Selecting Partners in Strategic Alliances: An Application of the SBM DEA Model in the Vietnamese Logistics Industry

: Background: Strategic alliance is a popular strategic option for business entities to strengthen the competitive advantages of all partners in a partnership. The global logistics industry has witnessed the formulation of several successful strategic alliances. However, the Vietnamese logistics industry seems to grow slowly and lacks long-term inter-ﬁrm partnerships. In such a context, it is critical to have a more effective approach to selecting partners in strategic alliances to increase long-term relationships and ﬁrm performance. Method: Thus, this study proposes using the SBM-I-C DEA model to examine and suggest partners for Vietnamese logistics ﬁrms to form strategic alliances. Results: Our ﬁndings show that integrating technology in managing strategic alliances will foster companies in the alliance to formulate a better strategy with up-to-date information on policies. Conclusion: Using the SBM-I-C DEA model, companies can minimize operating costs and optimize delivery time. Thus, companies can better satisfy customers. From the research ﬁndings, some implications are proposed for Vietnamese logistics companies.


Introduction
International commerce has been steadily increasing in recent years due to globalization and economic connectivity among countries, which is deepening and broadening, creating numerous opportunities for import-export enterprises and the country's economy. Logistics services, in particular, are a vital component of international trade. Furthermore, businesses in wealthier countries are rapidly outsourcing their operations to rising regions such as Southeast Asia to reduce manufacturing costs. In addition, with urbanization rates increasing at a massive pace, population densities in cities are on the rise, and supplying those urban areas with goods in a sustainable manner is becoming more and more challenging (Nitsche, 2021, Logistics) [1]. Due to its vast natural resources, low raw material costs, and labor wage, Vietnam is considered one of the most desirable emerging markets. Additionally, our country's topography is suitable for encouraging geographical and political advantages in developing logistics infrastructures such as deep-water harbors, international airports, the Trans-Asian railway system, and international transport hubs.
The logistics industry in Vietnam is considered an emerging market and has an increasing role to play in the development of Vietnam's economy. According to the Vietnam Association of Logistics Service Enterprises, along with the GDP growth rate, industrial production value, import-export turnover, and retail value of goods and services, in recent years, Vietnam's logistics has had a relatively high growth rate of 12-14% [2]. The total import and export turnover of goods since 2010 has increased by 3.6 times. Meanwhile, the GDP has increased by 2.4 times, from USD 157 billion in 2010 to USD 544 billion in 2020, of which exports have increased at an average rate of 4.5%/year, becoming a vital driving force of economic growth. In the past two years, the COVID-19 epidemic has had a strong impact the source of information, and opening more service sectors in this billion-dollar service value chain. As a result, these firms may compete with foreign logistic corporations operating in Vietnam.
For years, strategic alliance has emerged as a popular business strategy for many industries. As a result, numerous transportation companies have identified the potential benefits of forming strategic alliances. Logistics alliance is the logistics model between selfoperated logistics and outsourcing logistics. It combines the advantages of self-operated and outsourcing logistics and reduces the risks of the two opposite models. Regarding the new trends in the logistics industry in the coming years, sustainability is a buzzword that will drive industry changes. Strategic alliance is primarily focused on as it is considered one of several ways to promote sustainability in the global supply chain because it helps reduce transportation costs and air emissions.
Furthermore, a strategic alliance in the logistics industry also helps to connect various members in the global supply chain and make it easier for goods transportation worldwide. According to Nitsche (2021), optimization to plan, control or execute the physical flow of goods and the corresponding informational and financial flows within the focal firm and with sustainable supply chain partners helps productivity increase in logistics networks (applied economics) [1].
However, only a few pieces of research on strategic alliances in the logistics industry have been conducted in Vietnam. In this regard, we use the super (SBM-I-C) DEA (Data Envelopment Analysis) model to analyze and evaluate the ability of domestic enterprises to cooperate [6]. Our sample included 16 Vietnamese logistics companies, and data for analysis were obtained for three years, from 2018 to 2021. The primary goal of our research is to validate the application of the SBM-I-C DEA model in selecting strategic alliance partners for logistics firms in Vietnam.

Logistics Industry and Strategic Alliance
Logistics is a service consisting of people, processes, and technology to deliver the right product at the right cost, time, and place in the right quantity and condition to the right customer. Logistics processes manage the movement and storage of goods among the different supply chain partners [7]. Therefore, measuring the performance of the supply chain is fundamental to identifying and addressing deficiencies in logistics activities, and it serves as a good input for managerial decision making. However, logistics is a wellintegrated trading and product movement system, not only a transportation system.
According to Glaister (1998), a strategic alliance is described as an "inter-firm collaboration over a given economic space and time for the attainment of mutually defined goals" [8]. Similarly, Taylor (2005) stated that a strategic alliance is an interconnection between multi-business partners that shares resources, managerial control, and rewards in collaboration and makes ongoing contributions in one or perhaps more strategic areas, such as technological or product innovation [9]. It is also an efficient paradigm for assisting organizations in accessing and conserving the resources required for dynamic development innovation and risk sharing. Vyas et al. (1995) and Mockler (1997) established a strategic alliance model that emphasizes the essential traits of a successful partnership, including goal integration, should move towards a similar direction, synergy-joint actions should add more value than the sum of their parts by leveraging the strengths of each partner [10,11].
The logistics alliance concept is formed when we combine the strategic alliance definition and the logistics industry characteristics. A logistics alliance is organized by two or more business entities to cooperate through signing contracts in the long term. The primary purpose of the alliance is to leverage members' advantages to share resources, have complementary advantages, and achieve logistics objectives together. A strategic alliance is characterized by interdependence, cooperation, risk, and benefit sharing among alliance members. 2M, Ocean Alliances, and The Alliance are examples of global carrier shipping alliances that pool resources to expand service offerings and geographic coverage. Collaboration among local transportation and logistics industries is expected to increase their ability to compete against multinational firms significantly.
Some studies on strategic alliances have been conducted in Vietnam. For example, Vu (2019) stated that collaboration and joint ventures are critical strategies for improving the performance of logistics businesses in Vietnam [12]. Thus, the authors also emphasize that many enterprises are not capable of accomplishing it with their strength, so a logistics alliance is a reasonable choice. To achieve the best possible outcome from these criteria, we must examine transportation, human resource systems, buildings, upgrading and extending warehouse systems, loading and unloading equipment, and other support services. Moreover, a logistic company should connect and expand its service network in the country and worldwide to create foreign markets and enhance the professional capacity of officials from there. If domestic firms seek to compete for market share with foreign corporations, these variables will be an enormous difficulty to deal with.

Data Envelopment Analysis Model and Its Application
Charnes et al. (1978) established Data Envelopment Analysis (DEA), a statistical approach for identifying the impact of a decision-making unit (DMU) [5]. A DMU is a group of entities that receive the same set of inputs and produce the same set of outputs. In cases of one or more inputs or outputs, the DEA is used to determine relative efficiency [13].
DEA has changed over the years as different models have been modified. Non-radial models, such as Tone's (2001), provided slacks-based measures (SBM-I-C) and input excess and output deficit measurement. However, because early models produce the same score (equal 1) for all units in the efficient frontier, they cannot distinguish between efficient DMUs' performance [14]. The need to evaluate efficient DMUs prompted the creation of a number of super-efficiency models. According to Du et al. (2010) the super-SBM-I-C model accomplishes this by calculating the target DMU's shortest distance to the efficient frontier while excluding the target DM [15].
Much research on the use of DEA in various industries has recently been published. Oum et al. (2008) used DEA models to evaluate the strategic and functional productivity of a Spanish airline in 2001 [16]. Furthermore, Das and Teng (2003) and Wang et al. (2016) applied the DEA model in various areas in businesses such as Renault-Nissan, Merck, and AB Astra [17,18]. Liang et al. (2006) used DEA to enhance the feasibility of supply management sectors. In addition, a substantial study has been conducted to measure the efficiency of the logistics industry in specific cities using a variety of inputs and outputs in conjunction with data envelopment analysis (DEA) [19]. For example, Gen and Syarif (2005) researched the logistics industry's efficiency based on the selection of four variables: delivery reliability, delivery flexibility, delivery cycle, and inventory level [20]. Hamdan investigates the efficiency of the logistics industry with a focus on the rate of return, the delayed arrival rate, and the price. Li and Liu (2019) focused on the number of trucks, the transportation and warehousing and fixed postal investment, the urban road area, and the urban road length. The latter consists of the freight volume and the freight turnover [21].
Similarly, Nguyen and Tran (2018) used the DEA model to evaluate the strategic alliance in Vietnamese logistic firms. They concluded that collaboration among local enterprises could boost supply chain integration, making it more productive and increasing the industry's competitiveness. In addition, they analyzed that the contemporary background of Vietnam is that it is a developing country with a lengthy and dynamic geographical structure. Its logistics are likewise in the development process and appear to have a high potential [22]. However, a lack of experience and technology, fragmented operations, severe price competition among local firms, and dominance by global logistics giants are all challenges that may hinder the local sector's growth. Therefore, strategic alliance is a good strategy for Vietnamese logistics firms. This research uses a procedure with 7 steps. Each of the steps is detailed as follows:

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The data of DMUs were collected from VietStock, which is a famous stock market in Vietnam [23]. In this research, one DMU was selected and is defined as a target company that is a basic company that selects other companies as partners for a strategic alliance.

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Step 2: Selection of input/output variables. • Inputs and outputs are the main impact factors used by DEA model to measure the relative efficiency of a DMU to other DMUs.

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In this step, the super-SBM-I-C was used to measure the efficiency of different DMUs.

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Step 4: Pearson correlation analysis • DEA was used for incompetency estimation for DMUs by developing a comparative effectiveness score through the change in the multiple foundation data into a ratio of a single virtual output to a single virtual input. Subsequently, correlation testing for collected input and output is quite important. In this research, the Pearson Correlation Coefficient Test was used to check the suitability of selected input and output variables.

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Step 5: Analysis before strategic alliance. • This step aimed to select one target company and understand its performance before applying strategic alliance with allied members. This helped to understand the performance of the target company after applying the strategic alliance in the next step. • Step 6: Analysis after strategic alliance. • This step aimed to analyze the performances of various alliances available for the target company selected in the previous step. From the results available from different alliance strategies, we can identify the best one for a selected target company. The performance of each strategic alliance can be estimated by using the super-SBM-I-C model.

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Step 7: Summary (Partner Selection). • This step aimed to summarize a suggestion, based on the previous step. Basically, the strategic alliance should result in positive results that can benefit all allied members. • An overview of the steps is drawn in Figure 1 effectiveness score through the change in the multiple foundation data into a ratio of a single virtual output to a single virtual input. Subsequently, correlation testing for collected input and output is quite important. In this research, the Pearson Correlation Coefficient Test was used to check the suitability of selected input and output variables.

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Step 5: Analysis before strategic alliance. • This step aimed to select one target company and understand its performance before applying strategic alliance with allied members. This helped to understand the performance of the target company after applying the strategic alliance in the next step.

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Step 6: Analysis after strategic alliance. • This step aimed to analyze the performances of various alliances available for the target company selected in the previous step. From the results available from different alliance strategies, we can identify the best one for a selected target company. The performance of each strategic alliance can be estimated by using the super-SBM-I-C model.

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This step aimed to summarize a suggestion, based on the previous step. Basically, the strategic alliance should result in positive results that can benefit all allied members.

Non-Radial Super Efficiency Model (Super-SBM)
In this study, the non-radial slack-based measure of super-efficiency (super SBM) of DEA is applied. This model was introduced by Tone in 2001 [14].
In the super SBM model, given n DMUs with the input and output matrices X = (Xij) ∈ R m×n and Y = (Yij) ∈ R 8×n , respectively. Let λ be a non-negative vector in R n . The vectors S − ∈ R m and S + ∈ R s indicate the input excess and output shortfall, respectively. This model provides a constant return to scale. It is defined in Equation (1) that subjects to Equation (2).
The variable S + measure the distance of inputs Xλ and outputs Yλ of a virtual unit from those of the unit evaluated. The numerator and the denominator in the objective function measure the average distance of inputs and outputs, respectively, from the efficiency threshold. The DMUs (X 0 , Y 0 ) is SBM-efficient, if p * = 1. This condition is equivalent to s − * = 0 and s + * , s + * = 0 if there are no input excesses and no output shortfalls in any optimal solution. The SBM-I-C model is non-radial and deals with input/output slacks directly. The SBM-I-C returns and efficiency measure between 0 and 1.
The best performers have the full efficient status denoted by unity. The super-SBM-I-C model is based on the SBM-I-C model. Tone (2001) discriminated these efficient DMUs and ranked the efficient DMUs by super-SBM-I-C model. Assuming that the DMU (X 0 , Y 0 ) is SBM-I-C-efficient, p * = 1; the super-SBM-I-C model is defined in Equation (3) and subject to Equation (4).
The input-oriented super-SBM-I-C model is derived from Equation (3) with the denominator set to 1. The super-SBM-I-C model returns a value of the objective function that is greater or equal to 1. The higher the value, the more efficient the unit.
Suppose that y r0 ≤ 0. It defines y + r and y + −r by: ..,n y rj y rj > 0 (5) y + r = min j=1,...,n y rj y rj > 0 In the objective function, if the output r has no positive elements, then it is defined as y + r = y + −r − 1 The term s + r /y r0 will be replaced in the following way. (The value y r0 of in the constraints has never changed.) If y + r > y + −r the term is replaced by: If y + r = y + −r the term is replaced by: where B is a large positive number (in DEA-Solver B = 100). Furthermore, the denominator is positive and strictly less than y + r . Moreover, it is inverse to the distance y + r − y r0 . Hence, this scheme concerns the magnitude of the nonpositive output positively. The score obtained is units invariant; it is independent of the units of measurement used.

Data Collection
In this research, 16 companies were recorded as the most notable market logistic organizations. Initial capitalization is targeted at DMUs due to their importance in the logistics industry in Vietnam published in the stock market. The list of 16 companies were included and listed in Table A1 (Appendix A) 3. 1

.4. Input and Output Variables Selection
In this exploration, some previous research in logistic industries were referred to in order to find suitable variables as inputs and outputs. Input and output are the two most important data for evaluating DMUs. These selected variables should be able to reveal the performance of DMUs. Table 1 below shows the summary of input and output variables used in some past research for the assessment of DMUs. Let an optimal solution for SBM-I-C be p * , λ n , s − * , s + * . There are numerous input and output factors that are routinely used to assess the logistics industry's efficiency. The nature of the study and the peculiarities of a certain efficiency evaluation situation determine which input and output variables are used. Based on the theory of "Operational Efficiency" by Lee and Johnson (2013), which emphasizes the relationship between output revenue and the cost of using input resources or the ability to turn input resources into outputs the best in business activities, the input and output variables were selected in this study [25]. Because of logistics operations in Vietnam cost highly compared with other countries such as Thailand, China, and Malaysia, to improve the operational efficiency, cutting down the logistic costs is essential. The input variables include fixed assets, operating expenses, and the cost of goods sold. These are chosen based on the factors occupying the high percentage on Vietnamese logistics costs such as transport cost, warehousing cost, investment in infrastructure, and technology. The output variables are capital, revenue, and operating income. We believe these factors reflect the essential business resources and outcomes of the respective industry. Details of each variable are shown below: • Fixed assets (I): The assets owned by, leased by, or required for the functioning of any Logistics Group firm, as well as any future expansions thereof [22,26,27].

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Operating expense (I): An operating expense is an expense a business incurs through its normal business operations. Often abbreviated as OPEX, operating expenses include rent, equipment, inventory costs, marketing, payroll, insurance, step costs, and funds allocated for research and development [28].

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Cost of goods sold (I): The total costs incurred related to a shipment from the time a transaction is generated to the end of a transaction for a shipment. For export services, the cost of goods sold includes sea freight for export, lifting fee, warehousing fee, and document fee [29] •

Pearson Correlation
There are two major factors of the basic DEA data assumptions; they are homogeneity and isotonicity. Basically, the DEA input data and output data need to be isotonic, which means they have a positive correlation. Therefore, we apply the correlation test as an importance step to make sure the input and output data are isotonic. For example, any increase. In this research, we decide to use Pearson correlation to measure the strength of the linear relationship of normal distribution. According to Lo et al. (2001), the correlation coefficient is always between −1 and +1. If the coefficient of correlation is positive, the factor demonstrates an isotonic solid relationship will be put into the DEA model. On the other hand, if the correlation coefficient is negative, showing a weak isotonic relationship, it will be re-examined [16,33].
The results of correlation coefficients between input and output variables are show in Tables 6-9.    Tables 6-9 provide positive correlations that mean correlation coefficients between input and output variables have a strong relationship. Hence, these data can be used for the analysis of DEA calculations.

Analysis before Alliance
The efficiency of the DERMIs is calculated based on the primary data of 2018, and their ranking before alliances are obtained as well. Table 10 summarizes the empirical results. In this research, we used the super-SBM-I-C model in order to measure the efficiency of 16 DMUs and rank them before alliance with the data of 2019. The result of the rankings and scores is shown in Table 10, with DMU 9 having the highest performance (with the score = 3.88457). The DMU 13 has the lowest efficiency (with the score = 0.1676). Thus, we choose to target DMU 3, which is in the 14th ranking. These low efficiencies indicated the important of alliance strategy, which will help the target company to raise its performance.

Analysis after Alliance
The result form Table 10 shows that the inefficiency score is 0.30894 and low rank is 14th/16. This means the target DMU 3 should enhance the operating activity by implementing alliance strategy. Using the software of the DEA-Solver SBM-I-C model, we combine DMU 3 with 15 other DMUs and obtain the total 31 virtual DMUs. By evaluating this new result, we can see an improvement in the firm's performance after the cooperation.
The results obtained in terms of scores and ranking are presented in Table 11. The score of Table 11 indicates that the target DMU 3 performs the highest efficiency when building an alliance strategy with DMU 2, DMU 15, DMU 4, DMU 8, and DMU 7. This represents the new DMU 3 ranking as being the 26th place. This indicates that any results of cooperation greater than 26th place create better alliance than the original DMUs. Otherwise, if the new ranking is less than the 26th place, then the alliance is even worse. Based on this criterion, this study divided the results obtained into two groups. In order to have an easy comparison, we tabulated 10. The rise in the ranking of DMUs after the alliance demonstrates that the target company can receive advantages from an alliance. Table 10 reveals that 12 companies (i.e., DMU 2, DMU 15, DMU 4, DMU 8, DMU 7, DMU 10, DMU 11, DMU 9, DMU 5, DMU 12, DMU 6, and DMU 1) have the desired features, which correlate with the desire of the partners to do business together.
The virtual companies (DMU 3 + DMU 2; DMU 3 + DMU 15; and DMU 3 + DMU 4) have the greatest number of opportunities to achieve the highest and best efficiency when using a strategic alliance business model (score > 1). Thus, these three companies are highly appreciated when considering a strategic alliance. The second group includes the companies in the category of the not-good alliance partnership.
The first group in the Table 12 display an improvement after an alliance of DMU 3 with 12 other DMUs, including DMU 2, DMU 15, DMU 4, DMU 8, DMU 7, DMU 10, DMU 11, DMU 9, DMU 5, DMU 12, DMU 6, and DMU 1. The top three of the highest efficiencies are defined by the difference of target DMU 3 ranking and virtual alliance ranking (DMU 2, DMU 15 and DMU 4). This means DMU 3 should prioritize to choose these three companies to implement the alliance strategy. Especially, DMU 2 has the greatest potential for cooperation because of its largest difference value (15). In contrast, the second group has three enterprises (DMU 16, DMU 13, and DMU 14) which create a worse cooperate strategy. Therefore, the target DMU 3 should not choose those DMUs for alliance strategy owing to the non-benefits for the target company.

Partner Selection
The best alliance partnerships are identified in the previous section based on the position of the target DMU 3. Nonetheless, we must conduct additional research into the viability of alliance partnerships and compare situations before and after alliances. There are clearly 12 good partners, as evidenced by the results in Table 10. In contrast, the other three partners should not. In other words, DMU 9, DMU 2, DMU 1, DMU 6, DMU 5, DMU 10, DMU 11, and DMU 7 are already performing well; if no special circumstances exist, they have no need to form an alliance relationship with DMU 3.
Combined with Tables 8 and 9, the efficiency and ranking of all DMUs before and after alliance are reviewed again in Figure 2. The points that are closest to the middle are given a higher ranking. The partnership will assist in the creation of a manufacturing system that reduces waste, adds value to the consumer, and achieves perfection. Aside from that, the organization must improve mutual understanding by finding new collaboration opportunities from less viable partnership partners. In a nutshell, the results and conclusions of this case study contribute to new guidelines for strategic alliances. The readers will immediately recognize Quang Ninh Port Joint Stock Company as a prominent candidate for an alliance strategy (DMU 2, the best efficiency improvement for the target company). tem that reduces waste, adds value to the consumer, and achieves perfection. Aside from that, the organization must improve mutual understanding by finding new collaboration opportunities from less viable partnership partners. In a nutshell, the results and conclusions of this case study contribute to new guidelines for strategic alliances. The readers will immediately recognize Quang Ninh Port Joint Stock Company as a prominent candidate for an alliance strategy (DMU 2, the best efficiency improvement for the target company).

Conclusions
Nowadays, the logistics industry and many other industries face numerous challenges, such as: How to achieve competitive advantage and enter new markets? How to obtain new customers and resources and scale up its business? To solve the above-mentioned problems, this research proposes using the super-SBM-I-C DEA model to analyze and suggest solutions for Vietnamese logistics companies when selecting partners in a strategic alliance.
Based on the public data of 16 Vietnamese logistics enterprises from 2018 to 2021, this study used the SBM-I-C model to evaluate each DMU's performance before and after joining a strategic partnership. In our research, the Gemandept Joint Stock Company (DMU 3) was used as a case study to determine the potential benefits of strategic alliances between firms. The DEA-super-SBM-I-C model was applied to evaluate the efficiency of all real DMUs and virtual DMUs. The empirical analysis showed that 12 candidates are suitable for the Germandept Joint Stock Company to form strategic alliances with, except DMU 16, DMU 13, and DMU 14. However, the Quang Ninh Port Joint Stock Company is feasible for the Gemandept Joint Stock Company. From our findings, this research proposed using the DEA-super-SBM-I-C model as a more accurate, appropriate approach to select partners in strategic alliances by evaluating the performance of logistics companies. The model provides a reference for logistic strategists when choosing alliance partners.
In terms of theory, our study validates the SBM-I-C DEA model in a new context of Vietnam. We found that the model has the greatest number of opportunities to achieve the highest and best efficiency when using a strategic alliance business model. In terms of practice, this study provides a mathematical approach to selecting partners in a strategic alliance in the logistics industry of Vietnam. This approach is our new contribution to the related work in an emerging research context as Vietnam, particularly in the logistics industry, is at its embryonic stage of development.

Conclusions
Nowadays, the logistics industry and many other industries face numerous challenges, such as: How to achieve competitive advantage and enter new markets? How to obtain new customers and resources and scale up its business? To solve the above-mentioned problems, this research proposes using the super-SBM-I-C DEA model to analyze and suggest solutions for Vietnamese logistics companies when selecting partners in a strategic alliance.
Based on the public data of 16 Vietnamese logistics enterprises from 2018 to 2021, this study used the SBM-I-C model to evaluate each DMU's performance before and after joining a strategic partnership. In our research, the Gemandept Joint Stock Company (DMU 3) was used as a case study to determine the potential benefits of strategic alliances between firms. The DEA-super-SBM-I-C model was applied to evaluate the efficiency of all real DMUs and virtual DMUs. The empirical analysis showed that 12 candidates are suitable for the Germandept Joint Stock Company to form strategic alliances with, except DMU 16, DMU 13, and DMU 14. However, the Quang Ninh Port Joint Stock Company is feasible for the Gemandept Joint Stock Company. From our findings, this research proposed using the DEA-super-SBM-I-C model as a more accurate, appropriate approach to select partners in strategic alliances by evaluating the performance of logistics companies. The model provides a reference for logistic strategists when choosing alliance partners.
In terms of theory, our study validates the SBM-I-C DEA model in a new context of Vietnam. We found that the model has the greatest number of opportunities to achieve the highest and best efficiency when using a strategic alliance business model. In terms of practice, this study provides a mathematical approach to selecting partners in a strategic alliance in the logistics industry of Vietnam. This approach is our new contribution to the related work in an emerging research context as Vietnam, particularly in the logistics industry, is at its embryonic stage of development.
Nevertheless, this present study has some limitations. Firstly, the DEA is one kind of sensitive method for factor selection. The input/output variables selection could be different, and the results would be impacted. Therefore, a robustness test is necessary. The various input/output variables and removing outliers from DMUs should be re-calculated and re-discussed. For future study, sensitive analysis for different inputs or outputs of DMUs or data of additional years should be included. Moreover, we suggest future research use qualitative methods such as in-depth interviews to verify research results and evaluate the appropriateness of proposed solutions in the actual context of logistics companies. Secondly, the sample size in this study is small. Thus, potential bias in analysis might exist. Expanding the sample to increase the accuracy of analysis results is recommended. Thirdly, this study focuses on data from Vietnamese logistics companies in three recent years, which is limited in terms of timeframe. We strongly suggest that other studies should have a more extended timeframe for analysis to provide more accurate results when using the DEA-super-SBM-I-C model.

Conflicts of Interest:
The authors declare no conflict of interest.