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Article

Improving Farm Cooperatives’ Performance and Sustainability: A Study of Agricultural Managers’ Competencies Based on the Grounded Theory and the fsQCA Methods

by
Xiyuan Yu
1,
Wenli Liu
1,*,
Lingli Qing
2 and
Di Zhang
1
1
School of Business, Hunan Agricultural University, Changsha 410128, China
2
School of Accounting, Guangzhou College of Commerce, Guangzhou 511363, China
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(2), 1263; https://doi.org/10.3390/su15021263
Submission received: 6 December 2022 / Revised: 27 December 2022 / Accepted: 28 December 2022 / Published: 9 January 2023
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Abstract

:
As an effective talent selection and performance management method in enterprises, can the competency model also play an essential role in farm cooperatives? Limited research currently focuses on improving farm cooperatives’ performance through agricultural managers’ competency. Our study takes the initiative to create the “agricultural manager competency model,” which includes five competency dimensions: knowledge and technology, personal capabilities, career orientation, personality traits, and intrinsic drive. On this basis, the multiple competencies are analyzed by the fuzzy set qualitative comparative analysis(fsQCA). We found that agricultural managers improve the performance of farm cooperatives. There are four paths to generate high performance, which summarize three types of agricultural managers: technical career, managerial career, and integrated entrepreneurial.

1. Introduction

Farm cooperatives are an important means of promoting agricultural modernization in China, playing an important role in organizing smallholder production, imparting production technologies, improving bargaining power for agricultural products, and providing credit access [1,2,3]. By the end of April 2021, 2.259 million farm cooperatives had been established nationwide, 86 times more than in 2007. It is not the quantity, but the quality of sustainable development that becomes the issue that cooperatives should focus on [4]. Small scale, poor management, over-reliance on government support, etc. are the main problems that limit the development of cooperatives. Therefore, it is worth exploring effective ways for agricultural managers to improve the quality of cooperative management. In China, agricultural managers were officially included in the National Occupational Classification in 2019. It refers to people who are engaged in management services, such as agricultural production organization, equipment operation, technical support, product processing and marketing, in agricultural cooperative organizations such as farmers’ cooperatives. Generally, agricultural managers are internally elected or externally hired, and receive compensation in the form of salary or dividends while working for the maximum benefit and sustainable development of the cooperative. Extensive practice shows that, as a new type of agricultural management talent in China, agricultural managers can effectively fill the management capacity gap within the organization. They give full play to the role of “capable leaders” after entering farmers’ cooperatives, which is essential for optimizing the organizational governance structure, alleviating the “talent crisis” in rural areas, and accelerating rural revitalization and sustainable development [5].
Currently, the studies conducted around agricultural managers mainly focus on their cultivation policies [6,7], introduction dilemmas and countermeasures [8], entrepreneurial financing [9,10], and their impact on organizational performance [11]. Regarding the relationship between agricultural managers and cooperative performance, it is generally believed that agricultural managers’ entrepreneurial talent can significantly improve cooperatives’ sustainable competitive advantage [12,13]. However, some scholars point out that introducing agricultural managers will increase agency costs and may introduce laziness and corruption problems [14,15], thus reducing productivity and performance; this suggests that the role of agricultural managers in cooperative performance still needs to be clarified [16]. So how do we identify agricultural managers who genuinely meet the needs of the cooperative [17,18]? Studies conducted on competency models have focused on acute care nurses, safety managers, and BIM Leaders [19,20,21]. Few studies have focused on the impact of agricultural managers’ competencies on cooperative performance. As a new profession developed in China, the competency of agricultural managers is a vital appointment evaluation criterion for cooperatives to consider and an important factor affecting the sustainability of cooperatives. Therefore, it is necessary to establish an exclusive competency model for agricultural managers and explore the path of the competency model to influence the performance and sustainability of cooperatives.
Deciphering how to screen the competency level of agricultural managers to achieve sustainable organizational development has become a significant practical problem that needs to be solved urgently. Since China’s agricultural manager industry has not yet formed a unified professional competency model, our first task is not to explore a single competency quality, but to build a complete competency model for agricultural managers [22]. Then, based on the competency model, our task is to find the combination of competencies that can effectively improve the cooperatives’ performance. In terms of research methods, previous studies have often used methods that include ordinary least squares [13], structural equation modeling [18], and the Dematel method [23]; these are suitable for general situations, but ignore the combination effects among competency qualities. The various competency qualities of agricultural managers do not produce results independently, but influence cooperative performance through a combination of different competencies.
Therefore, based on the current research results, this study explores constructing a competency model for agricultural managers and identifying possible competency combinations to improve cooperative performance by combining Grounded Theory and fuzzy set Qualitative Comparative Analysis (fsQCA) methods [24]. First, the agricultural managers’ competency model could mitigate existing research gaps and provide ideas for constructing industry generic competency models. Second, this study uses a hybrid method combining Grounded Theory and fsQCA in a new attempt to study the competency model and its impact on organizational performance. Third, the conclusion summarizes representative types of agricultural managers, including technical career, managerial career, and integrated entrepreneurial, that can help cooperatives identify and hire the right agricultural managers. Moreover, the conclusions can provide a theoretical and decision-making basis for cultivating a high-quality agricultural manager team, improving farmers’ cooperatives’ performance, and promoting rural modernization and sustainable development.
The rest of the paper is structured as follows: Section 2 conducts a literature review and outlines the research framework; Section 3 presents the research method and sample; Section 4 reports upon the agricultural managers’ competency model and the fsQCA research results; and Section 5 summarizes the main findings, and contributions and provides managerial implications, limitations, and future research directions.

2. Literature Review

Farmer cooperatives are widespread in Europe, while, in contrast, farmer cooperatives in China started to develop gradually in the 1980s. Unlike classic cooperatives, Chinese farmer cooperatives are generally not formed by ordinary farmers on their own initiative, but are led by rural elites, such as local government officials, businessmen, extended professional families, or agribusinesses [25,26]. In addition, there is an over-reliance on government for farmer cooperatives [27]. Some of the cooperatives seem to be established only to receive government subsidies and do not actually operate [28]. Most of them are seen as “shell cooperatives”. Inherent deficiencies, coupled with a lack of management, have become key issues limiting the sustainable development of China’s farmer cooperatives. The role of human capital in organizational development is significant. Therefore, improving the quality of management is an important opportunity for the development of cooperatives.
The Upper Echelons Theory suggests that the performance of an organization ultimately depends on the behavior of the top managers [29]. Whether or not agricultural managers, as external managers introduced by farmers’ cooperatives, can sustainably improve organizational performance has been a hot issue in related research. On the one hand, some scholars believe that agricultural managers can help improve cooperative performance [30,31,32]. With the deepening of the agricultural scale, agricultural managers are crucial human capital to realize the organic linkage between traditional farmers and modern agriculture [33]. Excellent agricultural managers have strong competencies in resource integration and utilization, production and operation, organization and management, innovation and entrepreneurship, and good government and social network relations [34,35,36,37,38]. They could play a vital role in the organization’s orderly production, internal governance improvement [39], new business expansion [40], and capital financing to help cooperatives develop sustainably [41]. On the other hand, some scholars have argued that agricultural managers will trigger the principal–agent problem. Ma Yanli (2019) pointed out that agricultural managers are only involved in profitability and are not burdened with losses, which changes the organizational principal–agent problem from one layer to two layers; this results from increasing agency costs [14,42]. Moreover, because of the imperfect organizational governance structure, the presence of asymmetric information and opportunistic motives [43,44,45], and the difficulty and high cost of monitoring, the cooperation between the agricultural manager and the cooperative is not always stable on both sides [15].
Focusing on individual agricultural managers, a high level of competence can improve individual performance and subsequently achieve a high level of cooperative performance through efficient work [46]. In practice, the construction of the agricultural manager model needs to reflect its professional characteristics [22]. In order to escape the limitations of traditional agricultural profitability, which is squeezed by the “cost floor” and “price ceiling”, the competency model of agricultural managers should emphasize rural love and entrepreneurial traits more than the generic competency model of managers [47]. Spencer believed that competence should be specific to different work environments and should help companies identify the best in different job types [48]. They defined the above-water portion of the iceberg model as the Threshold Competencies, and the below-water portion as the Differentiating Competencies. When applied to the profession of agricultural manager, the threshold competencies should include knowledge and ability. The knowledge level should involve knowledge of agricultural production, business, policies, and regulations. At the ability level, agricultural managers should have the basic abilities of a manager, such as organizing production, managing employees, and selling agricultural products. The differentiating competencies reflect more of the deeper traits of agricultural managers. Agricultural managers should have a passion for agricultural work and a commitment to long-term professional pursuits. In a word, competency research is primarily applied to improve organizational performance and differentiate quality talent [49]. Agricultural managers are an important external force in promoting the transformation of traditional agriculture to modernized agriculture. They are different from farmers in the past and managers of modern enterprises. Therefore, since different levels of competency have different impacts on performance, organizations need to identify the competency of agricultural managers and select them to hire the right agricultural managers, in line with their development needs [22,50].
So, what competencies should agricultural managers have? Why does the impact of different agricultural managers on cooperative performance vary? How do we select agricultural managers who meet the needs of organizational development? These problems become an essential issue that needs to be solved to smooth the way for agricultural managers to enter farmers’ cooperatives. Therefore, this study intends to establish the following analysis framework (Figure 1): 1. to qualitatively analyze the core competencies that agricultural managers should possess, based on interview records, and construct a competency model for agricultural managers; 2. To explore the variability of the impact of different competency couplings on cooperative performance, based on the comparison of actual performance changes after the introduction of agricultural managers; 3. to summarize the representative combinations of competencies and clarify the categories of talents required by farmers’ cooperatives.

3. Methodology

First, this study explores the professional characteristics of agricultural managers and constructs a model of agricultural managerial competencies based on the grounded theory. Second, based on the fsQCA approach, we explore the impact of competency organization on cooperative performance and provide theoretical guidance to promote the sustainable development of the agricultural manager profession and their role in cooperatives.

3.1. Sample

The data used in this study are from a study conducted in seven municipalities in Hunan Province from May to December 2021. In order to gain insight into the job responsibilities of agricultural managers, this study selected 32 interviewees; they were agricultural managers, cooperative presidents, employees, and local government managers. The interviews were conducted in a face-to-face format, with an average length of 45–90 min each, to obtain a realistic description of the agricultural manager’s competencies. These interviewees are familiar with the requirements of an agricultural manager and can provide us with answers to what competencies an agricultural manager should have, as shown in Table 1.
According to the competency model constructed by the interview data, we selected cooperatives that met the requirements and had the following traits: the agricultural manager who had been in the farmer’s cooperative for more than one year, and the cooperative that had more than three years of regular business activities, operational assets, facility configurations, and an established and complete management system. In total, 94 cooperatives met the requirements. One-on-one research was conducted with these agricultural managers to collect data on fsQCA conditions and outcomes. The sample characteristics are shown in Table 1.

3.2. Research Design

The study used Behavioral Event Interview (BEI) to identify the competencies required for the career [51]. Open-ended behavioral review interviews could understand respondents’ “significant events” and “behavioral descriptions” in their practice, thereby revealing the potential competencies of high performers. In this study, a semi-structured interview was conducted, with only an outline of the interview set; the interview topic and pace were controlled, giving the interviewees as much freedom and autonomy as possible. The interview questions were as follows: What qualities or abilities do you think an excellent agricultural manager should have? What are the main qualities or competencies needed for the scope of work of an agricultural manager? Recall 2–3 incidents of the most successful and least successful agricultural managers. The questioning style was adjusted according to the interviewee’s identity, to ensure a complete and adequate interview.
After obtaining the BEI interview transcripts, they were coded according to the “Grounded Theory” to summarize the competencies of agricultural managers. The Grounded Theory advocates that researchers do not make any theoretical assumptions in advance but make decisions directly through actual observation, collection, and data analysis, producing theories with universal applicability [52]. Grounded Theory emphasizes the flexible use of coding guidelines in the research process. Scholars conceptualize fragmented texts and categorize them into entity theories through scientific logic integration, deduction, and analysis [53]. This paper chooses the Grounded Theory method to analyze the professional characteristics of agricultural managers. The interview record underwent a triple coding of open coding, axial coding, and selective coding to form a competency model for agricultural managers. Finally, the saturation test proved that the competency model constructed in this study was fully explored.
Introduced by Ragin, the QCA method combines the advantages of qualitative and quantitative analysis, and provides a new approach to solving complex causality, causal asymmetry, and disparate paths [54,55]. Exploring the impact of agricultural managers’ competencies on improving cooperative performance is a complex coupled process. Competencies do not affect cooperative performance singularly, but can have different effects on improving cooperative performance depending on the strengths and combinations. In order to clarify the optimal competency portfolio of agricultural managers and better promote sustainable organizational growth, this paper uses the QCA method to explore the impact of agricultural managerial competencies on cooperative performance. First, the interview record was converted into QCA data through calibration. Second, necessity analysis was used to demonstrate that there was no single competency determining the cooperatives’ performance. Third, we explored the combination of competencies of agricultural managers that have a high performance through sufficiency analysis. Finally, the robustness test was used to examine the stability of the results.

4. Results and Discussions

4.1. Agricultural Managers Competency Model

4.1.1. Open Coding

Open coding is the process of conceptualizing and categorizing interview records. By transcribing, reading, and summarizing the original data word by word, avoiding the interference of subjective factors, we could extract the concepts and categories related to the competency of agricultural managers [56]. Firstly, we used a stratified coding method to code the effective conversations in the interview records. The original record code of 32 interviewees is 1396, focusing on the initial conceptualization of the main competencies. Secondly, we screened 1396 records with a repetition frequency higher than 3, and obtained 131 key information codes in the order of A01, A02...A131, as shown in Table 2. Finally, further refinement by generalization–categorization, and iterations to obtain 21 initial competencies, coded as B01, B02… B021, are shown in Table 3.

4.1.2. Axial Coding

Axial coding refers to summarizing similar categories based on the conceptualizations formed by open coding, and then condensing them to form main categories [57]. By further sorting out the internal logic and the role of relationships among the categories, this paper integrates the 21 categories. It forms five main categories: knowledge and technology, personal capabilities, career orientation, personality traits, and intrinsic drive, as shown in Table 3.

4.1.3. Selective Coding

Selective coding is generalizing the core categories and reconstructing the theoretical framework. According to the “above-water” part and “below-water” part, the five main categories are classified into two core categories to derive the agricultural manager competency model, as shown in Figure 2. The threshold competencies include knowledge and technology, and personal capabilities; the differentiating competencies include career orientation, personality traits, and intrinsic drive.

4.1.4. Saturation Test

The saturation test involves recording the new data at three levels until no significant new categories appear. By the time the 25th interview transcript was coded, the open and axial coding had covered most of the concepts and categories, and no core categories emerged except for the five existing core categories (knowledge and technology, personal capabilities, career orientation, personality traits, and intrinsic drive). Then, by the 30th interview record, only six new concepts and one new category emerged, and the obtained concepts and categories were almost final. In the 31st and 32nd interviews, there were no new concepts and categories of competencies. Thus, the competency model of agricultural managers has reached theoretical saturation.

4.2. fsQCA Analysis Results

4.2.1. Measurement and Calibration

According to the Agricultural Managers Competency Model (Figure 2), we set five preconditions, namely knowledge and technology (KT), personal capabilities (PC), career orientation (CO), personality traits (PT), and intrinsic drive (ID). Annual operating income, annual net income per member, and the production of pollution-free and organic agricultural products represent the farmer cooperatives’ economic, social, and ecological performance [58,59,60].
In this study, we use the 4-value scale to directly assign the 21 competencies and the three performance indicators: 0 (total disagreement), 0.33 (more disagreement than agreement), 0.67 (more agreement than disagreement), and 1 (total agreement) [61]. Finally, each set’s assigned scores were normalized to obtain the final assignment [62]. Moreover, to avoid excluding a value of 0.5, this study assigned the data calibrated to “0.5” directly to “0.5001” [63].
Final   assignment = H x max 1 ≤ x ≤ 94 H x
Based on the above assignment, we consider the “combination effect” among the competencies, and explore the impact on the improvement of the cooperatives’ performance through internal interaction and joint action between different competency combinations.

4.2.2. Necessary Analysis

For fsQCA, the preconditions that necessarily lead to the result are necessary conditions. When the consistency is more significant than 0.9, it indicates that the condition is necessary for the result. In this study, we use fsQCA 3.0 software to test whether each precondition is necessary for high cooperative performance. The results are shown in Table 4, which shows that the consistency level of all conditions is less than 0.9, indicating that each precondition cannot significantly affect cooperative performance independently.

4.2.3. Sufficiency Analysis

Referring to the Ragin and Fiss-related literature [64,65], the case threshold is set to 1, and the consistency threshold is set to 0.8. Furthermore, we choose the intermediate solution as the final result [66]. The condition variable appears with ●; ⊗ indicates that the reason variable does not appear; the space indicates that the condition variable can appear or not appear. The results are shown in Table 5.
Depending on the specifics of the condition combination, the three competency combinations of the agricultural managers that produce high cooperative performances can be summarized.
(1)
Technical career agricultural managers. The pathway of configuration 1(C1) can explain approximately 26.6% of the cases, and the consistency level is 94.9%. C1 shows that a higher cooperative performance is mainly attributed to the higher knowledge and technology, and career orientation. These agricultural managers are usually engaged in agricultural production, rooted in rural areas, with skilled production techniques as their main advantage. They have high professionalism, strong career expectations, and clear career plans. The typical representative is Mr. Xiang, 51 years old, the manager of JH Tobacco Cooperative, with junior high school education. He has been long engaged in the planting industry, has rich experience in tobacco growing technology and with other crops, and was elected by the members to be the cooperative manager in 2012. Under the leadership of Mr. Xiang, JH cooperative gradually began to develop a mixed planting of tobacco and vegetables. In total, 1177 mu of growing tobacco and vegetables were grown together in the cooperative in 2020 (927 mu of “tobacco + bok choy” and 250 mu of “tobacco + cabbage”, one mu equals 0.165 acres), and 235 mu of tobacco and vegetable rotation (pumpkin 85 mu, sweet potato 150 mu) were produced. The mu production value of pumpkin is 3100 RMB, which has all been sold to local supermarkets at a price of 1 RMB/catty. Sweet potatoes are also all sold to vegetable dealers in Guangdong, the market demand exceeds supply. In 2020, the cooperative accumulated more than 2 million RMB of income from vegetable sales and achieved an income increase of more than 25,000 RMB per household for the members. The cooperative’s production demonstration is practical, and the farmers’ income increase is noticeable.
(2)
Managerial career agricultural managers. The two pathways of configuration for 2(C2) and configuration 3(C3) are similar. Together, they explain approximately 43.8% of the cases. C2 points out that higher personal capabilities and personality traits can produce a higher cooperative performance. This category of agricultural managers is highly educated and good at management. They can drive the performance of cooperatives by introducing new business models, innovating business categories, and improving management efficiency. Looking at the cases corresponding to this configuration, the typical representative is Mr. Liao, 30 years old, the manager of CJ Planting Cooperative, who graduated from a well-known university. Before joining the cooperative through an open competition in 2018, he mainly worked in sales. Among the “Online Stores” operated by Mr. Liao, 46 kinds of agricultural products have been recognized by the state as poverty alleviation products. In 2019 and 2020, the cooperative was selected as one of the “Top Ten Model Online Shops” by the county’s government for two consecutive years. In 2020, the cooperative achieved a total income of 8.22 million RMB and a surplus of 1.16 million RMB, with the obvious benefits of helping farmers increase their income.
Compared with C2, the agricultural managers of C3 mainly show the characteristics of agricultural industry talents, with industry management experience as their competitive advantage. C3 points out that higher personal capabilities, higher career orientation, and higher intrinsic drive can produce a higher cooperative performance. The principal delegate is Tang, 48 years old, the manager of the YZ Pig Breeding Cooperative, with a college degree. He was previously engaged in pig breeding management in a large agricultural company and was invited by a relative to join the cooperative. Because of his long-term experience in the pig breeding industry, Tang has a strong breeding management level and rich experience in swine fever prevention and control. After he entered the cooperative, he updated the feeding mode of the whole community, renovated the breeding isolation infrastructure, and improved the elimination loopholes. The survival rate and slaughter rate of pigs in the cooperative are much higher than the local average. In addition, due to the accumulation of contacts in the industry, Mr. Tang also brought stable sales channels to the cooperative. In 2020, the cooperative sold all the slaughtered pigs, achieving a cumulative surplus of 3.52 million RMB.
(3)
Integrated entrepreneurial agricultural managers. The pathway of configuration 4(C4) can explain approximately 46.3% of the cases, and the consistency level is 86.4%. C4 shows that a higher cooperative performance is mainly attributed to higher personal capabilities, career orientation, and intrinsic drive. Such agricultural managers love agriculture, are willing to be rooted in rural areas, have excellent agricultural production techniques and long-term experience in agricultural production, and have an advanced sense of innovation and modern business management talents. A typical representative is Mr. Liu, a 35-year-old manager of PY Organic Farm, with a master’s degree. Initially, Mr. Liu worked mainly in technical agricultural production at the farm. However, later, he took the lead in proposing the concept of food and agriculture education, and introduced a curriculum system that combines planting production with parent-child education. The curriculum system is novel and educational, and has been adopted by several local kindergartens. Liu also produced a provincial teaching project, made jointly with kindergartens. Since then, PY Organic Farm, based on farm production, has continued exploring other industrial integration road expansions. On the one hand, the opening of the farm WeChat, the Taobao online store, and large shopping malls have been advantageous for establishing stable supplies, sales partnerships, and the sale of high-quality organic vegetables. On the other hand, based on food and agriculture education, for undertaking parent–child team activities, orchard picking, the family vegetable garden, and other leisure activities, the farm has brought great added value.

4.2.4. Robustness Test

Robustness testing is an integral part of the QCA study. Scholars often examine the stability of the results by adjusting the threshold, consistency, measurement, and calibration criteria [61]. In this study, we increased the consistency threshold from 0.8 to 0.9 and the PRI consistency threshold from 0.7 to 0.75 for robustness testing. The results are shown in Table 6. The condition variable appears with ●; ⊗ indicates that the reason variable does not appear; the space indicates that the condition variable can appear or not appear. When the consistency threshold determination criterion was raised to 0.9, the results produced were consistent with those before the adjustment. When the PRI consistency determination criterion was raised to 0.75, the group states that yielded high cooperative performance were obtained. Among them, C5 was almost identical to C1. C6 indicates that high personal capabilities, career orientation, and intrinsic drive will produce high organizational performance. C6 is the overlapping result of C2 and C3, which can be categorized as managerial career agricultural managers. C7 indicates that high knowledge and technology, personal capabilities, career orientation, personality traits, and intrinsic drive will produce high performance, which is similar to C4 and can be categorized as an integrated entrepreneurial agricultural manager.

5. Conclusions

5.1. Main Findings

Based on the actual talent needs of the cooperative operation, this study combines Grounded Theory with fsQCA analysis, constructs an agricultural manager competency model, and explores the linkage effect of each competency combination on cooperative performance. The results demonstrate that, first, the Agricultural Manager Competency Model consists of five dimensions. According to the observability, they are divided into threshold competencies in the above-water part and differentiating competencies in the below-water part. Among them, the threshold competencies include two dimensions of knowledge and technology, and personal capabilities, and the differentiating competencies include the three dimensions of career orientation, personality traits, and intrinsic drive. Overall, none of the knowledge and technology, personal capabilities, career orientation, personality traits, and intrinsic drive competencies can determine cooperative performance, suggesting that single competencies do not constitute a decisive factor in influencing cooperative performance. Third, by analyzing the fsQCA results, four driving pathways were obtained that led to high levels of cooperatives’ performance. Moreover, the four pathways can be summarized into three agricultural manager types: technical career, managerial career, and integrated entrepreneurial types.

5.2. Theoretical Contributions

Existing studies have pointed out that employee competency is a vital factor affecting company performance. As managers are brought in from outside by cooperatives, agricultural managers’ competencies are bound to impact performance. Therefore, based on the competency perspective, this study analyzes the impact of the combined effect of the various competencies of agricultural managers on the cooperatives’ performance. The main theoretical contributions are as follows: first, the existing research on agricultural managers’ competency is in its initial stage; therefore, the industry still needs to form a systematic and comprehensive competency model. Based on a large amount of interview information and applying the Grounded Theory, we construct a competency model of agricultural managers, which enriches the research on management career competency and provides ideas for constructing industry generic-competency models. Second, the study considers the agricultural managers’ competencies as an important influencing factor on cooperative performance. The QCA is an innovative method, introduced to analyze the influence of the interaction of competencies on cooperative performance improvement. It enriches research methods and provides a new perspective on human capital issues in agricultural organizations. Third, this study uses a hybrid method combining Grounded Theory and QCA. In the interviews, a large amount of textual material, regarding theory and evidence, obtained by the researcher, could not be converted into fuzzy set scores by calibration [67]. These texts need to be coded in advance. Grounded Theory performs this precise job. It can code the competencies contained in the dialogues into various concepts and categories, complete the pre-calibration of data for QCA research, and achieve both complementary advantages [68].

5.3. Managerial Implications

The findings of this paper put forward four policy recommendations for the agricultural manager industry. First, the government needs to optimize the cultivation mechanism of agricultural managers, take the competency model as a critical basis for designing cultivation content, and establish a competency training system that meets the actual job requirements. The education can design training content in modules such as knowledge and technology, competence and literacy, professional ethics, and innovation and entrepreneurship. In line with the actual job responsibilities, the government and schools should explore the classification of training agricultural managers into various types, such as technical professionals, management professionals, and comprehensive entrepreneurs. Second, it is necessary to smooth the access channels for agricultural managers and take the competency model as an important reference for market competition standards. Combined with the actual situation of the position, the relevant departments should incorporate the competency model into the evaluation system of agricultural managers. Moreover, the competency model should be used as a “touchstone” to identify excellent agricultural managers to promote the sustainable development of the agricultural manager team. Third, cooperatives should pay attention to the impact of agricultural managers’ competence on performance, and improve the performance evaluation and incentive mechanism for agricultural managers. Meanwhile, cooperatives can motivate agricultural managers to improve their competencies to affect organizational performance. Cooperatives can link the agricultural managers’ competency evaluation to the distribution of benefits, thus forming a virtuous cycle of higher competency–higher performance–higher rewards. Fourth, the agricultural manager industry should take the competency model as an essential guide for industry planning, so that policy formulation can be more relevant to grassroots practice. Entirely playing the role of top-level design guidance, the government should establish agricultural managers’ market access, development support, work assessment, and other related policies to strengthen the foundation of rural talent in a targeted manner.

5.4. Limitations and Future Research

Several limitations are worth noting, which may provide ideas for the future research direction. First, specific potential competencies in the agricultural manager competency model may not have been explored. We could not fully consider all the competencies when conducting interviews. Future research can refine the agricultural manager competency model. Second, we chose to analyze the impact of agricultural managers’ competencies on the cooperatives’ performance by using the QCA method to obtain the most optimal competency portfolio. However, there needs to be a more in-depth analysis of the degree and path of each competency that affects performance. Scholars could choose different research methods, such as the structural equation modelling approach, for another perspective [69]. Third, the research data in this study are mainly from Hunan Province, China, which is limited by geographical scope. Future research can be further validated and compared through large-scale data collection.

Author Contributions

Conceptualization, X.Y. and W.L.; methodology, X.Y. and D.Z.; software, X.Y. and D.Z.; validation, X.Y., W.L., L.Q. and D.Z.; formal analysis, X.Y.; investigation, X.Y., D.Z. and W.L.; resources, X.Y., W.L., L.Q. and D.Z.; data curation, X.Y., W.L., L.Q. and D.Z.; writing—original draft preparation, X.Y.; writing—review and editing, W.L. and L.Q.; visualization, X.Y.; supervision, W.L. and L.Q.; project administration, W.L.; funding acquisition, W.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Hunan Social Science Fund Project (19YBA195) and the Postgraduate research and innovation project of Hunan Agricultural University (2022XC043).

Institutional Review Board Statement

Ethical review and approval were waived for this study, due to this study did not involve human clinical trials or animal experiments and all participation was voluntary. This research was approved by the college institution and was completed under the supervision of the college throughout. All subjects gave written informed consent in accordance with the Declaration of Helsinki. Respondents were assured of confidentiality and anonymity.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data in this study are collected from questionnaires.

Conflicts of Interest

The authors declare no conflict of interest.

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Sustainability 15 01263 g001 550
Figure 1. Analysis framework.
Figure 1. Analysis framework.
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Sustainability 15 01263 g002 550
Figure 2. Agricultural managers’ competency model.
Figure 2. Agricultural managers’ competency model.
Sustainability 15 01263 g002
Table
Table 1. Sample Description.
Table 1. Sample Description.
CharacteristicsIndicatorsGT IntervieweesQCA Subjects
NumberPercentageNumberPercentage
GenderMale2371.88%7882.98%
Female928.12%1617.02%
Age<30412.5%99.57%
30~40515.62%2627.66%
40~501237.5%3537.23%
>501134.38%2425.53%
EducationPrimary School and below721.88%1010.64%
Middle School825%1313.83%
High School515.62%2930.85%
Junior College515.62%1718.09%
Bachelor Degree and above721.88%2526.59%
JobAgricultural managers1237.5%94100%
Cooperative employees825%
Cooperative presidents825%
Government managers412.5%
Table
Table 2. Open Coding.
Table 2. Open Coding.
Interview RecordsConceptualizationInitial Category
In the beginning, I felt that I could not persevere, I did not think that the conditions would be so difficult, working in the sun every day. But then I got used to it, mainly because I made some achievements and did not want to give up.A12: Strong will powerDedicated and pragmatic
The first few years were so stressful that my hair turned white. When the swine fever came, all the pigs had to be disposed of, and it was useless to pull teeth; that was really a big loss. This year the situation is better, the price of pork has gone down again.A18: Mental toleranceStress resistance
You should also always cheer yourself up and tell yourself that you can do it.A74: Self-encouragement
Every year there will be some irregular training, it is quite useful. Many farmers follow us to attend the training and then come back to teach the techniques they have learned to other farmers.A7: Attend trainingCultivating farmers
He comes every few days when the chicks first arrive to help us keep an eye on them, and usually teaches us some new techniques.A44: Teaching technology
I sometimes fail to negotiate prices with suppliers and buyers, they also deliberately lower prices, and do not earn much money. There is no way, we have to sell it even at a lower price, or the cabbage will rot.A15: Negotiation skillsCommunication and negotiation ability
Many farmers are very bad at communication, but you have to reason with them, which is too much effort.A40: Communication skills
To raise pigs for a long time, technology is a key factor; we also have a lot of problems to figure out, now that pig farming is particularly demanding technology.A31: Breeding technologyAgricultural production technology
I am not worried about our chicken sales, mainly because I have contacts, specializing in supermarket suppliers.A82: Sales networkSocial network
Each decision must be carefully considered, especially in the prevention of African swine fever. With poor control, the region’s pigs will all die.A128: Decision-makingLeadership skills
We’d better establish a brand of our own. I’d like to make a parent–child educational farm where children can eat the vegetables they grow.A107: Established brandBrand strategy
We will also learn some of the social software that is popular among young people now. We often use WeChat to promote agricultural products.A55: Internet marketingInternet mindset
We are going to build a sunshine restaurant, more than 2000 square meters, with three-dimensional planting and a network unified control of nutrients, light, and temperature.A63: Smart agriculture
Table
Table 3. Axial Coding.
Table 3. Axial Coding.
Main CategoryInitial CategoryConceptualization (Frequency)
Knowledge and technologyB1: Agricultural policyNational policies (11), agricultural policies (10), agricultural insurance (10), agricultural laws (7)
B2: Agricultural production
technology		Planting experience (11), breeding techniques (7), epidemic prevention measures (3)
B3: Agribusiness skillsFinancial knowledge (7), agricultural products (4), business management (4)
Personal
capabilities		B4: Organize productionProduction scheduling (15), expansion of production (10), production supervision (10), personnel scheduling (8)
B5: Controlling cost and qualityCost reduction (9), reduction in working hours (8), quality monitoring (5), reduction in service prices (3)
B6: Sales capabilitySales channels (13), market demand (11), new markets (9), bargaining skills (7)
B7: Management capabilityTeam management (11), cooperation consciousness (9), financial management (6), job responsibilities (5)
B8: Leadership skillsDecision-making (13), leadership charisma (9), judgment (5), long-term planning (5)
Career
orientation		B9: Cultivating farmersTeaching technology (8), attend training (8), farmer participation in management (3)
B10: Learning capabilitiesVocational training (11), keeping learning (7), learning from experience (5)
B11: Communication and
negotiation ability		Negotiation skills (12), communication skills (11), relationship coordination (7)
B12: Social networkSales network (12), supplier network (6), financing sources (5)
Personality traitsB13: Dedicated and pragmaticMental tolerance (12), dedication (10), professional ethics (8)
B14: ResponsibilityResponsibility (12), contribution awareness (8), sense of service (6)
B15: Passion for agricultureBear hardship (11), enjoying rural life (8), returning home to start a business (5)
B16: Stress resistanceMental tolerance (9), self-encouragement (8), crisis management (8)
Intrinsic driveB17: Environmental consciousnessSustainable development (10), environmental protection (9), ecological civilization (3)
B18: Industrial consciousnessDiversified industries (7), industrial integration (6), deep processing (5)
B19: Brand strategyEstablished brand (8), brand marketing (7), promote brand (6)
B20: Internet mindsetInternet marketing (15), flow-line production (5), smart agriculture (3)
B21: Entrepreneurial capabilitiesNew products (8), business models (6), management innovation (5)
Table
Table 4. Necessity analysis of single variables.
Table 4. Necessity analysis of single variables.
Conditional VariableConsistencyCoverage
Knowledge and technology (KT)0.8330.722
Personal capabilities (PC)0.7840.749
Career orientation (CO)0.6420.814
Personality traits (PT)0.7610.695
Intrinsic drive (ID)0.7430.816
Table
Table 5. Results of configuration analysis.
Table 5. Results of configuration analysis.
Cause condition1234
Knowledge and technology (KT)●⊗ ●
Personal capabilities (PC) ●●●
Career orientation (CO)●⊗●●
Personality traits (PT)⊗●⊗
Intrinsic drive (ID)⊗⊗●●
Consistency0.9490.9480.9350.864
Original coverage0.2660.2240.2140.463
Unique coverage0.0790.0480.0080.224
Overall consistency0.881
Overall coverage0.619
Table
Table 6. Robustness test.
Table 6. Robustness test.
Cause Condition567
Knowledge and technology (KT)●⊗●
Personal capabilities (PC) ●●
Career orientation (CO)●●●
Personality traits (PT)⊗⊗●
Intrinsic drive (ID)⊗●●
Consistency0.9490.9610.87
Original coverage0.2660.1730.422
Unique coverage0.0960.0160.267
Overall consistency0.888
Overall coverage0.563
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Yu, X.; Liu, W.; Qing, L.; Zhang, D. Improving Farm Cooperatives’ Performance and Sustainability: A Study of Agricultural Managers’ Competencies Based on the Grounded Theory and the fsQCA Methods. Sustainability 2023, 15, 1263. https://doi.org/10.3390/su15021263

AMA Style

Yu X, Liu W, Qing L, Zhang D. Improving Farm Cooperatives’ Performance and Sustainability: A Study of Agricultural Managers’ Competencies Based on the Grounded Theory and the fsQCA Methods. Sustainability. 2023; 15(2):1263. https://doi.org/10.3390/su15021263

Chicago/Turabian Style

Yu, Xiyuan, Wenli Liu, Lingli Qing, and Di Zhang. 2023. "Improving Farm Cooperatives’ Performance and Sustainability: A Study of Agricultural Managers’ Competencies Based on the Grounded Theory and the fsQCA Methods" Sustainability 15, no. 2: 1263. https://doi.org/10.3390/su15021263

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