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Article

An Extension of Fuzzy SWOT Analysis: An Application to Information Technology

1
College of Management and Accounting, Allame Tabatabaei University, 1489684511 Tehran, Iran
2
Faculty of Management, University of Tehran, 1417614418 Tehran, Iran
3
Department of Economy and Management, Khatam University, 02189174500 Tehran, Iran
4
Faculty of Civil Engineering, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
5
Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, 10223 Vilnius, Lithuania
*
Author to whom correspondence should be addressed.
Information 2018, 9(3), 46; https://doi.org/10.3390/info9030046
Submission received: 30 January 2018 / Revised: 20 February 2018 / Accepted: 24 February 2018 / Published: 27 February 2018
(This article belongs to the Section Information Systems)

Abstract

:
When considering today’s uncertain atmosphere, many people and organizations believe that strategy has lost its meaning and position. When future is predictable, common approaches for strategic planning are applicable; nonetheless, vague circumstances require different methods. Accordingly, a new approach that is compatible with uncertainty and unstable conditions is necessary. Fuzzy logic is a worldview compatible with today complicated requirements. Regarding today’s uncertain and vague atmosphere, there is an absolute requirement to fuzzify the tools and strategic planning models, especially for dynamic and unclear environment. In this research, an extended version of Strengths, Weaknesses, Opportunities and Threats (SWOT) fuzzy approach has been presented for strategic planning based on fuzzy logic. It has solved the traditional strategic planning key problems like internal and external factors in imprecision and ambiguous environment. The model has been performed in an information technology corporation to demonstrate the capabilities in real world cases.

1. Introduction

Classic Strengths, Weaknesses, Opportunities and Threats (SWOT) analysis has been developed based on stable environment that means if the environment of an organization were steady, invariable, and predictable, the classic SWOT analysis could be performed for the organization. In today’s world, environment of organizations is stormy, fast changing, unpredictable, and with uncertainties. For instance, external (or internal) factors of an organization are not always opportunity (strength) or threat (weakness); in other words, in different conditions, they have different meanings. For encountering with today’s complicated and ambiguous environment, fuzzy SWOT analysis is useful and can solve some problems of classic SWOT analysis [1]. The highlights of this paper are using tri-angular membership function, using three α-cut planes for defuzzifying, and a combinational method consisting of TOPSIS and the weighted average for prioritization.
SWOT (an acronym standing for Strengths, Weaknesses, Opportunities and Threats) analysis is a commonly used tool for analyzing internal and external environments in order to attain a systematic approach and support for decision making [2,3,4,5,6,7,8,9,10]. The SWOT approach is based on the aggregation of the internal (strengths, weak-nesses) and external (opportunities, threats) factors for adopting strategies. In other words, the extracted strategies of SWOT matrix is comprised of four categories of factors combinations:
  • Strengths and Opportunities (S-O);
  • Strengths and Threats (S-T);
  • Weaknesses and Threats (W-T); and,
  • Weaknesses and Opportunities (W-O) [11].
Helms and Nixon in 2010 presented a research in which academic researches of the last decade in the field of strategic management, and especially the SWOT method, were analyzed [12]. Moreover, similar researchers analysed and reviewed the performance of SWOT analysis and illustrated its applications, performance and future possible contributions [13]. The previous approaches have not considered quantitative methods to evaluate and sort the strategies under uncertain situations; however, the illustrated literature review that is presented in Section 2 overviews some possible methods for this matter. One possible approach that deals with uncertainty is fuzzy logic.
A fuzzy set is a class of objects with grades of membership. A membership function is between zero and one [14]. Fuzzy logic is derived from fuzzy set theory to deal with reasoning that is approximate rather than precise. It allows for the model to easily incorporate various subject experts’ opinion in developing critical parameter estimates [15]. In other words, fuzzy logic enables us to handle uncertainty [16,17]. There are some kinds of fuzzy numbers. Among the various shapes of fuzzy number, triangular fuzzy number (TFN) is the most popular one. It is represented with three points as follows: A = (a1, a2, a3). The membership function is illustrated in Figure 1. Let A and B are defined as A = (a1, a2, a3), B = (b1, b2, b3). Then C = (a1 + b1, a2 + b2, a3 + b3) is the addition of these two numbers. Besides, D = (a1 − b1, a2 − b2, a3 − b3) is the subtraction of them. Moreover, E = (a1 × b1, a2 × b2, a3 × b3) is the multiplication of them [15,18,19].
The remainder of this research is organized as follows. Initially, the existing research on fuzzy SWOT are presented in Section 2, afterward, Section 3 represents algorithm of proposed fuzzy SWOT. Finally the proposed model is applied to a case analysis for checking the applicability of the model.

2. Literature Review

Ghazinoory et al. presented a method based on fuzzy logic to solve SWOT structural problems, like lack of considering uncertain and two sided factors and the lack of prioritization [11]. In this paper, the triangular membership function has been defined for all factors; the minimum of internal and external factors was calculated for aggregating. In defuzzifying, α-cut plane technique was used and prioritization has been done based on the amount of each fuzzy area in SWOT matrix quadrants. Kheyrkhah mentioned structural problems of classic SWOT like not to prioritize internal and external factors and disability to consider vagueness in some of internal and external factors, and they stated that fuzzy SWOT analysis could solve these problems [20]. Moreover, they compared the extracted strategies from fuzzy SWOT analysis with strategies extracted from classic SWOT analysis in order to show supremacy of fuzzy SWOT analysis. Hosseini Nasab described one of classic SWOT defects and proposed a fuzzy SWOT approach to solve this problem [21,22].
In this paper, three points as a triangular area in EFE-IFE coordinate specified and according to the strategic triangular position (relative position of three points) a realistic strategy has been extracted. Ecmekcioglu proposed multi-criteria fuzzy SWOT to solve classic SWOT problems like not to prioritize strategies and vagueness of factors. The proposed model has three parts. First, using fuzzy AHP to specify the weight of internal and external vector, second, using fuzzy TOPSIS for prioritization, and third, specifying the best strategy proposal by evaluating the internal and external factors [23]. Chernov indicated that there are different uncertainties and vagueness in real competitive market and real economic conditions causing classic SWOT to be ineffective [24].
Amin presented a novel method using fuzzy logic, triangular fuzzy numbers and SWOT analysis to deal with vagueness of human thought. Their quantified SWOT was applied in the context of supplier selection. Moreover, they proposed a fuzzy linear programming model to specify how much should be purchased from each supplier [18]. Ghazinoory illustrated a literature review of SWOT analysis of about 577 papers that have been published up to the end of 2009. Historical development of SWOT, methodological development of SWOT, suggestions and challenges are explained. Furthermore, they stated some problems of SWOT analysis and suggested a proposed model to solve the problems [25].
Kazaz as a first part analysed 50 large construction firms in Turkey by SWOT analysis. They identified each firm primary goal. The results of the first part were used to develop a fuzzy model for determining the main objectives of the firms. Finally extracted strategies related to the firm’s main goal were introduced [26]. Dimic used a SWOT analysis and fuzzy Delphi method as the basis to evaluate impact factors. Fuzzy SWOT analysis is applied to formulate strategic options and the selection of the optimal option is realized through DEMATEL (Decision-making and Trial and Evaluation Laboratory)-based ANP (Analytic Network Process) [27]. Beheshti presented a hybrid COPRAS G with MODM model to optimize the strategy portfolio optimization based on strategies emanated from SWOT Matrix under uncertain circumstances. They applied their proposed model in Iranian mercantile exchange to validate their model [28].
In this paper, a solution that has used fuzzy logic and fuzzy sets theory in SWOT analysis is pro-posed and a mathematical method for different phase of the solution is presented. The strategy selection process, especially the closeness coefficient for the fuzzy area has been extended by adding a step based on TOPSIS method.

3. Algorithm

Algorithm of the proposed fuzzy SWOT analysis consists of six stages as follows:
  • Membership Function;
  • Aggregation;
  • Defuzzification;
  • Prioritization;
  • Extracting strategies;
  • Final Prioritization.
The first two stages are based on paper of Ghazinoory et al. [11]. The general scheme of the algorithm is shown in Figure 2. The last two stages encompassing extracting strategies and final prioritization are the extension and changes added by the authors to the FSWOT based approach.

3.1. Membership Function

Membership function is triangular and specified by tree parameters, as follows:
t r n ( x : a , b , c ) = { 0 x < a ( x a ) ( b a ) a x b ( c x ) ( c b ) b x c 0 x > c
where a, b, and c are pessimistic, probable, and optimistic values, respectively. This membership function is defined for each external and internal factor in the range −10 to 10. An example of the triangular membership function is shown in Figure 3 [29,30].

3.2. Aggregation

Membership functions aggregation is based on following equation:
μ s ( x , y ) = min { μ I ( x ) , μ E ( y ) }
μ I ( x ) and μ E ( y ) are membership functions of internal and external factors respectively and μ s ( x , y ) is the result of aggregation that forms a three-dimensional (3D) surface. Figure 4 shows how this surface is made.

3.3. Defuzzification

In this stage, three α-cut surfaces parallel to SWOT matrix plane are defined for cutting the aggregated surface resulted in previous stage. The value of each of the three surfaces is between 0 and 1 and depends on experience of strategist. If the company is in turbulent and unpredictable market, α value can be close to 0 and if the market is stable and predictable, the values can be close to 1. A rectangular area is generated by crossing the aggregated surface and α-cut plane. In this paper, Picture of the rectangular area in SWOT matrix plane is named “fuzzy area”. Figure 5 shows defuzzification with one α plane.

3.4. Prioritization

There is n i × n e for each α value where ni and ne are the numbers of internal and external factors, respectively. Prioritization is done for every three α value, as Figure 6.
  • According to Figure 4, the center of gravity for every fuzzy area is calculated as [31]:
    x c g = | a 2 a 1 | 2
    y c g = | b 2 b 1 | 2
  • In the second step, according to Figure 3, the closeness coefficient for the fuzzy area is calculated as [14,32]:
    c c j = d j d j + d j + ,   j = 1   t o   n i × n e
    where d j + is the distance between center of gravity and positive ideal point (+10, +10) and d j is the distance between center of gravity and negative ideal point (−10, −10). d j + and d j are calculated, as follows [33]:
    d j + = ( 10 x c g ) 2 + ( 10 y c g ) 2
    d j = ( 10 x c g ) 2 + ( 10 y c g ) 2
Prioritization is done based on ccj value, each fuzzy area with greater ccj has higher priority.
Location of fuzzy areas in SWOT matrix plane has three states as follows:
State 1: one quadrant fuzzy area as shown in Figure 7.
State 2: two quadrant fuzzy area, as shown in Figure 8.
State 3: four quadrant fuzzy area as shown in Figure 9.

3.5. Extracting Strategies

Every fuzzy area is the aggregation result of two internal and external factors and can result in strategy if the two factors are related together. Being related or not depends on the strategist’s experience. Extracted strategy should be based on SWOT matrix quadrant. If the fuzzy area is two/four quadrant, the extracted strategy should be based on quadrant including greater part of the fuzzy area, if extracting strategy is not possible, strategy should be based on the smaller part of fuzzy area. If all parts of the fuzzy area are equal, then strategies are extracted from related quadrant.

3.6. Final Prioritization

The aforementioned stages are performed and analyzed for three α value. Consequently, the score of strategies with three α values is resulted. The priority of any strategy varies according to α value. In this stage, the weighted average for all strategies is calculated as:
r a = i = 1 3 α i × p i
where α i is specified by strategist and p is priority of each strategy depending on α values. Final prioritization is based on r a value. Strategy with smaller r a value has higher priority.

4. Case Study

To examine the applicability of the described algorithm, the proposed method was conducted for an IT company. As described before the stages of the proposed model are illustrated in Figure 10.
Internal and external factors were identified and membership functions were determined by questioning company experts about a, b and c values as shown in Table 1 and Table 2. The values are indicating fuzzy triangular numbers (FTN) for analyzing strength, weakness, opportunity, and threats of the considered organization. These amounts were first gathered by linguistic terms from the expert’s opinion and subsequently transferred to FTN quantitative values.
Based upon the factors mentioned above, the SWOT matrix is denoted as Table 3.
According to the proposed method, membership functions for all of the internal and external factors were generated. In the next stage, aggregation was performed. Aggregation result of I11 and E1 factors is shown in Figure 11.
As described before, in this stage, three α-cut planes cut the resulted surface achieved from aggregation, as shown in Figure 12. In this paper, α values are: 0.1, 0.5, and 0.9. 0.1 is represented for almost indefinite condition, 0.5 is represented for semi-definite condition, and 0.9 is for the almost definite one. This process was done for all (14 × 9) pyramids.
126 (14 × 9) pyramids were generated from aggregation that means there are 126 fuzzy areas for each α value. Figure 13 shows the fuzzy areas that resulted from three α-cut planes. As α increases from 0.1 to 0.9, the fuzzy areas decrease. For α = 0.1, the fuzzy area is four quadrant that has more flexibility in extracting strategies. For α = 0.9, fuzzy area is one quadrant and does not have flexibility for strategy.
Prioritization was fulfilled for each bunch of fuzzy areas. As described before, prioritization is based on closeness of coefficient value. Table 3 shows the result of α = 0.1 prioritization. Percent of each fuzzy area in SWOT matrix quadrants was calculated as shown in Table 4 that is useful for extracting strategies.
Not all of these 126 fuzzy areas result in strategy. The factors should be related and it depends on strategist. After considering and studying these 126 fuzzy areas, 16 strategies were extracted. Table 5 shows the extracted strategies, their priority, α value, and their quadrants. It should be noted that priority of each strategy varies as α value changes.
For final prioritization, the weighted average for each strategy was calculated. According to the Equation (8), ra values were calculated. The strategy with smaller ra has higher priority. Table 6 shows the final priorities.

5. Conclusions

Regarding to problems of classic SWOT for analyzing today’s environment, a different method of SWOT analysis that was based on fuzzy logic for enriching SWOT analysis for analyzing today’s environment was proposed. This method has specifications, like considering two sided factors, more flexibility in extracting strategies, and optimized prioritization. In the contrary to classic SWOT, this method is useful for analyzing unstable and turbulent environment. It is more applicable and reliable than classic SWOT. The highlights of this paper are using triangular membership function, using three α-cut planes for defuzzifying, and a combinational method consisting of TOPSIS and the weighted average for prioritization. The fuzzy logic type one has been implemented in this paper.
When considering the vagueness of environment, velocity of changes in IT industry in Iran, while considering the time frame and the low accuracy of forecasting SWOT factors in future, the SWOT analysis was considered and analyzed under uncertain circumstances by applying fuzzy logic in this research.
The proposed approach for evaluating and selecting the appropriate strategies are completely useful considering vague circumstances. As a case in point in this research, the scheduled approach was performed in an information technology enterprise to solve the complexity and undesirable approaches that were previously employed within their organizations. For increasing applicability and reliability, other fuzzy types are proposed for future researches encompassing hesitant fuzzy linguistic term set, interval valued intuitionistic fuzzy, intuitionistic fuzzy preferences relations, etc. Furthermore, on the basis of strategies ranking, budget limitations, and other structural or organizational policies, a novel stochastic or fuzzy strategy portfolio optimization model could be designed and presented for future researches.

Author Contributions

Mohammad Taghi Taghavifard and Hannan Amoozad Mahdiraji proposed and designed the approach and provided extensive advice throughout the study, Amir Massoud Alibakhshi performed the proposed approach in an Information Technology corporation and analyzed the data, Edmundas Kazimieras Zavadskas and Romualdas Bausys revised the manuscript completely regarding the abstract, introduction, research design and research methodology. All authors have read and approved the final manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Membership function.
Figure 1. Membership function.
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Figure 2. Scheme of algorithm.
Figure 2. Scheme of algorithm.
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Figure 3. Example of triangular membership function for values {−1 ,2, 3}.
Figure 3. Example of triangular membership function for values {−1 ,2, 3}.
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Figure 4. Aggregation [27].
Figure 4. Aggregation [27].
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Figure 5. Defuzzification [27].
Figure 5. Defuzzification [27].
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Figure 6. Center of gravity and coefficient of closeness.
Figure 6. Center of gravity and coefficient of closeness.
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Figure 7. One quadrant fuzzy area.
Figure 7. One quadrant fuzzy area.
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Figure 8. Two quadrant fuzzy area.
Figure 8. Two quadrant fuzzy area.
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Figure 9. Four quadrant fuzzy area.
Figure 9. Four quadrant fuzzy area.
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Figure 10. The stages of the proposed model.
Figure 10. The stages of the proposed model.
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Figure 11. Aggregation result of I11 and E1 factors.
Figure 11. Aggregation result of I11 and E1 factors.
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Figure 12. Three α-cut plane cut pyramid.
Figure 12. Three α-cut plane cut pyramid.
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Figure 13. Fuzzy areas for α = 0.1 ,   0.5 ,   0.9 .
Figure 13. Fuzzy areas for α = 0.1 ,   0.5 ,   0.9 .
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Table 1. Internal factors.
Table 1. Internal factors.
IDDescriptionValues
ABC
I1Great and effective relationship789
I2Great team work culture679
I3Great liquidity579
I4Delay in product designing−9−7−5
I5Employees low level motivation−9−7−4
I6Insufficient publicity−5−31
I7Imperfective processes−5−3−2
I8Inexperienced managers−9−7−5
I9Imperfective planning−10−8−6
I10Human resource shortcomings−5−22
I11Human resource shortcomings in required technologies−6−43
I12Job stress−5−33
I13Imperfective organizing−7−5−3
I14Low creativity and innovation−8−6−4
Table 2. External factors.
Table 2. External factors.
IDDescriptionValues
ABC
E1Upper hand organization support−426
E2Cooperator companies−735
E3Profitable market689
E4Supply exclusivity−424
E5Customers’ dissatisfaction−9−8−6
E6Universities’ capabilities in product designing246
E7Employee’s low paid salary−7−52
E8High price of product−7−6−3
E9Threats increasing in IT field246
Table 3. Strengths, Weaknesses, Opportunities and Threats (SWOT) Matrix of Considered IT Organization.
Table 3. Strengths, Weaknesses, Opportunities and Threats (SWOT) Matrix of Considered IT Organization.
SWOT for IT organizationExternal Factors
OpportunitiesThreats
  • Universities’ capabilities in product designing
  • Upper hand organization support
  • Cooperator companies
  • Profitable market
  • Supply exclusivity
  • Threats increasing in IT field
  • Employee’s low paid salary
  • High price of product
  • Customers’ dissatisfaction
Internal FactorsStrengthsSO StrategiesST Strategies
  • Great and effective relationship
  • Great team work culture
  • Great liquidity
WeaknessesWO StrategiesWT Strategies
  • Delay in product designing
  • Employees low level motivation
  • Insufficient publicity
  • Imperfective processes
  • Inexperienced managers
  • Imperfective planning
  • Human resource shortcomings
  • Human resource shortcomings in required technologies
  • Job stress
  • Imperfective organizing
  • Low creativity and innovation
Table 4. Prioritization, α = 0.1 .
Table 4. Prioritization, α = 0.1 .
RowIEQuadrantPercent of Fuzzy Area in Quadrant 1Percent of Fuzzy Area in Quadrant 2Percent of Fuzzy Area in Quadrant 3Percent of Fuzzy Area in Quadrant 4Closeness of Coefficient
1I1E31100.000.000.000.000.88825
2I2E31100.000.000.000.000.87498
3I3E31100.000.000.000.000.86317
4I1E61100.000.000.000.000.78287
5I1E91100.000.000.000.000.78287
6I2E61100.000.000.000.000.77435
7I2E91100.000.000.000.000.77435
8I3E61100.000.000.000.000.76652
9I3E91100.000.000.000.000.76652
10I1E11462.220.000.0037.780.69864
11I2E11462.220.000.0037.780.69077
12I3E11462.220.000.0037.780.68372
13I1E41452.780.000.0047.220.67411
14I2E41452.780.000.0047.220.66622
15I3E41452.780.000.0047.220.65921
16I1E21444.440.000.0055.560.65308
17I2E21444.440.000.0055.560.64514
18I3E21444.440.000.0055.560.63812
19I12E31233.3366.670.000.000.63133
20I10E31225.4074.600.000.000.62260
21I11E31228.4071.600.000.000.61769
22I6E31211.1188.890.000.000.60921
23I1E71416.050.000.0083.950.60057
24I2E71416.050.000.0083.950.59238
25I3E71416.050.000.0083.950.58523
26I7E320.00100.000.000.000.57809
27I12E61233.3366.670.000.000.56549
28I12E91233.3366.670.000.000.56549
29I10E61225.4074.600.000.000.55681
30I10E91225.4074.600.000.000.55681
31I11E61228.4071.600.000.000.55191
32I11E91228.4071.600.000.000.55191
33I1E840.000.000.00100.000.55051
34I13E320.00100.000.000.000.54559
35I6E61211.1188.890.000.000.54343
36I6E91211.1188.890.000.000.54343
37I2E840.000.000.00100.000.54203
38I3E840.000.000.00100.000.53471
39I14E320.00100.000.000.000.52652
40I5E320.00100.000.000.000.51669
41I7E620.00100.000.000.000.51207
42I7E920.00100.000.000.000.51207
43I4E320.00100.000.000.000.50899
44I8E320.00100.000.000.000.50899
45I1E540.000.000.00100.000.50701
46I2E540.000.000.00100.000.49840
47I12E1123420.7441.4825.1912.590.49753
48I9E320.00100.000.000.000.49299
49I3E540.000.000.00100.000.49101
50I10E1123415.8046.4228.189.590.48894
51I11E1123417.6744.5527.0510.730.48407
52I13E620.00100.000.000.000.47922
53I13E920.00100.000.000.000.47922
54I6E112346.9155.3133.584.200.47563
55I12E4123417.5935.1931.4815.740.47512
56I10E4123413.4039.3735.2311.990.46651
57I11E4123414.9937.7933.8113.410.46162
58I14E620.00100.000.000.000.46006
59I14E920.00100.000.000.000.46006
60I12E2123414.8129.6337.0418.520.45504
61I6E412345.8646.9141.985.250.45313
62I5E620.00100.000.000.000.45027
63I5E920.00100.000.000.000.45027
64I10E2123411.2933.1641.4514.110.44637
65I7E1230.0062.2237.780.000.44418
66I4E620.00100.000.000.000.44265
67I8E620.00100.000.000.000.44265
68I4E920.00100.000.000.000.44265
69I8E920.00100.000.000.000.44265
70I11E2123412.6231.8239.7815.780.44145
71I6E212344.9439.5149.386.170.43288
72I9E620.00100.000.000.000.42705
73I9E920.00100.000.000.000.42705
74I7E4230.0052.7847.220.000.42144
75I13E1230.0062.2237.780.000.41107
76I12E712345.3510.7055.9727.980.40186
77I7E2230.0044.4455.560.000.40085
78I10E712344.0811.9762.6321.320.39290
79I14E1230.0062.2237.780.000.39189
80I13E4230.0052.7847.220.000.38800
81I11E712344.5611.4960.1123.840.38780
82I5E1230.0062.2237.780.000.38217
83I6E712341.7814.2774.629.330.37889
84I4E1230.0062.2237.780.000.37469
85I8E1230.0062.2237.780.000.37469
86I14E4230.0052.7847.220.000.36866
87I13E2230.0044.4455.560.000.36696
88I9E1230.0062.2237.780.000.35967
89I5E4230.0052.7847.220.000.35890
90I4E4230.0052.7847.220.000.35142
91I8E4230.0052.7847.220.000.35142
92I12E8340.000.0066.6733.330.34885
93I14E2230.0044.4455.560.000.34737
94I7E7230.0016.0583.950.000.34521
95I10E8340.000.0074.6025.400.33941
96I5E2230.0044.4455.560.000.33752
97I9E4230.0052.7847.220.000.33650
98I11E8340.000.0071.6028.400.33400
99I4E2230.0044.4455.560.000.32999
100I8E2230.0044.4455.560.000.32999
101I6E8340.000.0088.8911.110.32452
102I9E2230.0044.4455.560.000.31510
103I13E7230.0016.0583.950.000.30909
104I12E5340.000.0066.6733.330.30496
105I10E5340.000.0074.6025.400.29516
106I11E5340.000.0071.6028.400.28951
107I14E7230.0016.0583.950.000.28812
108I7E830.000.00100.000.000.28801
109I6E5340.000.0088.8911.110.27954
110I5E7230.0016.0583.950.000.27762
111I4E7230.0016.0583.950.000.26966
112I8E7230.0016.0583.950.000.26966
113I9E7230.0016.0583.950.000.25426
114I13E830.000.00100.000.000.24751
115I7E530.000.00100.000.000.24028
116I14E830.000.00100.000.000.22331
117I5E830.000.00100.000.000.21104
118I4E830.000.00100.000.000.20171
119I8E830.000.00100.000.000.20171
120I13E530.000.00100.000.000.19431
121I9E830.000.00100.000.000.18385
122I14E530.000.00100.000.000.16494
123I5E530.000.00100.000.000.14924
124I4E530.000.00100.000.000.13683
125I8E530.000.00100.000.000.13683
126I9E530.000.00100.000.000.11175
Table 5. Extracted strategies, their priority, α values, and quadrants.
Table 5. Extracted strategies, their priority, α values, and quadrants.
IDIEStrategyPriorityQuadrant
α = 0.1 α = 0.5 α = 0.9 α = 0.1 α = 0.5 α = 0.9
S1I1E3Monopolizing the designing and supplying products111111
S2I3E3Mobile based products and services development222111
S3I1E6Out sourcing design of products to universities333111
S4I1E9Assigning the company as exclusive reference of designing and supplying the products444111
S5I3E9Acquiring small and hi-tech companies555111
S6I7E3Redesigning processes to improve company agility677222
S7I1E8Out sourcing designing and producing to small companies71111444
S8I13E3Changing current organizational structure to horizontal structure8109222
S9I3E8Instituting suppliers evaluation system91313444
S10I14E3Instituting innovation and creativity framework101212222
S11I3E5Changing after-sale services structure to improve speed and quality of services111414444
S15I8E9Holding management skills instruction courses for managers121516222
S13I6E3Holding annual fairs13661,222
S14I1E7Improving employees’ salary structure14881,444
S15I3E7Improving employees’ welfare measures159101,444
S16I11E1Employing elites with required proficiency1616151,2,3,42,32
Table 6. Final priorities.
Table 6. Final priorities.
IDIEStrategyPriorityraFinal Priority
α = 0.1 α = 0.5 α = 0.9
S1I1E3Monopolizing the designing and supplying products1111.51
S2I3E3Mobile based products and services development22232
S3I1E6Out sourcing design of products to universities3334.53
S4I1E9Assigning the company as exclusive reference of designing and supplying the products44464
S5I3E9Acquiring small and hi-tech companies5557.55
S13I6E3Holding annual fairs13669.76
S6I7E3Redesigning processes to improve company agility67710.47
S14I1E7Improving employees’ salary structure148812.68
S8I13E3Changing current organizational structure to horizontal structure810913.99
S15I3E7Improving employees’ welfare measures159101510
S7I1E8Out sourcing designing and producing to small companies7111116.111
S10I14E3Instituting innovation and creativity framework10121217.812
S9I3E8Instituting suppliers evaluation system9131319.113
S11I3E5Changing after-sale services structure to improve speed and quality of services11141420.714
S12I8E9Holding management skills instruction courses for managers12151623.115
S16I11E1Employing elites with required proficiency16161523.116

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Taghavifard, M.T.; Amoozad Mahdiraji, H.; Alibakhshi, A.M.; Zavadskas, E.K.; Bausys, R. An Extension of Fuzzy SWOT Analysis: An Application to Information Technology. Information 2018, 9, 46. https://doi.org/10.3390/info9030046

AMA Style

Taghavifard MT, Amoozad Mahdiraji H, Alibakhshi AM, Zavadskas EK, Bausys R. An Extension of Fuzzy SWOT Analysis: An Application to Information Technology. Information. 2018; 9(3):46. https://doi.org/10.3390/info9030046

Chicago/Turabian Style

Taghavifard, Mohammad Taghi, Hannan Amoozad Mahdiraji, Amir Massoud Alibakhshi, Edmundas Kazimieras Zavadskas, and Romualdas Bausys. 2018. "An Extension of Fuzzy SWOT Analysis: An Application to Information Technology" Information 9, no. 3: 46. https://doi.org/10.3390/info9030046

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