IDEAS home Printed from https://ideas.repec.org/a/ibn/ibrjnl/v11y2018i6p127-138.html
   My bibliography  Save this article

A New Policy-Making Model for Development of National Insurance Services Market Based on Resource-Based Approach

Author

Listed:
  • Mohammad Ayati Mehr
  • Mohammad Haghighi
  • Mohammad Ali Shah Hosseini
  • Assad Allah Kurd Naij

Abstract

Insurance industry is one of those industries providing financial services for people that couldn’t achieve a balanced development in provision of different services. In other words, insurance companies didn’t have a favorable performance as compared with other countries, except for provision of services for automobile industry. This actually pinpoints that the conditions for development and penetration into this market is not that much optimal. The main objective of this study is to provide a policy-making model for the development of service market under the light of a resource-based approach and investigation of model relations. The research method applied in this study is qualitative and it follows an applied objective. The population for this study are specified to the development of the model and interview was used to identify the criteria. The sample includes insurance companies’ managers and experts. These people have at least a bachelor’s degree and they have more than ten years of experience of managerial work. The number of experts included in this study include 20 people using saturation limit approach. The data were analyzed using grounded theory approach. The results showed that the main phenomenon was the concept of market-orientation. In addition, the causal conditions of this study include future-orientation and technological infrastructures. In intervention part of the study, dynamism of industry has been specified. In another part related to the context, culture has been identified. The identified strategies in the field of policy-making include innovativeness, entrepreneurialism, and a positive picture of the industry. Finally, the outcome of this model was the development of the market. The main suggestion of this study was to improve social culture. Besides, it will create a trusting mechanism with regard to policy-making and therefore the required atmosphere for the development and strengthening the market will be created.

Suggested Citation

  • Mohammad Ayati Mehr & Mohammad Haghighi & Mohammad Ali Shah Hosseini & Assad Allah Kurd Naij, 2018. "A New Policy-Making Model for Development of National Insurance Services Market Based on Resource-Based Approach," International Business Research, Canadian Center of Science and Education, vol. 11(6), pages 127-138, June.
  • Handle: RePEc:ibn:ibrjnl:v:11:y:2018:i:6:p:127-138
    as

    Download full text from publisher

    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/75471/41679
    Download Restriction: no

    File URL: http://www.ccsenet.org/journal/index.php/ibr/article/view/75471
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nicky J. Welton & Howard H. Z. Thom, 2015. "Value of Information," Medical Decision Making, , vol. 35(5), pages 564-566, July.
    2. Suh, Yongyoon & Kim, Moon-Soo, 2014. "Internationally leading SMEs vs. internationalized SMEs: Evidence of success factors from South Korea," International Business Review, Elsevier, vol. 23(1), pages 115-129.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lee, Alice J. & Ames, Daniel R., 2017. "“I can’t pay more” versus “It’s not worth more”: Divergent effects of constraint and disparagement rationales in negotiations," Organizational Behavior and Human Decision Processes, Elsevier, vol. 141(C), pages 16-28.
    2. Hussain, Hadia & Murtaza, Murtaza & Ajmal, Areeb & Ahmed, Afreen & Khan, Muhammad Ovais Khalid, 2020. "A study on the effects of social media advertisement on consumer’s attitude and customer response," MPRA Paper 104675, University Library of Munich, Germany.
    3. A. G. Fatullayev & Nizami A. Gasilov & Şahin Emrah Amrahov, 2019. "Numerical solution of linear inhomogeneous fuzzy delay differential equations," Fuzzy Optimization and Decision Making, Springer, vol. 18(3), pages 315-326, September.
    4. Arun Advani & William Elming & Jonathan Shaw, 2023. "The Dynamic Effects of Tax Audits," The Review of Economics and Statistics, MIT Press, vol. 105(3), pages 545-561, May.
    5. Aghion, Philippe & Akcigit, Ufuk & Lequien, Matthieu & Stantcheva, Stefanie, 2017. "Tax simplicity and heterogeneous learning," LSE Research Online Documents on Economics 86613, London School of Economics and Political Science, LSE Library.
    6. Marie Bjørneby & Annette Alstadsæter & Kjetil Telle, 2018. "Collusive tax evasion by employers and employees. Evidence from a randomized fi eld experiment in Norway," Discussion Papers 891, Statistics Norway, Research Department.
    7. Chuangen Gao & Shuyang Gu & Jiguo Yu & Hai Du & Weili Wu, 2022. "Adaptive seeding for profit maximization in social networks," Journal of Global Optimization, Springer, vol. 82(2), pages 413-432, February.
    8. Koessler, Frederic & Laclau, Marie & Renault, Jérôme & Tomala, Tristan, 2022. "Long information design," Theoretical Economics, Econometric Society, vol. 17(2), May.
    9. Annette Alstadsæter & Wojciech Kopczuk & Kjetil Telle, 2019. "Social networks and tax avoidance: evidence from a well-defined Norwegian tax shelter," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 26(6), pages 1291-1328, December.
    10. Sebastian Kaumanns, 2019. "“Some fuzzy math”: relational information on debt value adjustments by managers and the financial press," Business Research, Springer;German Academic Association for Business Research, vol. 12(2), pages 755-794, December.
    11. Samuel J Gershman, 2015. "A Unifying Probabilistic View of Associative Learning," PLOS Computational Biology, Public Library of Science, vol. 11(11), pages 1-20, November.
    12. Arun Advani, 2022. "Who does and doesn't pay taxes?," Fiscal Studies, John Wiley & Sons, vol. 43(1), pages 5-22, March.
    13. Steve Fortin & Ahmad Hammami & Michel Magnan, 2021. "Re‐exploring Fair Value Accounting and Value Relevance: An Examination of Underlying Securities," Abacus, Accounting Foundation, University of Sydney, vol. 57(2), pages 220-250, June.
    14. Panagiotis Ravanos & Giannis Karagiannis, 2023. "Correction: A VEA Benefit-of-the-Doubt Model for the HDI," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 170(2), pages 793-796, November.
    15. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    16. Jacobs, Mattis & Kurtz, Christian & Simon, Judith & Böhmann, Tilo, 2021. "Value Sensitive Design and power in socio-technical ecosystems," Internet Policy Review: Journal on Internet Regulation, Alexander von Humboldt Institute for Internet and Society (HIIG), Berlin, vol. 10(3), pages 1-26.
    17. Kristian D. Allee & Daniel D. Wangerin, 2018. "Auditor monitoring and verification in financial contracts: evidence from earnouts and SFAS 141(R)," Review of Accounting Studies, Springer, vol. 23(4), pages 1629-1664, December.
    18. Bertschek, Irene & Kesler, Reinhold, 2022. "Let the user speak: Is feedback on Facebook a source of firms’ innovation?," Information Economics and Policy, Elsevier, vol. 60(C).
    19. Peretzke, Julia & Sandhaus, Gregor, 2017. "Einsatzpotentiale von Cognitive Computing zur Unterstützung der Entscheidungsfindung im Supply Chain Management," ild Schriftenreihe 53, FOM Hochschule für Oekonomie & Management, Institut für Logistik- & Dienstleistungsmanagement (ild).
    20. Németh Tamás, 2019. "How to back up Modules with blended learning The e-Learning platform of FAME," Prosperitas, Budapest Business University, vol. 6(1), pages 102-112.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ibn:ibrjnl:v:11:y:2018:i:6:p:127-138. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Canadian Center of Science and Education (email available below). General contact details of provider: https://edirc.repec.org/data/cepflch.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.