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10. Drivers, Uncertainties, and Scenarios of the Iranian Economic System.

Author

Shahri, Mohammad Esmaeilzadeh, Azari, Mostafa, and Ramezani, Yousef

Subjects
ECONOMIC systems, CAPITAL market, ECONOMIC impact, ECONOMIC sanctions, DELPHI method, UNEMPLOYMENT, RENT seeking
Abstract

Iran's economy is founded on three main sectors: cooperative, private, and governmental. Various key documents outline objectives for each sector, highlighting certain industries with unique advantages and capacities. These industries not only have strong interconnections with other sectors but also drive demand and contribute to economic growth. This study aims to analyze and present various potential futures for Iran's economy, adopting a holistic and comprehensive perspective. It explores the key factors shaping the economy, including constructive economic drivers, significant uncertainties, and potential future scenarios, considering various influential dimensions. The research employs a qualitative scenario-based approach, structured in eight stages. Data is gathered through interviews, expert panels, and the Delphi survey method, with analysis supported by Mac and Scenario software and a wizard tool. The study identifies primary economic drivers for Iran, including economic growth, oil dependency, the impact of economic sanctions, fundamental challenges, and the level of economic openness. While other factors like high unemployment, a small capital market, rent-seeking, weak infrastructure, and corruption significantly influence the economic landscape, they are not classified as primary drivers in this study. However, these elements will be considered in developing future economic scenarios. Also, in order to prioritize solutions to deal with economic threats, it should be said that using short-term methods in the programs and executive plans of the country's institutions will be the first step to deal with threats. At the same time, planning and using solutions of higher levels, respectively, can move the country step by step in terms of structure and institutions in the direction of resilience, invulnerability and regeneration. [ABSTRACT FROM AUTHOR]

Published
2024
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16. A Causal Model of the Relationship Between Customer Contact Points and Word of Mouth Through Customer Experience in the Insurance Industry

Author

Ramezani, Yousef, Okhravi, Amir Hosein, Heydarnejad, Tahereh, and Salarpanah, Sepideh

Subjects
Word of mouth, Customer experience, Insurance industry, Customer contact points, Structural equation modeling
Abstract

Customer contact points are known as the building blocks of the customer experience and for designing and enhancing the customer experience. The purpose of this study is to investigate the causal relationships between customer contact points and word of mouth through customer experience in the insurance industry in 2021. The present study is applied in terms of purpose and descriptive-correlational in terms of implementation method. This survey included Mashhad insurance clients. As the number of customers is unlimited, the sample size 384 was calculated using Morgan table. Samples were selected using the non-probability Convenience sampling method. Confirmatory factor analysis was used to determine the validity of the questionnaires. The reliability of the questionnaires was obtained using Cronbach's alpha coefficient for all research variables above 0.7. Structural equation method was employed using PLS software. The results showed that customer contact points and their dimensions affect customer experience. Customer experience also had a significant effect on word of mouth. Ultimately, the customer experience mediated the impact of customer contact points and their dimensions on word of mouth. Finally, suggestions were presented based on the research findings., {"references":["Almani, A., Shirvani, H. A., & Gilaninia, S. (2012). A study of factors influencing positive word of mouth in the banking industry. Middle –East Journal of Scientific Research, 11(4), 454–463. https://doi.org/10.20397/2177-6652/2020.v20i1.1712","Barari, M., Ross. M., & Surachartkumtonkun, J. (2020). Negative and positive customer shopping experience in an online context. Journal of Retailing and Consumer Services, 53(1), 101985. https://doi.org/10.1016/j.jretconser.2019.101985","Baxendale, S., Macdonald, E. K. & Wilson, H. N. (2015). The impact of different touchpoints on brand consideration. Journal of Retailing, 91(2), 235–253.https://doi.org/10.1016/j.jretai.2014.12.008","Campbell, A. J. (2013). Creating customer knowledge competence: managing customer relationship management programs strategically. Industrial Marketing Management, 5(32), 375–383. https://doi.org/10.1016/S0019–8501%2803%2900011–7","Berry, L. L., Carbone, L. P., & Haeckel, S. H. (2002). Managing the total customer experience. Sloan Management Review, 43(3), 85–89. https://doi.org/10.4236/ajps.2015.614244","Chan, Y. Y. Y. & Ngai E. W. T. (2011). Conceptualizing electronic word of mouth activity: an input process– output perspective. Marketing Intelligence and Planning, 29 (5), 488–516. https://doi.org/10.1108/02634501111153692","Dehdashti–Shahrokh, Z. (2014). The effect of ease of use on word–of–mouth advertising and repurchase intent. Journal of New Marketing Research, 5(2), 15–36.","Dehghan, N., Farsi–Rad, A., & Gholami, M. (2016). Explain the relationship between product features, customer experience and brand equity with the purchase decision and loyalty to the mobile phone brand in Ahvaz. Journal of New Marketing Research, 5(3), 120–130.","Dye, R. (2000). The Buzz on Buzz, Harvard Business Review, Now Dec, 139–145.","Farrokhi, S., Yavari, A., Beykzad, J. (2017). The effect of word of mouth on customer preferences. Two Quarterly Journal of Value Chain Management, 5(2), 68–77.","Fereyduni, M., & Kalateh–Seifri, M. (2018). Provide a model of the effect of customers' emotional dependence on emotional loyalty and word of mouth. Sports Management (Movement), 3(10), 535–548.","Ferguson, R. J., Paulin, M. & Bergeron, J. (2010). Customer sociability and the total service experience: antecedents of positive word–of–mouth intentions. Journal of Service Management, 21(1), 25–44. https://doi.org/10.1108/09564231011025100","Folstad, A., & Kvale, K. (2018). Customer journeys: A Systematic literature review. Journal of Service Theory and Practice, 28(2), 196 –227. https://doi.org/10.1108/JSTP–11–2014–0261","Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018). Investigating the effects of smart technology on customer dynamics and customer experience. Computers in Human Behavior, 80, 271–282.https://doi.org/10.1016/j.chb.2017.11.014","Funk, D. (2017). Introducing a sport experience design (SX) framework for sport consumer behavior research. Sport Management Review, 20(2), 145–158. https://doi.org/10.1016/j.smr.2016.11.006","Garg, R., Rahman, Z., & Qureshi, M. N. (2014). Measuring customer experience in banks: scale development and validation. Journal of Modelling in Management, 9(1), 87–117. https://doi.org/ 10.1108/JM2–07–2012–0023","Gentile, C., Spiller, N. & Noci, G. (2017). How to sustain the customer experience: an overview of experience components that co–create value with the customer. European Management Journal, 66(2), 395–410. https://doi:10.1016/j.emj.2007.08.005","Hasangholipour, T., Seyed – Javadin, S. R., Roosta, A., & khanlari, A. (2012). An assessment model for customer relationship management process in iranian private–commercial banks. Journal of Information Technology Management. 4(10). 41–62.","Hasangholipour, T. (2015). Theoretical and experimental study of the factors predicting the verbal advertisements of customers about airlines (Case study of Iran Air). Business Management, 5(1), 41–60.","Heidari, S. (2011). Investigating the relationship between customer experience and customer loyalty and word of mouth by customers of Samsung products. Master Thesis, Shahroud University of Technology.","Hedayat–Nazari, F., & Dehdashti–Shahrokh, Z. (2018). Antecedents and outcomes of customer experience in iran online retailing. Public Management Researches, 11(41), 313–336.","Heshmati, I., Saeidnia, H., & Badizadeh, A. (2020). Designing a customer service management model for banking services. Business Management Explorations, 11(21), 247–268.","Jason, Q. Zh., Georgiana, C., & Dongwoo, Sh. (2010). When does electronic word–of–mouth matter? a study of consumer product reviews. Journal of Business Research, 63(12). 1336–1341. https://doi.org/ 10.1016/J.JBUSRES.2009.12.011","Jain, R., Jayesh, A., & Bagdare Sh., (2017). Customer experience – a review and research agenda. Journal of Service Theory and Practice, 27(3), 642– 662.https:// doi.org/10.1108/JSTP–03–2015–0064","Jüttner, U., Schaffner, D., Windler, K. & Maklan, S. (2013). Customer service experiences: Developing and applying a sequential incident laddering technique. European Journal of Marketing, 47(5/6), 738–769. https:// doi.org/10.1108/03090561311306769","Karimi, E. (2019). Design a framework to enhance the customer digital experience in internet banking. Business Management Perspective, 18(40), 56–36. https:// doi.org/10.29252/jbmp.18.40.36","Kenttamaa. (2014). The role of emotion in experimental decision. International Journal of Research in Marketing, 24.","Khalouzadeh–Mobarakeh, S. (2020). Designing a model for improving customer experience and response using social media marketing. Consumer Studies Quarterly, 6(1), 287–309.","Khanna, M., Jacob, I. & Yadav, N. (2014). Identifying and analyzing touchpoints for building a higher education brand. Journal of Marketing for Higher Education, 24(1), 122–143.https:// doi.org/10.1080/08841241.2014.920460","Khodaei–Gargari, M. T., & Abbasi–Esfanjani, H. (2019). Design a roadmap for improving the customer experience in service organizations based on data processing theory. Organizational Resource Management Research, 9(2), 19–39.","Kitapci, O. Akdogan, C. & Dortyol, I. T. (2014). The impact of service quality dimensions on patient satisfaction, repurchase Intentions and word–of–mouth communication in the public healthcare industry. Procedia–Social and Behavioral Sciences, 148, 161–169.https:// doi.org/10.4172/2471–9781.100044","Klaus, P., & Maklan, S. (2013), Towards a better measure of customer experience. International Journal of Market Research, 55(2), 227–246.https://doi.org/10.2501/IJMR–2013–021","Kranzbühler, A. M., Kleijnen, M. H., Morgan, R. E., & Teerling, M. (2018). The multilevel nature of customer experience research: an integrative review and research agenda. International Journal of Management Reviews, 2(20), 433–456.https://doi.org/10.1111/ijmr.12140","Kuehnl, C., Jozic, D. & Homburg, C. (2019). Effective customer journey design: consumers' conception, measurement, and consequences. Journal of the Academy of Marketing Science. 47(3), 551–568.https://doi.org/10.1007/s11747–018–00625–7","Laming, C., & Mason, K. (2014). Customer Experience – An Analysis of the Concept and Its Performance in Airline Brands. Research in Transportation Business & Management, 10, 15–25. https://doi.org/10.1016/j.rtbm.2014.05.004","Liu, C. H. S. & Lee, T. (2016). Service Quality and Price Perception of Service: Influence on Word of– Mouth and Revisit Intention. Journal of Air Transport Management, 52, 42–54. https://doi.org/10.1016/j.jairtraman.2015.12.007","Mansouri–Moayed, F., Moradi, M., & Malai, F. (2017). Investigating the effect of service retrieval performance on word of mouth: the role of perceived customer value, customer experience, emotional response and brand loyalty. Tourism Management Studies, 12(39), 49–72.","Meyer C., & Schwager A. (2007). Understanding customer experience. Harvard Business Review, 85(2), 117–26. https://www.jstor.org/stable/44134974","Meiners, N. H., Schwarting, U. & Seeberger, B. (2010). The renaissance of word–of–mouth marketing: a 'new' standard in twenty–first century marketing management. International Journal of Economic Sciences and Applied Research, 3(2), 79–97. http://ijbesar.teiemt.gr/docs/volume3_issue2/rennaisance.pdf","Neslin, S.A., Grewal, D., Leghorn, R., Shankar, V., Teerling, M. L., Thomas, J. S. & Verhoef, P. C. (2006). Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2), 95–112. https://doi.org/10.1177/1094670506293559","Norton, D. W. & Pine, B. J. (2013). using the customer journey to road test and refine the business model. Strategy & Leadership, 41(2), 12–17. https://doi.org/10.1108/10878571311318196","Patricio, L., Fisk, R. P., e Cunha, J. F. & Constantine, L. (2011). multilevel service design: from customer value constellation to service experience blueprinting. Journal of Service Research, 14(2), 180–200.https://doi.org/10.1177/1094670511401901","Podoshen J. S. (2008). The African–American consumer revisited: brand loyalty, word of mouth and the effects of the black experience. Journal of Consumer Marketing, 25(4), 211–222.https://doi.org/10.1016/j.sbspro.2013.10.514","Pullman, M. E., & Gross, M. A. (2014). Ability of experience design elements to elicit emotions and loyalty behaviors. Decision Sciences, 35(3), 551–578. https://doi.org/10.1111/j.0011–7315.2004.02611.x","Rahimi–Baghmalek, J. (2019). Design and explain the loyalty model based on the customer experience in the hotel industry using the data theory strategy of the foundation. Business Management Quarterly, 11(1), 125–140.","Ribamar Siqueira, J. (2020). A bayesian examination of the relationship of internal and external touchpoints in the customer experience process across various service environments. Journal of Retailing and Consumer Services, 53,102009. https://doi.org/10.1016/j.jretconser.2019.1020093","Richardson, A. (2010). using customer journey maps to improve customer experience. Harvard Business Review, 15(1), 2–5.","Rosen, E., & Waller, K. (2009). Consumer Brand Touch Points: A Case Study of Hennes & Mauritz in Sweden & Germany. University of Gothenburg.","Schmidt R. C., Yytinen, K. L., Keil, M., & P. Cule (2001). Identifying software project risks: an international delphi study. Journal of Management Information Systems,17 (4), 5–36. https://doi.org/10.1080/07421222.2001.11045662","Schmitt J., Josko B., & Zarantonello L., (2015). From experiential psychology to consumer experience. Journal of Consumer Psychology, 25, 166–171.http://dx.doi.org/10.1016/j.jcps.2014.09.001","Schallehn, H. Seuring, S., Strähle, J., & Freise M. (2019). Customer experience creation for after–use products: a product–service systems–based review. Journal of Cleaner Production, 210, 929–944. https://doi.org/10.1016/j.jclepro.2018.10.292","Shafiee, S. (2019). modeling factors affecting word of mouth advertising in sports–recreational collections. Physiological Research and Management in Sports, 11(4), 147–163.","Sharma, D. (2016). Enhancing customer experience using technological innovations: a study of the indian hotel industry. Worldwide Hospitality and Tourism Themes, 8(4), 469–480. http:// dx.doi.org/10.1108/09596110010342559","Shirmohammadi, Y. (2020). The effect of prestige, distinction, social benefits on brand productivity through brand attractiveness and customer brand identification (case study: Tabriz leather). Productivity Management, 14(1), 117–142.","Thomas, M. L., Mullen, L. G., & Fraedrich, J. (2011). Increased word–of–mouth via strategic cause related marketing. International Journal of Nonprofit and Voluntary Sector Marketing, 16 (1), 36–49. https://doi.org/10.1002/nvsm.393","Varnali, K. (2019). Understanding customer journey from the lenses of complexity theory. The Service Industries Journal, 39(11–12), 820–835. https://doi.org/10.1080/02642069.2018.1445725","Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009). Customer experience creation: determinants, dynamics and management strategies. Journal of Retailing, 85(1), 31–41. https://doi.org/10.1016/j.jretai.2008.11.001"]}

Published
2023
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24. Application of copula‐based approach as a new data‐driven model for downscaling the mean daily temperature.

Author

Nazeri Tahroudi, Mohammad, Ramezani, Yousef, De Michele, Carlo, and Mirabbasi, Rasoul

Subjects
*DOWNSCALING (Climatology), *SUPPORT vector machines, *ANT algorithms, *GENETIC programming, *TEMPERATURE
Abstract

Downscaling and simulating various meteorological variables at different time scales are fundamental topics for making climate change studies in a geographic region. Here, a new approach for downscaling the mean daily temperature was implemented using a vine copula‐based approach and considering the best CanESM2 predictors. The accuracy of the copula‐based approach was compared with genetic programming (GP), optimized support vector regression (OSVR), support vector machine (SVM), adaptive neuro‐fuzzy inference system (ANFIS) and artificial neural network (ANN) models at Birjand synoptic station in Iran. In the proposed approach, after examining the different vine copulas, the D‐vine copula was selected as the best copula according to the evaluation statistics and tree sequences. According to the root‐mean‐square error (RMSE) and Nash–Sutcliff efficiency (NSE), the accuracy of the ANN model in downscaling the mean daily temperature data was not acceptable and the other considered models were slightly overestimated. The results indicated that the copula‐based approach outperformed the other models in downscaling the mean daily temperature with NSE = 0.61. However, given the 99% confidence interval of the simulations, a slightly overestimation at temperatures above 20°C was observed for the copula‐based approach, which has better performance than the other considered models. The copula‐based approach was able to reduce RMSE by about 82, 20, 24, 47 and 34% compared to ANN, OSVR, GP, SVM and ANFIS models, respectively. The results also showed that the performance of the support vector regression model optimized by the ant colony algorithm is also acceptable and is in the second rank after the copula‐based approach. The accuracy of the copula‐based approach was also confirmed according to Taylor diagram and violin plot. The proposed approach has a higher accuracy than data‐driven models due to use of the conditional density of vine copulas, and the joint distribution of the mean daily temperature and selected predictors. [ABSTRACT FROM AUTHOR]

Published
2023
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27. The Mediating Role of Employee Work Engagement in the Relationship between Leadership Psychological Skills and Employee Voice Behavior.

Author

Hashemiamin, Arezoosadat and Ramezani, Yousef

Subjects
EMPLOYEE participation in management, JOB involvement, JOB satisfaction, LEADERSHIP, STRUCTURAL equation modeling
Abstract

Intensified competitiveness in global markets has directed most organizations toward retaining their employees as their most important intangible assets, because losing such individuals can impose huge costs to them. Therefore, managers do their best to keep employee satisfaction. For this purpose, they need to understand the needs of their employees and the organization's conditions. In such a situation, employees' positive voice behavior is of utmost importance for managers as it helps enhance their awareness. Therefore, it is essential to identify the factors inducing this behavior. Given that workplace comfort might shape employee voice behavior (EVB) and managers with leadership psychological skills (LPSs) can create a state of calmness for these individuals, as highlighted in previous research, the present study reflected on the relationship between the effects of LPSs on EVB. As employee work engagement (EWE) can be also influenced by LPSs and consequently shape EVB, the mediating role of this variable in the relationship between LPSs and EVB was further discussed. To meet the research goals, a total number of 310 employees working in Iranian food industry companies were employed as the study sample. Structural equation modeling (SEM) and the LISREL software were used for data analysis purposes. The study results ultimately demonstrated that LPSs could affect EVB when mediated by EWE. [ABSTRACT FROM AUTHOR]

Published
2022

30. Multivariate analysis of rainfall and its deficiency signatures using vine copulas.

Author

Nazeri Tahroudi, Mohammad, Ramezani, Yousef, de Michele, Carlo, and Mirabbasi, Rasoul

Subjects
*MULTIVARIATE analysis, *RAINFALL probabilities, *DROUGHT management, *CONDITIONAL probability, *CLIMBING plants
Abstract

In the present study, a method based on the conditional density of vine copulas was used to drought monitoring and predicting the rainfall deficiency signature for a 60‐day duration in Dashband, sub‐basin of Lake Urmia basin. The annual rainfall and rainfall deficiency signatures at 10‐, 30‐ and 60‐day durations were considered as variables. D‐, C‐ and R‐vine copulas were used to represent the dependence among the variables, finding that D‐vine copula results to be more accurate for the case of interest. We found that, if the rainfall is less than the long‐term mean in the region, the rainfall deficiency signature for near future can be estimated by acceptable accuracy. Moreover, the results of the conditional probability analysis of rainfall deficiency signature for a 60‐day duration respect to the other variables showed that, on average, the probability of the occurrence of rainfall deficiency signature of 250 mm compared to the long‐term mean in the study area is more than 50% per year. The results showed that the proposed approach may facilitate the meteorological drought management in the considered sub‐basin. [ABSTRACT FROM AUTHOR]

Published
2022
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36. Flood routing via a copula-based approach.

Author

Tahroudi, Mohammad Nazeri, Ramezani, Yousef, De Michele, Carlo, and Mirabbasi, Rasoul

Subjects
*FLOOD routing, *COPULA functions, *FLOOD control, *NATURAL disasters, *FLOODS, *CONFIDENCE intervals
Abstract

Floods are among the most common natural disasters that if not controlled may cause severe damage and high costs. Flood control and management can be done using structural measures that should be designed based on the flood design studies. The simulation of outflow hydrograph using inflow hydrograph can provide useful information. In this study, a copula-based approach was applied to simulate the outflow hydrograph of various floods, including the Wilson River flood, the River Wye flood and the Karun River flood. In this regard, twodimensional copula functions and their conditional density were used. The results of evaluating the dependence structure of the studied variables (inflow and outflow hydrographs) using Kendall's tau confirmed the applicability of copula functions for bivariate modeling of inflow and outflow hydrographs. The simulation results were evaluated using the root-mean-square error, the sum of squared errors and the Nash-Sutcliffe efficiency coefficient (NSE). The results showed that the copula-based approach has high performance. In general, the copula-based approach has been able to simulate the peak flow and the rising and falling limbs of the outflow hydrographs well. Also, all simulated data are at the 95% confidence interval. The NSE values for the copula-based approach are 0.99 for all three case studies. According to NSE values and violin plots, it can be seen that the performance of the copula-based approach in simulating the outflow hydrograph in all three case studies is acceptable and shows a good performance. [ABSTRACT FROM AUTHOR]

Published
2021
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37. Identifying the factors effecting the personal concerns of consumers in accepting new products: A case study on the cosmetic products of Payoon trading company

Author

Ramezani, Yousef, Alizadeh, Negar, Hashemiamin, Arezoo Sadat, Ramezani, Yousef, Alizadeh, Negar, and Hashemiamin, Arezoo Sadat

Abstract

The success of new products is accounted as the superior factor for the accomplishment of companies. The several investigations show that the rate of the failure of new products development has been mentioned as a basic problem in many of companies. One of the reasons of this failure is the high level of concern of the consumers in accepting the new product. The purpose of this research is identifying the effect of these concerns on accepting the new products. This research is practical in terms of purpose and is exploratory survey in terms of methodology. The tool of gathering the information in the stage of identifying the components of personal concerns of consumers is interview and in the stage of testing the hypothesis of research is questionnaire. The population of this research includes consumers of cosmetic product of Payoon trading company. The sample of interview is 23 people and the model of questionnaire is determined by simple random sampling and the formula of determining the volume of Cochran sample is calculated 246 people (the consumers of the products and the expertise). For testing the hypotheses the t-test is used and for ranking these factors the Friedman rating test is used. The results of research show that the fame and reputation has no significant effect on personal concern of users in accepting the new products. Therefore, it has the lowest priority in Friedman rating test. But the other variables including credit, price, product recognition, standards, and ease of use, reliance, and easy accessibility to product have significant effect on personal concerns of consumers in accepting the new product which are mentioned based on the priority of Friedman test.

Published
2019

40. Customer Care in Medical Tourism Industry: Explaining the Relationship between Quality, Satisfaction, Trust and Logical Price.

Author

Ramezani, Yousef, Aref, Masoumeh, and Shahtaghi, Mahdieh

Subjects
CUSTOMER services, MEDICAL tourism, CUSTOMER satisfaction, STRUCTURAL equation modeling
Abstract

Background and Objectives: Despite the dramatic growth of medical tourism in recent years, there is little information about the role of quality, satisfaction and trust in explaining the international patients' behavior after the purchase. The present study investigated these variables. Material and Methods: The purpose of this study was practical and it is a survey-analytic one. The statistical population of this study was all the patients outside the country (clients) of selected hospitals operating in the field of medical tourism in Mashhad and sampling was conducted as convenience. Data analysis was performed using bifurcated and multivariate regression and structural equation modeling in the SPSS and AMOS software. Based on the data gathered from 174 international patients, validity and reliability of the research were confirmed. Results: The results of the study showed that the quality of medical services and the quality of services, patient satisfaction, trust in staff and trust in the medical clinic had impact on the motivation for subsequent referrals of patients and the motivation for the next visit to the medical centers of medical care in Mashhad. Also, the logical price has a moderating role for thoese relationship. Conclusion: The role of tested variables, especially the reasonable price of health services, is important for the international referral of patients, and therefore requires the cooperation of all health care administrators, including nurses, doctors and managers to include these for the prosperity of the tourism industry and the development of the country. [ABSTRACT FROM AUTHOR]

Published
2020
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