Iranian Journal of Management Studies
Volume:11 Issue: 4, Autumn 2018

In the present age of globalization, technology-revolution and sustainable development, the presence of seasonality in tourist arrivals is considered as a key policy issue that affects the global tourism industry by creating instability in the demand and revenues. The seasonal component in a time-series distorts the prediction attempts for policy-making. In this context, it is quintessential to suggest an accurate method of producing the reliable forecast of foreign tourist arrivals. This paper evaluated the performance of Holt-Winters’ and Seasonal ARIMA models for forecasting foreign tourist arrivals in India. The data on India’s inbound tourism from Jan-2001 to June-2018 were used for preparing the forecast for the period July-2018 to June-2020. On the basis of Mean Absolute Error, Mean Absolute Percentage Error and Mean Square Error, the findings infer the relative efficiency of Holt-Winters’ model over Seasonal ARIMA model in forecasting the foreign tourist arrivals in India. Thus, to reduce the perceived negative impacts of seasonality in Indian inbound tourism and to ensure foreign tourist visits round the year, niche products best suitable for Indian climatic and socio-cultural-institutional conditions need to be introduced and promoted in a large scale both at the national and global levels.

We live in a very complex world where we face complex phenomena such as social norms and new technologies. To deal with such phenomena, social scientists often use reductionism approach where they reduce them to some lower-lever variables and model the relationships among them through a scheme of equations. This approach that is called equation based modeling (EBM) has some basic weaknesses in modeling real complex systems so that assumptions such as unbounded rationality and perfect information are strongly emphasized while adaptability and evolutionary nature of all engaged agents along with network effects go unaddressed. In tackling deficiencies of reductionism, the complex adaptive system (CAS) framework has been proven very influential in the past two decades. In contrast to reductionism, under CAS framework, complex phenomena are studied in an organic manner where their agents are supposed to be both boundedly rational and adaptive. As the most powerful methodology for CAS modeling, agent-based modeling (ABM) has gained a growing popularity among academics and practitioners. ABMs show how agents’ simple behavioral rules and their local interactions at micro-scale can generate surprisingly complex patterns at macro-scale. Despite a growing number of ABM publications, those researchers unfamiliar with it have to study a number of works to understand (1) why and what of ABM, (2) its differences with EBM (3) its main functionalities in scientific studies and (4) some of its applications in management science. So, this paper’s major contribution is to help researchers particularly those unfamiliar with ABM to get insights regarding its philosophy and use and gain a big picture of it.

This paper has provided "out of sample" evidence of stock returns predictability in Tehran Stock Exchange. 68 qualified companies over the period from 2002 to 2015 were selected and for five different "forms of returns", five superior predictive models have been designed by applying "General to specific" approach of modeling technique. Then "out of sample" analysis, based on rolling regressions, has been used to test the validation of the designed models. The result showed that all designed models have sufficient "out of sample" validity and the aggregate returns have a higher predictability level.

Capitalizing upon Implicit Leadership Theories (ILTs) and Leader-member exchange (LMX), this research aims to explore the relationship between the congruence of employees’ ILTs and those traits of their managers and employees’ supportiveness, resistance and behavioral creativity for change. After distributing three questionnaires at three points among teachers, 296 participants completed all three questionnaires. Having conducted some confirmatory factor analyses (CFAs), we utilized Latent Congruence Modeling (LCM) to analyze the final proposed models. The findings showed that the congruence has no direct effects on behavioral change responses. However, LMX can function as a full mediator. LMX has significant relationships with employees’ support and resistance for change. Nevertheless, the path coefficient was non-significant for behavioral creativity to change. This study, therefore, extends prevailing follower-centric perspective on leadership and strengthens its essence in organizational change with fundamental socio-cognitive research.

Customer relationship management (CRM) is one of the fastest growing management approaches which can lead to stronger competitive position, resulting in larger market share and profitability. In this study, CRM efficiency among the customers of the Iranian banks is analyzed using a network data envelopment analysis (NDEA) approach. To implement CRM in the NDEA model, input, intermediate and output variables are service quality, customer satisfaction and customer loyalty, respectively. This research is a descriptive survey in which the total customers of different Iranian banks in Isfahan comprise the statistical population. The sample included 420 people that were selected by cluster sampling. After distributing questionnaires, only 245 questionnaires were completed. The model is tested via PLS path modeling and confirmed. To rank banks performance, NDEA model is used. Results show the power of NDEA model in the differentiation of the banks since there are no two banks with the same rank. The efficiency of sub-process is also presented to extract the reason of inefficiency in the total process. Because of the adopted research approach, the research results may lack generalization. Therefore, researchers are encouraged to test the proposed propositions further. The paper includes a model for assessing CRM with NDEA model and helps managers rank their companies in the customers' point of view.

Investors and other contributors to stock exchange need a variety of tools, measures, and information in order to make decisions. One of the most common tools and criteria of decision makers is price-to earnings per share ratio. As a result, investors are in pursuit of ways to have a better assessment and forecast of price and dividends and get the highest returns on their investment. Previous research shows that neural networks have better predictability than statistical models. Thus, Harmony Search algorithm and neural network have been used in this work, since achieving the best forecast is more likely. For this purpose, a sample consisting of 87 companies has been selected from those listed at the Tehran Stock Exchange over a 10-year period (2006-2015). The results show the high accuracy of the designed model that predicts the price-to-earnings ratio at the stock exchange by hybridizing the balanced search algorithm with neural network.

Is the impact of sales promotion on consumer perception mediated by its hedonic and utilitarian benefits in the context of Indian consumers? Is gender having a moderating impact on the relationship between the benefits of sales promotion and consumer perception? Authors examined both questions using a partial least square structural equation modeling (PLS-SEM). Findings revealed that hedonic and utilitarian benefits mediate the relationship between sales promotion and consumer perception about the product in the context of the Indian consumer. Moderating impact of gender is also found. Female consumers give more preference to the hedonic benefits of sales promotion while male consumers are more attracted to the utilitarian benefits.

Hazardous waste management incorporates collection, separation, treatment, recycling and disposal of hazardous wastes. In this paper, a new multi-objective mixed integer model is presented for hazardous waste collection problem. The model aims to minimize transportation and construction costs, and environmental and population risks in hazardous waste management systems. This model is applied in a case study of Iran in order to help decision makers to decide on the location of separation, treatment, recycle, disposal centers, and established technology in treatment center. Moreover, this paper specifies routes between different facilities in collection network. For addressing population and environmental impacts and economical costs, three objective functions including total costs, total population exposure risk, and environmental risks are considered. An augmented ε-constraint method is used to generate Pareto optimal solution for these conflicting objectives. Finally, proposed model is utilized in our case study and numerical results and some managerial insights are provided