arXiv subjects

Saeed Nosratabadi

Publications and source records attributed to Saeed Nosratabadi.

At least 19 recordsLinked to original sources

arXiv2026

An Integrative Multidimensional Conceptualization of Telework Behavior: A Systematic Review and Grounded Theory Approach

Telework has expanded rapidly, and understanding the behaviors employees enact under it has become correspondingly important. This study develops an integrative conceptual framework specifying the multidimensional nature of telework behavior. A systematic literature review following PRISMA identified 114 review articles, which were analyzed using constructivist grounded theory. Six behavioral dimensions were identified, covering performance, communication, environmental, task, policy, and well-being conduct. Antecedents were grouped into individual factors, job characteristics, organizational norms, technological factors, and work environment factors. Outcomes were grouped into job satisfaction, productivity, turnover, health and well-being, work-life balance, and social isolation. Three contextual moderators were identified, namely telework modality, telework preference, and cultural and national context. Fourteen propositions link these categories and are advanced as testable claims rather than established findings, since they are derived from published review evidence and have not been empirically tested. The framework proposes telework behavior as the construct through which the conditions of remote work are translated into employee and organizational outcomes, and it specifies a content domain from which measures of the construct can be developed. It also indicates where organizations can direct policy, training, and intervention.

econ.GN↗

arXiv2026

Dynamic Capabilities for AI-Enabled Exploration: Antecedents, Mechanisms, and Innovation Outcomes

While the operational benefits of Artificial Intelligence (AI) are well-documented, the mechanisms through which firms leverage AI for strategic exploration and radical innovation remain under-theorized. This study addresses the black box of AI value creation by integrating the Technology-Organization-Environment (TOE) framework with the Dynamic Capabilities View (DCV). We propose that AI adoption is not a direct antecedent to performance but a multi-stage process wherein technological, organizational, and environmental factors enable the development of sensing capability, which in turn fosters a novel capability we term AI-Enabled Exploration. Analyzing survey data from 245 senior executives in Saudi Arabia, a high-growth economy undergoing state-led digital transformation, we employed Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the model. The results confirm a serial mediation chain: organizational readiness and technology compatibility drive sensing capability, which subsequently powers AI-enabled exploration to enhance innovation performance. Contrary to expectations, government support was not a significant predictor of sensing capability, suggesting that in resource-rich environments, external incentives are necessary but insufficient for capability building. Furthermore, competitive pressure was found to positively moderate the relationship between organizational readiness and exploration, acting as a critical catalyst that converts latent resources into active experimentation. These findings offer a theoretical roadmap for firms attempting to transition from AI-driven efficiency to AI-driven ambidexterity.

econ.GN↗

arXiv2026

Green Transformational Leadership and Sustainable Nursing Practices: Evidence from the Healthcare Sector

The healthcare sector contributes approximately 4.4% of global greenhouse gas emissions, yet research on the organizational determinants of sustainable behaviors among healthcare workers remains limited. This study examines how green transformational leadership and ethical climate influence sustainable clinical behaviors among registered nurses, with green psychological climate as a mediator and perceived organizational hypocrisy as a moderator. Data were collected from 760 nurses across 11 public and private hospitals in Jordan using a cross-sectional survey design. Structural equation modeling with bootstrapping was employed to test the hypothesized relationships. The results revealed that both green transformational leadership and ethical climate positively predicted sustainable clinical behaviors. Green psychological climate partially mediated both relationships. Perceived organizational hypocrisy significantly weakened the positive effects of green transformational leadership and ethical climate on sustainable behaviors. The model explained 35.7% of the variance in sustainable clinical behaviors. These findings highlight that fostering sustainability in healthcare requires not only supportive leadership and ethical organizational environments but also authenticity and consistency between stated values and actual practices. The study extends green transformational leadership theory to healthcare settings, integrates ethical climate research with environmental sustainability, and introduces perceived organizational hypocrisy as a critical boundary condition. Practical implications for healthcare administrators seeking to reduce their environmental footprint are discussed.

econ.GN↗

arXiv2023

Modeling the Impact of Mentoring on Women's Work-LifeBalance: A Grounded Theory Approach

The purpose of this study was to model the impact of mentoring on women's work-life balance. Indeed, this study considered mentoring as a solution to create a work-life balance of women. For this purpose, semi-structured interviews with both mentors and mentees of Tehran Municipality were conducted and the collected data were analyzed using constructivist grounded theory. Findings provided a model of how mentoring affects women's work-life balance. According to this model, role management is the key criterion for work-life balancing among women. In this model, antecedents of role management and the contextual factors affecting role management, the constraints of mentoring in the organization, as well as the consequences of effective mentoring in the organization are described. The findings of this research contribute to the mentoring literature as well as to the role management literature and provide recommendations for organizations and for future research.

econ.GN↗

arXiv2023

Social Sustainability of Digital Transformation: Empirical Evidence from EU-27 Countries

In the EU-27 countries, the importance of social sustainability of digital transformation (SOSDIT) is heightened by the need to balance economic growth with social cohesion. By prioritizing SOSDIT, the EU can ensure that its citizens are not left behind in the digital transformation process and that technology serves the needs of all Europeans. Therefore, the current study aimed firstly to evaluate the SOSDIT of EU-27 countries and then to model its importance in reaching sustainable development goals (SDGs). The current study, using structural equation modeling, provided quantitative empirical evidence that digital transformation in Finland, the Netherlands, and Denmark are respectively most socially sustainable. It is also found that SOSDIT leads the countries to have a higher performance in reaching SDGs. Finally, the study provided evidence implying the inverse relationship between the Gini coefficient and reaching SDGs. In other words, the higher the Gini coefficient of a country, the lower its performance in reaching SDGs. The findings of this study contribute to the literature of sustainability and digitalization. It also provides empirical evidence regarding the SOSDIT level of EU-27 countries that can be a foundation for the development of policies to improve the sustainability of digital transformation. According to the findings, this study provides practical recommendations for countries to ensure that their digital transformation is sustainable and has a positive impact on society.

econ.GN↗

arXiv2022

Artificial Intelligence Models and Employee Lifecycle Management: A Systematic Literature Review

Background/Purpose: The use of artificial intelligence (AI) models for data-driven decision-making in different stages of employee lifecycle (EL) management is increasing. However, there is no comprehensive study that addresses contributions of AI in EL management. Therefore, the main goal of this study was to address this theoretical gap and determine the contribution of AI models to EL. Methods: This study applied the PRISMA method, a systematic literature review model, to ensure that the maximum number of publications related to the subject can be accessed. The output of the PRISMA model led to the identification of 23 related articles, and the findings of this study were presented based on the analysis of these articles. Results: The findings revealed that AL algorithms were used in all stages of EL management (i.e., recruitment, on-boarding, employability and benefits, retention, and off-boarding). It was also disclosed that Random Forest, Support Vector Machines, Adaptive Boosting, Decision Tree, and Artificial Neural Network algorithms outperform other algorithms and were the most used in the literature. Conclusion: Although the use of AI models in solving EL problems is increasing, research on this topic is still in its infancy stage, and more research on this topic is necessary.

econ.GN↗

arXiv2021

Emerging Platform Work in the Context of the Regulatory Loophole (The Uber Fiasco in Hungary)

The study examines the essential features of the so-called platform-based work, which is rapidly evolving into a major, potentially game-changing force in the labor market. From low-skilled, low-paid services (such as passenger transport) to highly skilled and high-paying project-based work (such as the development of artificial intelligence algorithms), a broad range of tasks can be carried out through a variety of digital platforms. Our paper discusses the platform-based content, working conditions, employment status, and advocacy problems. Terminological and methodological problems are dealt with in-depth in the course of the literature review, together with the 'gray areas' of work and employment regulation. To examine some of the complex dynamics of this fast-evolving arena, we focus on the unsuccessful market entry of the digital platform company Uber in Hungary 2016 and the relationship to institutional-regulatory platform-based work standards. Dilemmas relevant to the enforcement of labor law regarding platform-based work are also paid special attention to the study. Employing a digital workforce is a significant challenge not only for labor law regulation but also for stakeholder advocacy.

econ.GN↗

arXiv2021

Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS

Advancing models for accurate estimation of food production is essential for policymaking and managing national plans of action for food security. This research proposes two machine learning models for the prediction of food production. The adaptive network-based fuzzy inference system (ANFIS) and multilayer perceptron (MLP) methods are used to advance the prediction models. In the present study, two variables of livestock production and agricultural production were considered as the source of food production. Three variables were used to evaluate livestock production, namely livestock yield, live animals, and animal slaughtered, and two variables were used to assess agricultural production, namely agricultural production yields and losses. Iran was selected as the case study of the current study. Therefore, time-series data related to livestock and agricultural productions in Iran from 1961 to 2017 have been collected from the FAOSTAT database. First, 70% of this data was used to train ANFIS and MLP, and the remaining 30% of the data was used to test the models. The results disclosed that the ANFIS model with Generalized bell-shaped (Gbell) built-in membership functions has the lowest error level in predicting food production. The findings of this study provide a suitable tool for policymakers who can use this model and predict the future of food production to provide a proper plan for the future of food security and food supply for the next generations.

econ.GN↗

arXiv2021

The Effect of Marketing Investment on Firm Value and Systematic Risk

Analyzing the financial benefit of marketing is still a critical topic for both practitioners and researchers. Companies consider marketing costs as a type of investment and expect this investment to be returned to the company in the form of profit. On the other hand, companies adopt different innovative strategies to increase their value. Therefore, this study aims to test the impact of marketing investment on firm value and systematic risk. To do so, data related to four Arabic emerging markets during the period 2010-2019 are considered, and firm share price and beta share are considered to measure firm value and systematic risk, respectively. Since a firm's ownership concentration is a determinant factor in firm value and systematic risk, this variable is considered a moderated variable in the relationship between marketing investment and firm value and systematic risk. The findings of the study, using panel data regression, indicate that increasing investment in marketing has a positive effect on the firm value valuation model. It is also found that the ownership concentration variable has a reinforcing role in the relationship between marketing investment and firm value. It is also disclosed that it moderates the systematic risk aligned with the monitoring impact of controlling shareholders. This study provides a logical combination of governance-marketing dimensions to interpret performance indicators in the capital market.

econ.GN↗

arXiv2021

Driving Factors Behind the Social Role of Retail Centers on Recreational Activities

Retail centers can be considered as places for interactional and recreational activities and such social roles of retail centers contribute to the popularity of the retail centers. Therefore, the main objective of this study was to identify effective factors encouraging customers to engage with interactional activities and measure how these factors affect customer behavior. Accordingly, two hypotheses were raised illustrating that the travel time (i.e., the time it takes for a customer to reach the retail center) and the variety of shops (in a retail center) increase the percentage of people who spend their leisure time and recreational activities retail centers. Two case studies were conducted in two analogous retail centers, one in Tehran, Iran, and the other in Madrid, Spain. According to the results, there is an interaction between the travel time and the motivation for the presence of people in the retail center. Furthermore, the results revealed that half of both retail center goers who spend more than 10 minutes to reach the retail centers prefer to do leisure activities and browsing than shopping. In other words, the longer it takes a person to get to the center, the more likely he/she is to spend more time in the mall and do more leisure activities. It is also found that there is a significant relationship between the variety of shops in a retail center and the motivation of customers attending a retail center that encourages people to spend their leisure time in retail centers.

econ.GN↗

arXiv2020

Social Capital Contributions to Food Security: A Comprehensive Literature Review

Social capital creates a synergy that benefits all members of a community. This review examines how social capital contributes to the food security of communities. A systematic literature review, based on Prisma, is designed to provide a state-of-the-art review on capacity social capital in this realm. The output of this method led to finding 39 related articles. Studying these articles illustrates that social capital improves food security through two mechanisms of knowledge sharing and product sharing (i.e., sharing food products). It reveals that social capital through improving the food security pillars (i.e., food availability, food accessibility, food utilization, and food system stability) affects food security. In other words, the interaction among the community members results in sharing food products and information among community members, which facilitates food availability and access to food. There are many shreds of evidence in the literature that sharing food and food products among the community member decreases household food security and provides healthy nutrition to vulnerable families and improves the food utilization pillar of food security. It is also disclosed that belonging to the social networks increases the community members' resilience and decreases the community's vulnerability that subsequently strengthens the stability of a food system. This study contributes to the common literature on food security and social capital by providing a conceptual model based on the literature. In addition to researchers, policymakers can use this study's findings to provide solutions to address food insecurity problems.

econ.GN↗

arXiv2020

State of the Art Survey of Deep Learning and Machine Learning Models for Smart Cities and Urban Sustainability

Deep learning (DL) and machine learning (ML) methods have recently contributed to the advancement of models in the various aspects of prediction, planning, and uncertainty analysis of smart cities and urban development. This paper presents the state of the art of DL and ML methods used in this realm. Through a novel taxonomy, the advances in model development and new application domains in urban sustainability and smart cities are presented. Findings reveal that five DL and ML methods have been most applied to address the different aspects of smart cities. These are artificial neural networks; support vector machines; decision trees; ensembles, Bayesians, hybrids, and neuro-fuzzy; and deep learning. It is also disclosed that energy, health, and urban transport are the main domains of smart cities that DL and ML methods contributed in to address their problems.

econ.GN↗

arXiv2020

Modelling Temperature Variation of Mushroom Growing Hall Using Artificial Neural Networks

The recent developments of computer and electronic systems have made the use of intelligent systems for the automation of agricultural industries. In this study, the temperature variation of the mushroom growing room was modeled by multi-layered perceptron and radial basis function networks based on independent parameters including ambient temperature, water temperature, fresh air and circulation air dampers, and water tap. According to the obtained results from the networks, the best network for MLP was in the second repetition with 12 neurons in the hidden layer and in 20 neurons in the hidden layer for radial basis function network. The obtained results from comparative parameters for two networks showed the highest correlation coefficient (0.966), the lowest root mean square error (RMSE) (0.787) and the lowest mean absolute error (MAE) (0.02746) for radial basis function. Therefore, the neural network with radial basis function was selected as a predictor of the behavior of the system for the temperature of mushroom growing halls controlling system.

econ.GN↗

arXiv2020

Leader Cultural Intelligence and Organizational Performance

One of the challenges for international companies is to manage multicultural environments effectively. Cultural intelligence (CQ) is a soft skill required of the leaders of organizations working in cross-cultural contexts to be able to communicate effectively in such environments. On the other hand, organizational structure plays an active role in developing and promoting such skills in an organization. Therefore, this study aimed to investigate the effect of leader CQ on organizational performance mediated by organizational structure. To achieve the objective of this research, first, conceptual models and hypotheses of this research were formed based on the literature. Then, a quantitative empirical research design using a questionnaire, as a tool for data collection, and structural equation modeling, as a tool for data analysis, was employed among executives of knowledge-based companies in the Science and Technology Park, Bushehr, Iran. The results disclosed that leader CQ directly and indirectly (i.e., through the organizational structure) has a positive and significant effect on organizational performance. In other words, in organizations that operate in a multicultural environment, the higher the level of leader CQ, the higher the performance of that organization. Accordingly, such companies are encouraged to invest in improving the cultural intelligence of their leaders to improve their performance in cross-cultural environments, and to design appropriate organizational structures for the development of their intellectual capital.

econ.GN↗

arXiv2020

Data Science in Economics

This paper provides the state of the art of data science in economics. Through a novel taxonomy of applications and methods advances in data science are investigated. The data science advances are investigated in three individual classes of deep learning models, ensemble models, and hybrid models. Application domains include stock market, marketing, E-commerce, corporate banking, and cryptocurrency. Prisma method, a systematic literature review methodology is used to ensure the quality of the survey. The findings revealed that the trends are on advancement of hybrid models as more than 51% of the reviewed articles applied hybrid model. On the other hand, it is found that based on the RMSE accuracy metric, hybrid models had higher prediction accuracy than other algorithms. While it is expected the trends go toward the advancements of deep learning models.

q-fin.GN↗

arXiv2020

Sustainable Banking; Evaluation of the European Business Models

Sustainable business models also offer banks competitive advantages such as increasing brand reputation and cost reduction. However, no framework is presented to evaluate the sustainability of banking business models. To bridge this theoretical gap, the current study using A Delphi-Analytic Hierarchy Process method, firstly, developed a sustainable business model to evaluate the sustainability of the business model of banks. In the second step, the sustainability performance of sixteen banks from eight European countries including Norway, the UK, Poland, Hungary, Germany, France, Spain, and Italy, assessed. The proposed business model components of this study were ranked in terms of their impact on achieving sustainability goals. Consequently, the proposed model components of this study, based on their impact on sustainability, are respectively value proposition, core competencies, financial aspects, business processes, target customers, resources, technology, customer interface, and partner network. The results of the comparison of the banks studied by each country disclosed that the sustainability of the Norwegian and German banks business models is higher than in other counties. The studied banks of Hungary and Spain came in second, the banks of the UK, Poland, and France ranked third, and finally, the Italian banks ranked fourth in the sustainability of their business models.

q-fin.GN↗

arXiv2020

Hybrid Machine Learning Models for Crop Yield Prediction

Prediction of crop yield is essential for food security policymaking, planning, and trade. The objective of the current study is to propose novel crop yield prediction models based on hybrid machine learning methods. In this study, the performance of the artificial neural networks-imperialist competitive algorithm (ANN-ICA) and artificial neural networks-gray wolf optimizer (ANN-GWO) models for the crop yield prediction are evaluated. According to the results, ANN-GWO, with R of 0.48, RMSE of 3.19, and MEA of 26.65, proved a better performance in the crop yield prediction compared to the ANN-ICA model. The results can be used by either practitioners, researchers or policymakers for food security.

cs.NE↗

arXiv2020

Food Supply Chain and Business Model Innovation

This paper investigates the contribution of business model innovations in improvement of food supply chains. Through a systematic literature review, the notable business model innovations in the food industry are identified, surveyed, and evaluated. Findings reveal that the innovations in value proposition, value creation processes, and value delivery processes of business models are the successful strategies proposed in food industry. It is further disclosed that rural female entrepreneurs, social movements, and also urban conditions are the most important driving forces inducing the farmers to reconsider their business models. In addition, the new technologies and environmental factors are the secondary contributors in business model innovation for the food processors. It is concluded that digitalization has disruptively changed the food distributors models. E-commerce models and internet of things are reported as the essential factors imposing the retailers to innovate their business models. Furthermore, the consumption demand and the product quality are two main factors affecting the business models of all the firms operating in the food supply chain regardless of their positions in the chain. The findings of the current study provide an insight into the food industry to design a sustainable business model to bridge the gap between food supply and food demand.

econ.GN↗