The upsurge in e-commerce resulted in the growth of the delivery industry, which changed consumer behavior and promoted many emerging business models. According to an e-commerce market research survey, Taiwan’s e-commerce’s turnover reached $
6.28 billion in 2018, with a year-on-year increase of 12.4%, setting a new high over the years; the average annual growth rate was 8.1% in the past 8 years, which is better than overall retail growth (1.1%), and its proportion in the retail industry increased from 3.2% to 5.1%. It can be seen that the transfer of domestic consumption channels has promoted the growth of e-commerce [1
]. The growth in e-commerce along with rapid economic development and changes in consumption patterns made it difficult for the traditional logistics industry business model to meet user needs, prompting logistics service providers to effectively enhance their competitiveness. For organizations, it is necessary to find a systematic method to help them effectively assess their current organizational capabilities, develop service profiles, and formulate competitive strategies to adapt to the turbulent business environment and rapidly changing consumer needs.
Several studies have used resource-based view (RBV) to determine vital organizational resources and capabilities and found that qualities such as “rare”, “valuable”, “irreplaceable”, and “incapable of imitating” form the basis of a company’s sustainable competitive advantage [2
]. Moreover, it was found that competencies including organizational operations, know-how, experience, and implicit knowledge are important for companies to achieve success [4
]. The company’s core competitiveness must have strategic value to guide it in achieving business goals and obtaining a competitive position in the market [5
]. A business strategy should be based on the company’s core competitiveness, which should be fully understood before making good use of external business opportunities and eliminating threats [5
]. The core competency is the basis of promoting a competitive advantage for any organization; this study proposes an assessment model for home delivery companies to identify their capabilities and compare with their competitors to determine their core competitiveness, which can be used to further build better operational strategies to avoid a price war.
Many studies use different methods to discuss the issue of developing organizational capabilities or prioritizing business factors. For instance, Guenzi and Troilo [8
] used a hierarchical value map to develop marketing capabilities, and Kumar and Kumar [9
] evaluated the technological capability of a hospital through multiple regression analysis. Lemmetyinen and Go [10
] used case studies to confirm key capabilities for managing tourism business networks. Erensal et al. [11
] applied the fuzzy analytic hierarchy process (FAHP) to understand the relationship between the competitive advantage, competitive priority, and capability of an enterprise in the context of technology management. Kim et al. [12
] used the analytic hierarchy process (AHP) to investigate the differences in perception among higher education stakeholders regarding the core competencies of tourism graduates. These studies show that organizations should strive to build their own capabilities to differentiate them from other companies, and multi-criteria decision-making methods are widely adopted. The AHP is an appropriate method that allows people to choose from multiple solutions to solve problems [13
]. It eliminates the difficulty of weighted scoring in linear models or the difficulty of standard weighted point estimation and is more accurate than other scoring methods. Occasionally, a company assessment might use both qualitative and quantitative criteria; the AHP can be used to build a system evaluation structure that integrates all criteria with a consistent and simple operation.
Although the AHP/TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) and other multi-criteria decision-making methods have been developed for decades, there are still many scholars who use them as indicators or program selection tools, whether they are discussing environmental issues or related issues on organizational perspectives; e.g., Hidayanto et al. [14
] used the fuzzy analytic network process (ANP) and Ahmadi [15
] used the fuzzy AHP to evaluate the organization’s readiness before implementing an enterprise resource planning (ERP) system; and Kilic et al. [16
] used the fuzzy AHP to discuss ERP system selection. Alansari [17
] proposed a modified fuzzy-AHP approach to analyze the factors influencing employees to bring their own device into an organization. Modak et al. [18
] combined a balanced scorecard and fuzzy analytic hierarchy process to evaluate the performance of coal mining organizations in making outsourcing decisions.
Recently, there is still much research that adopts both the fuzzy AHP and fuzzy TOPSIS to prioritize business indicators or strategic factors, although these are not new methods and have been adopted for years. Keshavarz [19
] used two fuzzy techniques to prioritize competencies for the coil industry in Iran; the result showed that process technology is more important than product technology and technology management. Rohani et al. [20
] used the same tool to measure intellectual capital among companies. Other researchers applied both methods in discussing various business decisions or issues, such as entrepreneurship factors in the banking industry [21
], customer relationship management (CRM) factors in the banking industry [22
], service quality in banking industry [23
], supply chain inventory coordination mechanisms and partnership factors [24
], organization preferences for internet marketing channels [25
], factors effective for marketing relationships [26
], and technology transfer factors [27
], and [28
] applied FAHP to the evaluation of various dimensions of IT capabilities, data quality, database security and knowledge management system implementation. These studies show that the AHP and TOPSIS methods are very useful and reliable in ranking factors. However, the uncertain information in the decision-making process and the ambiguity of human perception make it difficult for decision makers to accurately assess and capture subjective feelings and object recognition; thus, the fuzzy set theory [29
] can be used to assist in making objective and accurate decisions. Moreover, in order to ensure that the opinions or ideas of decision-makers can be expressed freely and adequately, the fuzzy set theory with a natural language concept can be applied as well. Therefore, this study combines fuzzy set theory with a linguistic value to establish an evaluation model and help organizations identify their organizational capabilities and solve complex problems in a fuzzy environment.
The purpose of this research is to establish an objective and systematic hierarchy of organizational capabilities for home delivery companies. In addition, the analysis process integrates fuzzy sets, the AHP, linguistic values, and ideal and anti-ideal solutions (the main concept of TOPSIS) to help companies understand their capabilities and compare them with those of their major competitors to enhance their competitiveness. The research questions of this paper can be summarized as follows:
What are the major organizational capabilities of the home delivery industry?
Is there a systematic and objective method for organizations to easily identify organizational capabilities for continuous improvement or enhancing competitive advantages? An empirical study of applying the fuzzy AHP and an ideal and anti-ideal approach is conducted to demonstrate the effectiveness of the proposed method.
What are the management implications of applying multi-criteria decision-making methods to identify organizational capabilities?
This article is organized as follows: first, the development of home delivery industry in Taiwan is introduced; then, the competency structures of home delivery companies are constructed using literature and expert review; next, the importance of each capability for home delivery companies is identified with the fuzzy AHP; thereafter, the competencies of each case company are evaluated and compared with those of other companies using the ideal and anti-ideal concept; and finally, the core competencies of each case company are formulated, and appropriate business strategy suggestions are provided based on the analysis.
4. Empirical Study
In order to illustrate the operation of this model, this study chose three major home delivery companies in Taiwan for the application of the model and to provide a reference for the home delivery industry. The analysis steps are presented in Figure 1
to, firstly, demonstrate the analysis flow.
The current top three home delivery companies in Taiwan are Company T (denoted as CT), Company P (denoted as CP), and Company H (denoted as CH). Company T has a contract with a Japan transportation corporation, relies on 5655 convenience stores (statistics to December 2019) as a strong backup, and has become the leading brand home delivery company in Taiwan. Meanwhile, Company P runs a similar business model to Company T. Both expand the market actively and make great profits in the e-commerce channel. Due to the saturation of the cargo market, Company H has also entered the express delivery industry based on its logistics experience in recent years. The intense competition has led to a price war in the home delivery industry in Taiwan.
4.1. Constructing the Hierarchy of Organizational Capabilities and Questionnaire Design
We consulted two experts (two managers who have worked in the industry for more than 5 years) and two scholars (university professors who are teaching logistics-related courses and conducting research in this field) for the organizational competencies of the home delivery industry. Their opinions and suggestions helped us to construct the hierarchy of organizational capabilities and refine the questionnaire.
There are two sections in the questionnaire: the first one examines the importance of each capability within the industry, and the second one identifies the key capabilities for the company. The four categories of organizational competency based on previous literature were included in the questionnaire, which are (1) basic organizational competencies, (2) special competitive competencies, (3) value-added competencies, and (4) management competencies. The hierarchical structure of capabilities was adopted for managers to select the key competencies for their companies. The first level is the purpose of this study, and the second level enumerates four categories of organizational capabilities. A total of twenty-five capabilities were illustrated in the third level derived from each category (as shown in Table 2
4.2. Data Collection
Data were collected through a survey administered to managerial levels in all three companies to determine the importance of organizational capabilities and identify the perceived capabilities of their companies. A total of ten managers per company, which included operations managers and logistics managers, participated in the survey conducted in 2019.
4.3. Fuzzy Weighting for All Capabilities
In this study, we applied the concept proposed by Kahraman et al. [53
] to measure the relative weight of each capability. Next, the method developed by Buckley and Uppuluri [56
] was utilized to calculate the fuzzy weights of each fuzzy matrix with the geometric mean method.
The fuzzy weight and integrated weight are presented in Table 3
. Given a positive reciprocal matrix
, the geometric mean of each row was computed using:
4.4. Capability Level Analysis
4.4.1. Calculation of the Average Ratings of Capabilities
The present study used linguistic values to determine the company’s strengths in various capabilities and converted the values to triangular fuzzy numbers (as defined in Section 3.2
). The average rating of each capability was generated for each case company and is shown in the first three columns of Table 4
4.4.2. Determination of the Ideal and Anti-Ideal Solutions, and Close Indices
The method introduced in Section 3.3
for ranking the TFNs for each capability in the case companies was applied, and the ideal and anti-ideal solutions were obtained, correspondingly. Hereafter, the distances between each capability of each case company and the ideal solution were calculated; the close indices are shown in the last three columns of Table 4
. These results show that the various capabilities of each case company are close to the ideal values. A value of 1 indicates that the case company not only possesses this core capability but also performs better than its competitors.
4.5. Company Level Analysis
4.5.1. Determination of the Distance from Ideal and Anti-Ideal Solutions
The distance between each case company and the ideal (
)/anti-ideal solution (
) was also calculated, and the results are shown in Table 5
4.5.2. Attainment of the Close Indices
The close index (C
*) for each case company was obtained using Equation (8) (Section 3.5
), and the results are shown in Table 6
. The managers were able to clearly identify their main competitors in the market and understand their own advantages and disadvantages in various organizational capabilities. Moreover, the managers were completely aware of their company’s differences to competitors, reflected through the close index of each capability.
The assessment model presented in this article can help companies to obtain two types of information: one is a micro perspective, which is the internal inspection of organizational capabilities; and the other is a macro perspective, which is the external comparative analysis between a company and its competitors, and constitutes the key organizational capabilities of the industry. Based on this information, companies can formulate appropriate operational strategies to improve service quality or create new service projects to differentiate them from their competitors and to enhance competitiveness.
4.6.1. Micro Perspective
Based on the integrated weights shown in Table 3
, the superior organizational capabilities of each case company are marked with an asterisk * and summarized in Table 7
Company T outperformed the other two companies on 10 out of 25 organizational capabilities, while Company P showed better advantages on 12 capabilities. Large cargo transportation is the main business of Company H. As mentioned earlier, it has just entered the consumer home delivery industry and has been providing its services for a short time. Compared with the other two companies, having many service agents is its only advantage. On the contrary, both Company T and Company P use their own convenience stores as service networks without the need for service agents. In particular, Company T is the leading retailer of convenience stores with a strong network of 5565 stores and a dynamic group synergy; thus, it performed well on capabilities, such as “C27 Complete transportation network”, “C33 Service convenience”, and “C46 Strategic management”.
As for Company P, the analysis showed that it outperformed Company T in logistics-related capabilities, such as “C13 Number of warehouses”, “C15 Operation equipment”, “C12 Cost control”, “C21 On-time delivery”, “C22 Service price”, and “C42 knowledge management”. This is because it was originally a traditional logistics company. In addition, Company P took the lead in launching a loyalty reward program combined with mobile applications in 2016, marking a new era of big data applications. This increased its advantage in value-added capabilities, such as “C24 IT ability”, “C25 Collaboration ability”, “C26 Innovation ability”, “C31 Customer satisfaction”, and “C34 Diversified service”. In recent years, Company P has developed rapidly to catch up with Company T. It is not only the second largest retailer of convenience stores with 3630 stores (statistics to 2019/12) but is also actively creating more innovative services.
4.6.2. Macro perspective
shows the overall comparison of the three case companies. The results show that Company P has more capability advantages than the other two case companies and is the most competitive. Company T and Company P have strong rivalry. The top three important capabilities for the home delivery industry based on the integrated weight in Table 3
Service price” (0.078), “C21
On-time delivery” (0.073), and “C23
Secure delivery” (0.07). All three capabilities belong under the category “C2
Special competitive competencies”. This implies that a company should possess unique competencies in order to hold the leading position in the industry.
However, the values shown in Table 6
are the weighted result of each capability. As the weight of each capability changes, the result will also change. Therefore, when managers reshape the company’s business strategies based on the information obtained, it is suggested that the weights of various capabilities should be confirmed retrospectively to accurately grasp the strategy’s advantages, disadvantages, and improvement priorities.
This study combined the fuzzy analytic hierarchy process and an ideal and anti-ideal approach, and illustrated, using practical data, that it can help the enterprise to identify organizational capabilities and determine its core competitiveness portfolio. Based on the limitation of internal resource allocation plans, managers can reformulate strategies to enhance their competitiveness. The empirical study provided twenty-five major capabilities for the home delivery industry and demonstrated the effectiveness of the organizational capability assessment model. The results showed that the “C2 Special competitive competencies” and “C1 Basic organizational competencies” were the two most important categories of organizational competency for the home delivery industry. The top five organizational capabilities were “C22 Service price,” “C21 On-time delivery,” “C23 Secure delivery,” “C11 Specific assets”, and “C12 Cost control”. These are essential and critical capabilities to maintain competitiveness in the home delivery industry.
This method not only helps companies to identify their organizational capabilities from an objective perspective but also to compare them with their competitors to obtain insights for enhancing their competitive advantage. This paper verifies, again, that the integrated application of the multi-criteria decision-making method and fuzzy theory is an objective method that is suitable for alternative selection or criteria prioritization in a turbulent business environment. Moreover, the integrated method is recommended to apply to various business issues, for example, to gather key customer opinions for obtaining valuable ideas, thereby improving the company’s service quality or helping to build a better service portfolio. Finally, this article uses existing methods for application analysis rather than the development of new methods. It is suggested that future research could take into account rough theory, the neural network concept, or other analysis techniques to improve information quality.