The Impact of Firm's Social Media Applications on Green Supply Chain Management

Social Media Applications (SMA) has become a common tool for networking and communication and also content sharing. As a result, firms utilize SMA for organizational purposes. Then again, Green Supply Chain Management (GSCM) is a popular subject in the area of operations management for both researchers as well as practitioners. However, the influences of SMA on GSCM practices and finally on organizational performance are not well understood. SMA can expedite information flow and knowledge sharing amidst supply chains and also may assist organizations for greening their SCM practices. In this research, we empirically investigate the impact of SMA capabilities on GSCM practices. Data collected from 206 manufacturing managers were analyzed applying a structural equation modeling methodology. Results confirm that the adoption of SMA in supply chains by manufacturing firms positively affect the GSCM practices among the chain

A Model to Evaluate the Organizational Readiness for Big Data Adoption

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption

A FBWM-PROMETHEE approach for industrial robot selection

Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.publishersversionpublishe

The Impact of Big Data Adoption on SMEs’ Performance

The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions

Social undermining: A model expansion of the employees’ social undermining and determining its relationship with organizational agility

Social undermining is a negative achievement of social life that imposes huge costs on organizations and societies. Undermining behaviors leave negative effects and consequences on organizations and people. In this study, the causal, background, and intervening causes along with the consequences of social undermining are discussed by building on grounded theory. This study is conducted to attenuate the negative effects of social undermining on staffs so as to contribute the managers and policy makers of the State Welfare Organization of Iran. In fact, the contribution of this research is to compute the elements of social undermining model in organizations.The results of the study reveal that there is a positive relationship between the direct undermining, Physical Undermining, Verbal Undermining, Nonverbal undermining and social undermining. Further, a positive and direct relationship exists between the aspects of social undermining and organizational agility

Pro-Rata Warranty pricing model with risk-averse manufacturers Mahdi Nasrollahi

Due to fierce competition and customer demand, manufacturers have started selling products with different warranty policies and pro-rata warranty is one of the most widely used warranty policy. In this paper we propose a warranty model for pro-rata, fixed period warranty policy with risk averse manufacturer that determines optimal warranty price under inflationary condition. This model has been proposed for products with time dependent failure intensity with Non homogeneous Poisson’s process for failure intensity function, and concave utility function. Using the exponential utility function, the decision model is developed to maximize the manufacturer’s certainty profit equivalent. Risk preference model is developed to find the optimal warranty price through the use of the manufacturer’s utility function for profit. Finally, the sensitivity of the warranty price models is analyzed using numerical examples with respect to such factors as (1) the manufacturer's risk preferences, (2) product failure rate parameters, (3) warranty period length, and (4) inflation and discount rat

Prioritizing the factors affecting the productivity of human resources using multi-criteria decision-making techniques in water and wastewater company of qazvin

La metodología de esta investigación es descriptiva según la recopilación de datos y es práctica y analítica. El estudio se administra a través de la realización de cuestionarios y se utiliza el método AHP para priorizar los factores. El objetivo principal de este estudio es priorizar los factores que afectan la productividad de los recursos humanos en la empresa de agua y aguas residuales en Qazvin utilizando técnicas de toma de decisiones multicriterio. Se han utilizado métodos T-test y M.A.D.M para analizar los datos. La validez de contenido se utiliza para probar la validez del cuestionario y Cronbach alfa (0,96) se utiliza para probar la fiabilidad

Designing Airline Hub-and-Spoke Network and Fleet Size by a Biobjective Model Based on Passenger Preferences and Value of Time

This study presents a biobjective hub-and-spoke (HS) network design model for the global air passenger networks. The model explores the tradeoff between the total airline cost (airline preference) and lost time cost for passengers (user preference) as the model’s objectives. Most previous studies have focused on airline objectives and established HS networks based on the viewpoint of airlines, despite the importance of passenger objectives. Poor passenger service and inconvenience and dissatisfaction may lead to network breakdown. The major criteria for passenger dissatisfaction in HS networks are schedule and trip delays caused by nondirect flights. These delays (the lost time cost for passengers) are multiplied by the passengers’ value of time (VOT) and minimized as one of the model’s objectives. Another objective that is minimized is the transportation costs of the airline depending on the services provided (short-, medium-, and large-haul flights). The model is solved in a case study (Iranian Aeronautics Network) that is applied to the well-known yearbook of tourism statistics data. Pareto frontier was found for all candidate airports. Also, the number of aircraft required (short-, medium-, and large-haul), as well as the average load factor for different types of aircraft in various weights of the first objective (airline costs), was presented. The results of Pareto frontier indicated that Imam Khomeini International Airport should be selected as the global hub airport for Iran international flight network. Otherwise, Shiraz International Airport and Tabriz International Airport (as the first alternative), as well as Isfahan International Airport and Mashhad International Airport (as the second alternative), would be the best choices. The weight of the first objective (airline costs) seems to be between 0.7 to 1, a practical and logical weight that can reduce passenger costs (as the second objective) by 20% on average by adding only 15 long-haul, 40 medium-haul, and 37 short-haul aircraft to the airline’s fleet. Also, in this range, the average load factor for medium- and long-haul aircrafts is greater than 0.9, which seems to be ideal

The impact of big data adoption on smes’ performance

Funding Information: Acknowledgments: This work was supported by the Portuguese Foundation for Science and Technology (FCT) and the Center of Technology and Systems (CTS). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions.publishersversionpublishe

Simultaneous interpretive structural modelling and weighting (SISMW)

Multi-criteria decision-making (MCDM) methods have been implemented in many fields. In the meantime, several methods have been proposed to obtain the weight of the criteria determined by various methods in different ways. In this paper, a new approach, called simultaneous interpretive structural modelling and weighting (SISMW), is proposed to solve a multi-criterion decision-making (MCDM) problem. Using SISMW, the weight of the criteria and the relationship between them could be determined simultaneously. In this approach, like the ISM method, pair comparison between criteria was made by the decision-maker to determine the relationships among the different criteria. With the help of this data, the weight of the criteria, as well as the causal (cause and effect) relationships between them, were determined in 12 steps. The main advantage of this method is that only one stage of data collection is required for obtaining weights and modelling, and so the research process may be faster. This may increase the reliability of the collected data because, in a one-step survey, the impact of time is minimized. This process can be useful for conceptualizing and developing theories to help decisionmakers understand the problem better