satisfaction and mental well-being. Additionally, we will explore the moderating role of on-the-job embeddedness…
Abstract
Purpose
Our study aims to investigate how the fear of COVID-19 affects job satisfaction and mental well-being. Additionally, we will explore the moderating role of on-the-job embeddedness in these relationships.
Design/methodology/approach
The study surveyed 358 Indian-origin IT professionals through Amazon Mechanical Turk. It used confirmatory factor analysis to analyze the measurement model and hierarchical linear regression in SPSS 21 software to examine the structural relationships between variables. A robustness check was conducted using the MODLR macro in SPSS to identify any spurious moderation.
Findings
The results reveal a curvilinear (or U-shaped) relationship between COVID-19 fear, job satisfaction and mental well-being. Further, on-the-job embeddedness linearly moderates the relationship between COVID-19 fear and job satisfaction and COVID-19 fear and mental well-being.
Research limitations/implications
The research design is cross-sectional, so results reported about causal relationships are considered cautiously. The relationships involving the variables and their direction are because of the theory’s assumptions rather than the test of causal relationships between variables.
Originality/value
This is the first study to show that the relationship between COVID-19 fear and job satisfaction and COVID-19 fear and mental well-being is curvilinear (or U-shaped). Further, we are again the first to show that on-the-job embeddedness positively moderates the two relationships: COVID-19 fear – job satisfaction and COVID-19 fear – mental well-being. This is one of the few studies that employed MODLR macro to check for spurious moderation.