Determining the relative importance of factors affecting the success of innovations in forest technology using AHP

Blagojević, Boško; Athanassiadis, Dimitris; Spinelli, Raffaele; Raitila, Jyrki; Vos, John

doi:10.1002/mcda.1670

Cited by 14 publications

(9 citation statements)

References 38 publications

(66 reference statements)

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“…Bernasconi, Choirat, and Seri (2010) suggest that AHP is a proven approach for achieving subjective measurement alternatives. The AHP model has been used to examine a variety of global business studies (Blagojevic, Athanassiadis, Spinelli, Raitila, & Vos, 2020;Chen et al, 2014;Cricelli, Famulari, Greco, & Grimaldi, 2020;Enyinda & Mbah, 2017;Enyinda, Mbah, & Ogbuehi, 2010;Piprani, Jaafar, & Ali, 2020;Wind, 1981). In Section 3.2, we leveraged the AHP model to prioritize the conflict factors.…”

Section: Methodsmentioning

confidence: 99%

Managing marketing–sales–service relationship conflict in a B2B multinational firm

Enyinda

Enyinda²,

Mbah

et al. 2021

Thunderbird Intl Bus Rev

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Managing conflict in marketing-sales-customer service relationship is pivotal for seamless, end-to-end customer experience, satisfaction of customer changing needs, and business performance. Research examining conflict management in marketingsales-service cross-functional relationships is lacking in B2B multinational firms, and particularly in the electronics industry. This article draws on social exchange theory (SET) to posit that conflict management in a B2B electronics firm marketing-salesservice relationship can be successful if trust, commitment, and reciprocity exist. This article examines factors influencing conflict in the team relationship in the focal firm and how to manage them. Data were collected from the departmental managers and were then analyzed with the analytic hierarchy process (AHP)-based multicriteria decision making (MCDM) framework. The findings reveal that the most important factor influencing conflict is task, followed by communication, inter alia. The overall best strategy in managing conflict is collaboration/integration, followed by confrontation. Our research enhances the understanding of conflict factors influencing the focal B2B interdepartmental relationships and the appropriate management strategies. Effectively managing the interdepartmental conflicts can lead to customer satisfaction and improved business performance.

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Section: Methodsmentioning

confidence: 99%

Managing marketing–sales–service relationship conflict in a B2B multinational firm

Enyinda

Enyinda²,

Mbah

et al. 2021

Thunderbird Intl Bus Rev

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“…In AHP, the goal is at the top level and criteria/subcriteria are at the lower levels (Armand & Roger, 2017). A pairwise judgment matrix is used (Blagojevic, Athanassiadis, Spinelli, Raitila, & Vos, 2019) to interpret the weights of each criterion/subcriterion created on its significance rendering to the others. According to Liberatore and Nydick (1997); Yoo and Choi (2006); Panda, Biswal, and Deepak (2014), AHP process can be used to support judgment makers to compute the weight for each criterion and subcriteria by using pairwise judgment matrix.…”

Section: Methodsmentioning

confidence: 99%

Benchmarking the private sector banks in India using MCDM approach

Gupta

Mathew²,

Gupta

et al. 2020

Journal of Public Affairs

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The study aims to rank the performance of Indian private sector banks on the basis of their financial capability. The study is focussed on those banks which are listed in the Bombay Stock Exchange (BSE), for the period from 2014–15 to 2018–19. The financial performance of selected Indian private banks is ranked using a hybrid Multi‐Criteria Decision Making (MCDM) technique, that is, AHP‐TOPSIS. The Analytical Hierarchy Process (AHP) is used to determine the weights of 10 financial performance pointers, and further Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to provide rankings and benchmarking of the private sector banks. Thereafter, overall ranking is given to the selected banks using interval‐valued TOPSIS (IV‐TOPSIS) for a combined period of 5 years. Sensitivity analysis has been performed to check the robustness of the method and its impact on the final ranking of the banks. Results showed that HDFC bank is the top performer and is ranked first for all the years consecutively during the period 2014–2019 and has set a financial indicator benchmark for other competitor banks. South Indian Bank showed the worst results in each year and stood last in the overall ranking. The IndusInd bank is followed after HDFC bank by improving its performance from 2014–19. With a slight margin, Kotak Mahindra Bank ranked after IndusInd. Through the AHP‐TOPSIS technique, each year the private sector banks ranking differs among themselves depending upon the financial performance pointers. Based on interval‐valued TOPSIS (IV‐TOPSIS), it was found that the overall HDFC was the top‐performing bank and South Indian Bank was the worst‐performing bank among all other banks. The results obtained from sensitivity analysis were found to be consistent with IV‐TOPSIS. The paper used the AHP technique, which is subjective in nature, and the results depend on the opinion of the experts. This study will assist the investors who want to channelize their funds in private banks having shares enlisted in BSE. It will accommodate banks to compare and judge their financial performance along with improving their performance and planning their future strategy. AHP‐TOPSIS utilized by past researchers is unable to bring out the cumulative performance for banks, but in this paper, IV‐TOPSIS is utilized, which can be effectively used to evaluate the cumulative performance of banks. The study tries to fill the gap in the literature by providing the overall ranking to private sector banks in India on the basis of financial indicators.

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“…Vaidya and Kumar (2006) identified over than 150-application of AHP in 10 areas including allocation, selection, benefit-cost, evaluation, planning and development, priority and ranking, decision making, forecasting, quality function deployment, and medicine. Examples of AHP applications include the human critical life decisions in selecting an apartment (Obeidat et al, 2018), and selecting an electric vehicle (Al Theeb et al, 2019), in the field of supplier selection (Dweiri etal., 2016), cost-benefit analysis (Wedley et al, 2001), evaluation (Fan et al, 2016), allocation (Yu & Tsai, 2008, planning and development (Chen & Wang, 2010), priority and ranking (Khan & Ahmad, 2017;Kiruthika & Somasundaram, 2018), decision making (Özcan et al, 2011), generating electric power and make sustainable usage of productive soil (Tamayo & Arias, 2019), medicine (Moon, et al, 2015), forecasting (Wang et al, 2014), innovation in manufacturing (Blagojevic et al, 2019) and quality function deployment (Chen, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

The Preference Selection Index Performance in Large Alternatives’ Decisions to Support the AHP: The Case of a University Selection

Obeidat

Ababneh²,

Theeb³

2023

JART

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Two multi-criteria decision making approaches were implemented in this paper for selecting a U.S. university considering the industrial engineering doctorate degree as a case study. The Preference Selection Index (PSI) and the Analytical Hierarchy Process (AHP) were these approaches. A total of 37 universities and 20 attributes were considered. The attributes were related to the university reputation, location, financial, and ease of admission. In this paper, the PSI model was initially constructed and its results were adopted in the AHP model. Data of this paper were obtained from the US News and World Report, Times Higher Education (THE) and other well-known organizations. Results proved that the PSI approach could be used in decisions with large number of alternatives and attributes, and the PSI model was able in making the AHP model requirements easier, by reducing the criteria and alternatives. In both the PSI and the AHP models, the university reputation had the highest preferences of students, followed by the ease of admission, financial and then location. Sensitivity analyses for the PSI and AHP models were performed to evaluate the accuracy of the results. Results of this study could be applied in other students’ disciplines for finding a suitable university.

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Determining the relative importance of factors affecting the success of innovations in forest technology using AHP

Cited by 14 publications

References 38 publications

Managing marketing–sales–service relationship conflict in a B2B multinational firm

Managing marketing–sales–service relationship conflict in a B2B multinational firm

Benchmarking the private sector banks in India using MCDM approach

The Preference Selection Index Performance in Large Alternatives’ Decisions to Support the AHP: The Case of a University Selection

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