Abstract
Focal firms in the agri-food business must take strategic measures to ensure certain product and quality characteristics along the value chain. This is necessary in order to be able to keep up with competitors in the long term. Research shows that competitive parity, in addition to competitive advantage, should be considered as a strategic dimension. In the production of food, often several actors are involved and the focal firm (that is primarily responsible for the product) must organize the business relationships along the chain in such a way that the desired outcome is achieved in the short and long term. The achievement and safeguarding of competitive parity have been little researched so far. Up to now, there has only been a systematic literature review on this topic. Therefore, this aim of this research is to examine which factors influence the construct of competitive parity. Based on the findings of the literature review and further literature on the management of value chains, a theoretical framework is developed. A first empirical application of the model makes it possible to derive recommendations for action for focal firms in the agri-food industry. Therefore, we conduct a quantitative survey in the wine industry. PLS-SEM is used to analyze the model, using SmartPLS software. This research is the first to empirically investigate the strategic relevance of competitive parity in the agri-food business. The results show that chain management needs to pay attention to competitive parity besides the competitive advantage. Whereas the measures of cooperation have a stronger effect on achieving a competitive advantage, the measures of coordination have a stronger effect on achieving competitive parity. The constructs of power and trust — in contrast to the existing basis of resources and capabilities in the company — seem to have a significant influence on cooperation and coordination.
1. Introduction
The organic market grew to almost 15.9 billion euros in 2021, of which more than 60% is generated in the food retail sector (Schaack, 2021, 2022). In 2018, Lidl started a cooperation with Bioland (Lidl, 2020, 2022) and introduced Bioland products to its assortment for the first time. Previously, customers could only buy products that met the European Union’s minimum standard and carried the EU organic label. The management of Lidl saw an opportunity of gaining a competitive advantage, if they can offer a broad range of organic products meeting higher standards than the EU-standard of organic production. Thus, Lidl increased the pressure on other retailers to offer organic products that are higher than the EU-standard, too. This can be seen when taking a closer look at the strategic reactions of the management of Aldi, one of the major competitors of Lidl in the German food retail. Since 2023, Aldi and the Naturland growers association have been cooperating, which means that the discounter is now offering products from an organic association for the first time, too (Diemand, 2023).
The example shows not only that the supply of organic food is growing due to the increasing demand, but also that the behavior of one market participant or leading firm can put pressure on other market participants to follow-up and also to take strategic measures in order to survive in the competition in the long term. In the previous example, Lidl created a first mover advantage and, at the same time, one could say that the food retailer set a new industry standard for other retailers. The time it took Aldi to create a similar strategic partnership between Aldi and Naturland shows that a strategic approach is necessary to achieve competitive parity. In particular, when speaking about credence attributes (such as organic or sustainably produced) that are of major importance in food chains, chain management is a complex task that requires strategic measures. Although this is a new industry standard and one would assume that it would be easy to achieve, it is not a trivial, short-term matter, but requires strategic action on a full scale and over a long period of time. Focal firms need to deploy resources and capabilities in a purposeful way to ensure competitive parity in terms of certain product characteristics.
This practical example from the German agri-food business is in line with recent (more theoretical) research that shows that competitive parity, in addition to competitive advantage, should be considered as a strategic dimension (Richter and Hanf, 2023). As food is often produced in vertical coordinated value chains in which several actors are involved at different stages of the chain, the focal firm must manage the chain.
Only little research has been done on the construct of competitive parity in the context of strategic management and chain management. A systematic literature review has been conducted on this topic (Richter and Hanf, 2023). The resource-based view (RBV) builds the theoretical background for the review. Richter and Hanf (2023) have shown on a theoretical basis that competitive parity is of strategic importance. Based on this theoretical investigation, the following research questions arise: Is it empirically evident that competitive parity is of strategic importance? If so, do the measures taken differ between competitive parity and competitive advantage? The aim of this research is to examine which factors influence the construct of competitive parity.
This paper builds on and expands the theoretical framework developed by Richter and Hanf (2023). It goes more in depth regarding potential factors that influence the long-term compliance with product and quality criteria along the chain. Assumptions regarding the influence of each factor are made. As the research is exploratory in nature, partial least squares structure equation modelling (PLS-SEM) is used to evaluate the first application of the model. The empirical study is conducted in the wine industry which can be used as an example for other agri-food industries, as vertical coordination is increasing (Bitsch and Hanf, 2022; Richter et al., 2021) and, at the same time, consumer demands in wine are rising as well, for example regarding sustainability features (Wagner et al., 2023). From the results, implications for managers of focal firms can be derived. These are not only valid for the wine industry but can also be transferred to other agri-food industries.
This paper contributes to the research on the construct of competitive parity in the context of chain management. It is the first that investigates and shows the strategic relevance of competitive parity in the agri-food business empirically. It becomes clear that the individual factors have varying degrees of influence depending on the desired outcome. This indicates that a different strategy is required to achieve or maintain competitive parity.
The theoretical background and framework are briefly presented in Section 2. The methodology is explained in Section 3, including the operationalization of variables (Section 3.1) and the survey design of the quantitative survey (Section 3.2). In Section 4, the model assessment including the quality assessment of the outer and the inner model and results of the PLS-SEM are shown. In Section 5 follows the discussion of the results. Section 6 presents managerial implications. Section 7 closes with a conclusion including the contribution and limitations of this study.
2. Theoretical background and framework
2.1 Competitive parity from a resource-based perspective
A first systematic literature review by Richter and Hanf (2023) examines competitive parity at the firm-level (literature from the field of strategic management) and at the network-level (literature from the field of chain management). The authors’ aim was to systematically compile and present the current state of research on competitive parity. Surprisingly, in a period of more than 27 years, only 22 scientific articles could be found that mention the topic of competitive parity at firm-level as a secondary aspect in the literature on competitive advantage, and only two articles that take up competitive parity at network-level. This shows the strong exploratory character of the research.
The focus of the literature review was on the network-level. The prerequisite for this was the investigation at firm-level to find out whether competitive parity is a strategic dimension at all. Although very little literature is available, this could be shown (Richter and Hanf, 2023). Competitive parity should be addressed as part of strategic management to avoid a competitive disadvantage (see Rowe and Barnes, 1998). Richter and Hanf (2023) also consider competitive parity as intended strategic dimension, referring to the framework of intended, emergent and realized strategy of Mintzberg (1973). The construct is connected with strands of strategic action, e.g. study and imitation of sources of success of higher performing firms by lower performing firms (Barney et al., 2021), or being a late mover by purpose in order not to invest too many resources and capabilities as first movers have to (Richter and Hanf, 2023).
Competitive parity is based on resources that can be possessed and deployed by other firms as well, i.e. imitable resources (and capabilities). The outcome of competitive parity is normal or average and allows the firm to operate in the industry. Both dimensions, competitive parity and competitive advantage, are intended outcomes and strategic in nature; while competitive advantage serves to achieve above-average rents and maintain price premiums, parity is rather required to remain competitive and ensure a long-term survival in the market (Richter and Hanf, 2023).
Of course, it is possible that competitive parity may be achieved by chance, e.g. when a firm is striving to achieve competitive advantage but does not succeed. This can be the case if a focal firm wants to achieve a competitive advantage by securing a specific product attribute, but, as they offer their product, they recognize that other firms also provide it, so they realize that they are in par with competitors. However, focal firms also realize that it is necessary to fulfill some product or quality attributes to remain competitive, and they do need a long-term approach to achieve this. Parity is a strategic dimension as long-term measures and long-term planning is required if focal firms want to achieve competitive parity regarding specific product attributes by purpose.
Some of these findings overlap or originate from the field of research relating to the imitation of firms (see Barney, 1995; Barney et al., 2021; Bloodgood, 2019; Giachetti and Lanzolla, 2016; Lieberman and Asaba, 2006; Pacheco-de-Almeida and Zemsky, 2007). When competitors face a disadvantage they will attempt to imitate the resource or capability (Bloodgood, 2019). With regard to knowledge acquisition, it can create ‘parity by providing firms with similar resources […] that enable them to engage in similar knowledge application activities such as product and process enhancements and cost reduction efforts’ (Bloodgood, 2019).
On the network-level, several authors extend the operational understanding of parity of Mentzer et al. (2000) to a strategic understanding. Authors mainly distinguish between operative (or parity) and strategic (or advantage) chain management (Hanf and Hanf, 2007; Gagalyuk, 2012; Gagalyuk et al., 2013). Parity chain management can be applied with regard to specific product or quality attributes that must be fulfilled but do not serve to differentiate from the competition. In other words, it can be used to achieve parity with competitors in terms of product or quality attributes and at the same time achieve a cost advantage (Hanf and Hanf, 2007). In this context, maximizing efficiency and effectiveness to minimize costs is one of the main considerations (Hanf and Hanf, 2007). The focal firm (that is primarily responsible for the product) must develop and implement a collective strategy in order to achieve parity and advantage chain management and ensure the required product and quality attributes (Richter and Hanf, 2023). As different stages along the supply chain and large number of actors (network members) are involved, network management is a very comprehensive and difficult task. The focal firm must therefore find a balance between short-term and long-term goals, e.g. adapting to changing consumer demands and supporting consumers’ trust and the firm’s or network’s reputation, respectively. The focal firm must be aware that the coordination of the network requires (limited) resources and involves opportunity costs. The supply chain network needs to be managed accordingly.
Overall, competitive parity in chain management refers to the concept of achieving a level of competitiveness that is equivalent to that of competitors in the same industry or market. This concept is used in chain management to ensure that a company’s chain management practices and capabilities are at least as effective and efficient as those of its competitors. This can include the compliance with specific product and quality attributes (quality levels), but also other aspects where it is important to be as good as the industry average or competitors. These include, for example, building strong partnerships, technology, e.g. software usage and adoption, innovation, transportation, processing, warehousing, etc. However, in this article the focus is set on product and quality attributes. Achieving competitive parity is not a single exercise, but a continuous process. Managers must continuously identify areas for improvement and take measures to remain competitive. It is necessary to pursue a strategy to achieve and maintain competitive parity.
2.2 Establishing a framework of possible factors influencing the achievement of competitive parity
Gulati et al. (2005) have shown that two important means of managing vertical relationships to achieve the strategic goals of the focal firm are cooperation and coordination. The cooperation perspective mainly tackles the question what contributions are made and what outcomes are expected by business partners, the coordination perspective focuses on how interactions are organized by business partners (Gulati et al., 2012).
Potential factors influencing cooperation and coordination in business networks include the availability of resources and capabilities (based on the RBV and relational view). The resource endowment (or resource base), i.e. the access to resources and capabilities, is a key question when considering the strategic dimensions competitive parity and competitive advantage (Ambrosini and Bowman, 2009; Bowers et al., 2014; Helfat et al., 2007; Hull and Covin, 2010). Also, the availability of resources and capabilities has an influence on cooperation (alignment of interests) and on coordination (alignment of actions). The access to additional, valuable resources and capabilities may influence the decision of the management whether to cooperate or not. This is also true for knowledge and knowledge acquisition (Bloodgood, 2019).
Furthermore, power and trust mechanisms influence cooperation and coordination between business partners, e.g. the focal firm and network members of the supply chain network (Bachmann, 2001; Belaya and Hanf, 2013, 2012a, 2012b; Belaya et al., 2009; Bitsch and Hanf, 2022; Gagalyuk et al., 2013; Ireland and Webb, 2007; Priesmeyer et al., 2012; Thorelli, 1986; Xhoxhi et al., 2022). Last but not least governance influences cooperation and coordination (Williamson, 1979). Richter and Hanf (2023) assume based on the transaction cost perspective and vertical coordination, that it depends on the importance of the resource or capability for the focal firm if spot market transactions, vertical coordination (including long-term partnerships) or even vertical integration are applied. Also, in vertical coordination, different resources and capabilities are needed (i.e. the capability to monitor and control the contracted party) (Richter and Hanf, 2023).
Figure 1 illustrates the basic framework of competitive parity in strategic chain management of Richter and Hanf (2023). According to the figure, the aforementioned constructs power, trust, resources, capabilities and governance influence each the construct cooperation as well as the construct coordination. Cooperation (alignment of interests) and coordination (alignment of actions) influence (or build) strategic chain management, which, in turn, influences the intended competitive dimension: competitive advantage or competitive parity.
Framework of competitive parity in strategic chain management. Source: Richter and Hanf (2023).
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
As this work is placed in the context of vertical coordinated value chains, we leave out the construct ‘governance’ for the further analysis. Also, we remove the construct ‘(strategic) chain management’ from the model, as chain management is represented by the constructs cooperation and coordination. Based on chain management literature, it can be assumed that cooperation and coordination have a positive impact on the achievement of the respective targeted competition dimension at the network-level (competitive advantage and competitive parity) (H1, H2, H3, H4):
H1: The alignment of interests has a direct positive effect on the achievement of competitive advantage.
H2: The alignment of interests has a direct positive effect on the achievement of competitive parity.
H3: The alignment of actions has a direct positive effect on the achievement of competitive advantage.
H4: The alignment of actions has a direct positive effect on the achievement of competitive parity.
In addition, it can be deduced from the literature that the constructs of power and trust, as well as the availability of resources and capabilities, have a positive impact on the construct of cooperation (alignment of interests) (H5, H7, H9, H11):
H5: A higher degree of power exercised by the focal firm has a direct positive effect on the alignment of interests between business partners in the supply chain network.
H7: A higher degree of trust established by the focal firm has a direct positive effect on the alignment of interests between business partners in the supply chain network.
H9: The endowment of valuable, complementary resources among network members has a direct positive effect on cooperation (the alignment of interests).
H11: The endowment of valuable, complementary capabilities among network members has a direct positive effect on cooperation (the alignment of interests).
Based on the literature, the impact of power, trust, and the availability of resources and capabilities, also have a positive impact on the construct of coordination (alignment of actions) (H6, H8, H10, H12):
H6: A higher degree of power exercised by the focal firm has a direct positive effect on the alignment of actions between business partners in the supply chain network.
H8: A higher degree of trust established by the focal firm has a direct positive effect on the alignment of actions between business partners in the supply chain network.
H10: The endowment of valuable, complementary resources among network members has a direct positive effect on coordination (the alignment of actions).
H12: The endowment of valuable, complementary capabilities among network members has a direct positive effect on coordination (the alignment of actions).
From this, the model shown in Figure 2 can be derived. Figure 2 represents the model including the assumed influence on the competitive dimensions. It shows, how the single constructs are assumingly connected with each other. The model and underlying research have a strong exploratory character. To be able to investigate the influence (positive or negative) and strengths of influence that the constructs have in the model, the partial least squares structure equation modelling (PLS-SEM) is used.
Modelling the assumed influence on the competitive dimensions. Source: Own illustration.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
3. Methodology – Empirical study
PLS-SEM is adequate to investigate research with an exploratory character examining less developed or still developing theory (Hair et al., 2019a,b, 2021; Henseler, 2018). To be able to use the PLS-SEM technique, each variable used in the model needs to be operationalized (Hair et al., 2021; Sarstedt et al., 2016). The operationalization leads to items (or indicators) which represent single questions in the questionnaire. By using items, the latent constructs (variables) are made measurable.
3.1 Operationalization of variables
The variables used in the model are operationalized in the paragraphs below. Corresponding indicators or measurement variables were obtained from the literature of the resource-based view and relational view, but also from the literature on the management of value chains, strategic alliances and strategic partnerships.
Competitive advantage: The RBV is based on the assumption that companies differ in their performance as a result of their heterogeneity (Barney, 1991; Peteraf, 1993). From a resource-oriented perspective, internal variables in particular are decisive for the success of the company (Barney, 1991; Wernerfelt, 1984). Both the RBV and the knowledge-based view (as an extension of the RBV) argue that a competitive advantage can be achieved through the combination of resources and competencies that a firm possesses and deploys, and emphasize the importance of keeping resources, competencies and knowledge within the boundaries of the firm. Resources, competencies and knowledge can also extend beyond the boundaries of the firm. The relational view, which complements the RBV, integrates the perspective of resource-based theory and relational network theory to explain the competitive strategies of firms (Dyer and Singh, 1998). Accordingly, the sources of competitive advantage are based on the internal resources of a company and also on the external resources in the relational networks (Arya and Lin, 2007; Dyer and Singh, 1998; Lavie, 2006). Competitive advantage is reflected when an above-average profit is generated and the competitive position is improved (Barney, 1991, 1995; Barney et al., 2021; Porter, 1985). Rintamäki et al. (2007) argue that there is a link between customer value and competitive advantage. These two concepts are interrelated but independent streams of literature that can be used to identify customer value propositions (Rintamäki et al., 2007). While customer value is determined by the customer’s subjective perception and evaluation of the overall customer experience, competitive advantage is defined by the use of the company’s resources and capabilities to create customer value (Rintamäki et al., 2007).
Competitive parity: Achieving and maintaining competitive parity is reflected in various parameters according to the literature in the context of the resource-based view (Barney, 1995), VRIO concept and relational view (Dyer and Singh, 1998; Dyer et al., 2018), as well as the literature on business imitation (Giachetti and Lanzolla, 201; Lieberman and Asaba, 20066; Pacheco-de-Almeida and Zemsky, 2007). These include a conscious focus on avoiding competitive disadvantages; long-term planning to remain competitive; trying to anticipate competitor behavior and future customer demands to ensure that products have all the necessary characteristics (‘points of parity’); imitation of the resource base; imitation of corporate structures and processes; and neutralization of competitors’ competitive advantages.
Cooperation: In cooperation with business partners, challenges can arise when interests, values or goals differ (Hanf and Dautzenberg, 2006). Therefore, it should be investigated in which ways the cooperation is successful and common goals are achieved in the network. This includes the following indicators: sharing of sensitive information, existence of common values and norms, compliance with contractually or otherwise stipulated conditions, willingness to fulfill necessary tasks accordingly, commitment going beyond necessary tasks, ability to assess the behavior of business partners. (Belaya and Hanf, 2012a,b)
Coordination: In cooperation, coordination challenges can also arise if the individual activities of the business partners are not perfectly aligned with each other (Hanf and Dautzenberg, 2006). Therefore, the importance of the criteria for the coordination of the individual activities between the respondent firm and business partners will be queried via the following indicators: Similarities in organizational/company structures, knowledge of task distribution within the supply chain, knowledge of tasks and activities performed by business partners, synchronization of (logistics) processes, punctuality and completeness of deliveries and orders, responsiveness to external enquiries e.g. on product and process quality, transparency of business partners’ decision-making processes. (Belaya and Hanf, 2012a,b)
Power and trust: There are different ways to achieve compliance with agreements and arrangements. These include, for example, when supporting the business partners or making demands. The basis of trust between the business partners also plays an important role in the observance of agreements. In recent decades, research interest in the relational mechanisms of power and trust in inter-firm cooperation has increased (e.g. Belaya and Hanf, 2012a,b, 2013; Belaya et al., 2009; Brito and Miguel, 2017; Chae et al., 2017; de Vries et al., 2023; Ireland and Webb, 2007).
Ireland and Webb (2007) argue that the simultaneous application of power and trust is necessary for efficient and effective management of business relationships. In contrast, there are also scholars who see power and trust rather as complementary concepts (Bachmann, 2001; Belaya et al., 2009). As they substitute each other, one instrument can be used when the other does not achieve the desired result.
(a) Power: In general, ‘power […] refers to the ability, capacity or potential to get others to do something; to command, influence, determine or control the behaviors, intentions, decisions or actions of others in the pursuit of one’s own goals or interests against their will’ (Belaya and Hanf, 2012b). The construct of power can be operationalized via various indicators. These include: in general, influencing the decisions of business partners, coercive power and non-coercive power or influence strategies (French and Raven, 1959). Coercive power enables individuals to punish others; in chain management this can be the case if the network member does not comply with requirements of the focal firm. Coercive power includes warning of penalties or other sanctions in case of non-compliance with the agreement, as well as the threat of terminating the cooperation. Non-coercive power includes reward, expert, informational, legitimate1 and referent2 power (Belaya and Hanf, 2012b, 2013; French and Raven, 1959; Raven and Kruglanski, 1970). These include monetary and non-monetary incentives/rewards, solution-oriented support in case of problems, preferential treatment in future situations, communication of legal consequences in case of non-compliance with the agreement, communication of positive examples from cooperation with other business partners to increase identification and the desire to cooperate, access to information valuable to the business partner.
(b) Trust: Besides power, trust is a second key concept in managing relationships (e.g. Lumineau, 2017; Morgan and Hunt, 1994). According to Blomqvist (1997), trust means keeping agreements and arrangements and showing commitment to the business relationship. It also includes mutual trust that neither party will exploit vulnerabilities of the other. Trust is further subdivided into ‘contractual trust’, i.e. adherence to the ethical principles of inter-firm cooperation, such as honesty, integrity, fairness and respect, ‘competence trust’, i.e. trust in the technical competence as well as in the managerial competence of the business partner, and ‘goodwill trust’, i.e. trust in the renunciation of unfair advantage-taking as well as the willingness to take initiatives to exploit new opportunities beyond what has been explicitly promised. (Blomqvist, 1997; Sako, 1992).
Resources and capabilities: Based on the resource-based view (Barney, 1995; Barney et al., 2021), resources and capabilities available in a company or company network are decisive for a cooperation decision.
(a) Resources: The importance of the resource base should be interrogated via the following indicators: valuable or relevant complementary resources that business partners have, valuable or relevant missing resources that business partners have, and additional resources that business partners have that could be relevant in the future to meet customer needs.
(b) Capabilities: The importance of capabilities for the management of supply chain networks is to be queried via these indicators: Relevant complementary skills that business partners possess, relevant missing skills that business partners possess and special additional knowledge/skills that business partners possess.
All constructs are queried via reflective measurement models. An overview of the operationalization of variables can be found in Table A1 in the Appendix. The individual indicators were mapped as questions. Respondents rate the individual questions using 7-point Likert scales. This scale was used as previous surveys had already used it to examine similar constructs.
3.2 Survey design of the quantitative survey
The questionnaire is based on the operationalization of the variables. 14 pre-tests were conducted with focal firms. Participants were asked to comment on the format of the questionnaire, the wording, length and order of the questions. The feedback was then collected and the questionnaire was modified accordingly. This led to an improvement in the final questionnaire used for the survey. The final questions for each item (or indicator) can be found in Table A2 in the Appendix.3
The application of the model and first empirical study is done in the wine industry. Similar to other agri-food industries (Dongoski, 2019; Hanf and Kühl, 2005; Swinnen and Maertens, 2007), vertical coordination is both present and increasing in the (German) wine business (Bitsch and Hanf, 2022; Richter et al., 2021). This means that focal firms (either wine producers themselves, or in the case of private labels, the retailers) work together with network members in the supply chain network. Chain management is necessary to steer the business relationship. The overall wine quality is high and quality standards exist in the sector, thus it is difficult to differentiate via quality attributes (Richter and Hanf, 2020; Richter et al., 2022).
The online survey was conducted from 19 August 2022 to 19 September 2022 (a total of 32 days). For this purpose, 2400 wine producing companies from the countries Germany, Austria, Switzerland and Italy (South Tyrol) were contacted by email.4 As the questionnaire was in German, only companies in German-speaking countries were contacted. The addresses were obtained from different wine guides such as Eichelmann, Gault Millau and Falstaff. Therefore, we can assume that these are renowned wine producers who work vertically coordinated with other upstream and downstream companies and who do not want to risk their reputation.
Descriptive evaluation
A total of 310 participants took part in the survey and completed the questionnaire in full. This corresponds to a response rate of 12.9%. Various types of businesses were represented among the participants (note: multiple selection was possible!). The majority were wineries (274), in addition there were some cellars (43), sparkling wine cellars (24) and cooperatives (13) that took part in the survey. The majority of respondents came from Germany (67%), followed by Austria (20%), Italy/South Tyrol (7%) and Switzerland (6%). Companies of the most diverse sizes were represented. Just under 23% of the respondents said they marketed less than 50 000 bottles a year, 29% between 50 000–100 000 bottles, 19% between 100 000–200 000 bottles, 13% between 200 000–500 000 bottles, just under 5% 500 000–1 million bottles and about 10% over 1 million bottles. Slightly more than half of all companies stated that they regularly purchase grapes, must or wine.
Since 288 respondents see themselves as responsible for shaping the business relationship and stated that they maintain close cooperation with other companies along the value chain for several years, further analysis was limited to these cases. The reason for this is that the further questions are all aimed at strategic management in inter-firm partnerships and supply chain networks, i.e. it is necessary for the companies to fulfill these criteria.
4. Quality assessment of the model and results
The partial least squares (PLS) method of structural equation modelling (SEM) was applied to test the model using SmartPLS software 4. PLS-SEM is well suited to the exploratory nature of the research question (Hair et al., 2019b; Henseler, 2018) and has increasingly been used in business research, management research and other scientific fields such as agriculture (Sarstedt and Cheah, 2019; Hair et al., 2011). Also, the method is used regularly in agri-business research (e.g. Belaya and Hanf, 2012a,b; Gagalyuk and Hanf, 2011, 2013; Gagalyuk et al., 2013 Garbade et al., 2016; Gyau and Spiller, 2009; Jamai et al., 2022; Martínez-Filgueira et al., 2022; Otter et al., 2014; Tan and Chen, 2021).
The suitability of the measurement model in PLS is assessed in terms of the inner and outer models. The structural model characterizes the structural paths between the constructs, whereas the measurement models represent the relationships between the constructs and associated indicators (Sarstedt and Cheah, 2019).
4.1 Quality assessment of the measurement model (outer model)
The reflective measurement model is tested for reliability and validity. For this purpose, the measured values of the composite reliability or factor reliability, the indicator reliability and the average variance extracted (AVE) as well as the discriminant validity are used.
Indicator reliability expresses the proportion of variance that can be explained by the latent variable. According to Hair et al. (1998), an item is considered insignificant and removed from the model if its factor loading is less than 0.4. Values between 0.40 and 0.70 are within an acceptance range in which indicators are retained if they contribute to content validity (Hair et al., 2017). After removing the indicators with low indicator loadings, the remaining indicators all have factor loadings higher than 0.5 (see Table A3 in the Appendix).
The results of the evaluation of the measurement model are presented in Table A4 in the Appendix. The composite reliability (factor reliability) and Cronbach’s alpha are measures of internal consistency and should not be lower than 0.6. In our case, the Cronbach’s alpha values of all variables are within the recommended limits. The composite reliability is also greater than 0.6 for all variables. According to Gyau and Spiller (2009), the homogeneity criterion is above 0.7, i.e. the values should be above 0.7. Thus, in both cases the value for the variable competitive advantage would be undercut. However, it is argued that the composite reliability index is more reliable in assessing convergent validity because it takes into account the relative weighting of the different indicators in a latent construct, whereas the Cronbach’s alpha assumes equal weighting (Gyau and Spiller, 2009). In exploratory research projects, values between 0.60 to 0.70 are acceptable (Hair et al., 2017). Cronbach’s alpha is considered the lower bound and the composite reliability the upper bound of the internal consistency reliability (Hair et al., 2017). For the variable competitive advantage, the Cronbach’s alpha is 0.628, which is acceptable.
The average variance observed (AVE) should be higher than 0.5 (Hair et al., 2017). The AVE value means that a latent variable is able to explain on average more than half of the variance of its indicators. The AVE value for the constructs power and competitive parity are slightly lower, at 0.447 and 0.475, respectively.
The assessment of discriminant validity is intended to ensure that a reflective construct has the strongest relationships to its own indicators (e.g. compared to all other constructs) in the PLS-SEM model (Hair et al., 2022). For the discriminant validity criterion, it is recommended to focus on the heterotrait-monotrait criterion (HTMT) (Hair et al., 2017, 2019a). The 95% confidence interval should not contain the value 1.0 in the HTMT statistic for any of the construct combinations (Hair et al., 2017). If the HTMT value is below 0.90, discriminant validity between two reflective constructs is given (Ringle et al., 2022). In our model, discriminant validity is met at the 95% confidence interval for all construct combinations.
4.2 Quality assessment of the structural model (inner model)
For the evaluations of the inner model, we follow the procedure for the evaluation of the structural model according to Hair et al. (2017). After testing the collinearity between the constructs using the variance inflation factor (VIF), the path coefficients (significance and relevance) in the structural model are tested. This is followed by an examination of the prediction quality with the coefficient of determination (R²) and the f² effect sizes.
Assessment of collinearity between constructs using the variance inflation factor (VIF)
If the VIF is greater than or equal to 5, there is a potential collinearity problem. Therefore, the VIF should be less than 5. Otherwise, the constructs should be eliminated or combined into new constructs. Checking the VIF values in our model shows that there is no collinearity between the constructs.
Assessing the relationship between constructs with the path coefficients
The path coefficients represent the theoretically assumed relationships between the constructs. They are standardized and usually lie between -1 (strong negative relationship) and +1 (strong positive relationship). Very low values (close to 0) are usually not significant (Hair et al., 2017). Values greater than 0.1 are acceptable, or as a more conservative measure, values greater than 0.2 are considered acceptable (Marcoulides, 2014).
The significance of the path coefficients is tested using bootstrapping (with 5,000 replicate samples). If the empirical t-value exceeds a critical value, it can be concluded that the coefficient is statistically significant at a certain probability of error (the significance level). The larger the t-value, the stronger the argument against the null hypothesis (Backhaus et al., 2018). The p-values should be lower than 0.05 (significance level of 5%). The bootstrap confidence intervals also allow a test of whether a path coefficient deviates significantly from 0. The determination of these confidence intervals is based on the standard errors determined via bootstrapping. If a confidence interval for an estimated path coefficient does not contain the value 0, the hypothesis that the path is equal to 0 can be rejected and thus a significant effect can be assumed (Hair et al., 2017). The results are presented in Table 1. It shows that 7 out of 12 hypotheses formulated were supported, including H1, H2, H4, H5, H6, H7 and H8.
Results of the structural model.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
Evaluation of the forecast quality with the coefficient of determination (R²)
The R² measures the variance explained in each of the endogenous constructs and is therefore a measure of the explanatory power of the model (Hair et al., 2019a; Shmueli and Koppius, 2011). The R² value lies in a value range between 0 and 1, with higher values indicating better forecast performance or greater explanatory power (Hair et al., 2017, 2019a). It is important to note that the R² is a function of the number of predictor constructs, in other words, the greater the number of predictor constructs, the higher the R² (Hair et al., 2019a). For that reason, the R² should be interpreted in relation to the context of the study, based on the R² values from related studies and models of similar complexity (Hair et al., 2019a). In business research, R² values of the endogenous constructs of greater than or equal to 0.67, 0.33 and 0.19 can be considered substantial, moderate and weak, respectively (Chin, 2014). In the model, the constructs cooperation and coordination have R² values of 0.326 (weak) and 0.339 (moderate), respectively. The construct competitive parity has an R² value of 0.229 (weak). The R² value of the construct competitive advantage, at 0.100, even falls below the threshold value for a weak assessment. It means that only 10% of the construct competitive advantage is explained by the predictor constructs (COOP and COOR). The rather moderate and weak R² values of the endogenous latent variables reveal the complexity of the research model and the low number of predictor constructs. Nevertheless, the forecasting quality of the explorative model can be classified as moderate.
Testing the f² effect sizes
The f² effect sizes are used to test whether an exogenous construct exerts a substantial influence on an endogenous construct. Values of 0.02 and above indicate small effects, 0.15 and above indicate medium effects, and 0.35 and above indicate large effects (Weiber and Sarstedt, 2021). According to the analysis, there is no effect for some of the relationships between constructs, these include RESOURCES → COOP, RESOURCES → COOR, CAPABILITIES → COOP and CAPABILITIES → COOR. The results show that the previous mentioned exogenous constructs (RESOURCES and CAPABILITIES) to not exert a substantial influence on the endogenous constructs (COOP and COOR). This is consistent with the result of the significance test of the individual path coefficients.
5. Discussion of results
The interpretation of the results of the model assessment is carried out in the context of the primary research questions, which have a strongly exploratory character. (1) The basic question we want to answer by testing the structural equation model is whether competitive parity plays a role at all in the strategic context of chain management. (2) The unit of study is the focal firm of a value chain network. Therefore, it will also be investigated whether measures of cooperation and coordination are taken to achieve competitive parity. (3) Furthermore, it should be investigated which influencing factors have an effect on the construct of competitive parity. For this purpose, the path coefficients and their significance will be examined more closely.
Re (1): The R² value of 0.229 of the construct competitive parity is to be classified as weak. In fact, only 22.9% of the construct competitive parity is explained by the predictor constructs (COOP and COOR). Maybe this is partly due to the fact that the management of focal firms (survey participants) is maybe only subconsciously aware of competitive parity. The value shows, however, that this construct should be taken into account in strategic management.
Re (2): The positive effect of cooperation (COOP → PAR) and coordination (COOR → PAR) on competitive parity are significant at a significance level of 1%. From this it can be deduced that cooperation and coordination measures are taken when managing the business network in order to achieve or ensure competitive parity. When looking at the path coefficients and their significance, it is interesting to note that the construct cooperation has a stronger effect on achieving a competitive advantage (
Re (3): The analysis of the path coefficients of the structural equation model in our sample shows that all assumed signs of the relationship between the constructs correspond to the direction of the path coefficients. Seven of the assumptions made are significant at a 5% level of significance. That is, there is a 5% probability that the null hypothesis was falsely rejected. Five of the assumptions made are not significant at the 5% level of significance. These include the positive effect of coordination on achieving a competitive advantage (COOR → ADV), the positive relationship between resources and cooperation (RESOURCES → COOP), as well as the positive relationship between resources and coordination (RESOURCES → COOR), and the positive relationship between capabilities and the construct cooperation (CAPABILITIES → COOP) and the construct coordination (CAPABILITIES → COOR). According to this, the influence of resources and capabilities, which is present in the focal firm, is positive, but not significant. This is surprising, since a clear connection can be assumed from the literature in the area of RBV and the relational view. However, maybe this also indicates that one does not need those amounts of resources and capabilities to achieve competitive parity, compared to the amount one would need to gain and sustain a competitive advantage. Or, maybe it is not about the amount of resources and capabilities, but about a different kind of resources and capabilities that is needed to achieve competitive parity. Another reason might be that the respondents in this survey were only subconsciously aware about the resources and capabilities, as the questionnaire’s setting was already one of vertical coordination.
When looking at the power and trust mechanisms, a significant positive influence can be found. The positive influence of trust on cooperation (TRUST → COOP) (
6. Managerial implications
The results of our research open the floor for managerial implications. The results show that not only gaining a competitive advantage is vital to survive in the market, but the management of focal firms should develop and pursue a strategy to reach competitive parity. The strategic dimensions competitive advantage and competitive parity must be addressed simultaneously because considering only the achievement of a competitive advantage by itself does not guarantee to stay competitive while concentrating on the achievement of competitive parity alone may lead to failure in the long run as well.
The example by Lidl and Aldi (see Introduction) shows, that sometimes, one focal firm has the initial idea and builds a strategy to achieve a competitive advantage or a first mover advantage. However, follow-up firms (or second movers) will react strategically and use long-term measures to achieve competitive parity.
As in agri-food chains more than one actor is involved in the production and marketing of food products and focal firms work with other firms in the supply chain network, building and maintaining (or even nurturing) strategic partnerships is of major importance. Lidl, for example, invested in building a partnership with Bioland. Then, Aldi started to establish a partnership with another organic grower’s association, Naturland, to be able to keep up with Lidl. However, it took Aldi roughly 4–5 years to achieve competitive parity with Lidl.
This example clearly shows that management of Aldi does focus on competitive parity. Making Naturland certified agri-food products available in their stores, causes the situation that Aldi is as competitive as Lidl offering Bioland certified products. Does the management try to maintain their organic business segment to achieve competitive advantage or see their engagement in organic business as an instrument for restoration of competitive advantage? Maybe this is the case. As Richter and Hanf (2023) described in their article, there is a continuum from competitive disadvantage over competitive parity to competitive advantage. All nuances (or points on the continuum) exist and it is certainly possible that the perspectives sometimes overlap. Thus, there might be a gap between the managerial and the theoretical perspective.
The management of focal firms should increasingly use mechanisms that strengthen the basis of trust with the business partner in order to achieve the best possible results of cooperation and coordination to achieve competitive parity. As trust can be divided into contractual, goodwill and competence trust, various measures should be chosen to tackle the different trust dimensions. To increase trust in terms of managerial and technical competence, regular meetings can help to share information with the business partner about the capabilities of the firm’s management and staff. As explained before, contractual trust contains adherence to the ethical principles of inter-firm cooperation, such as honesty, integrity, fairness and respect. To strengthen this contractual trust between business partners, we suggest that open, honest, and regular communication is needed between the business partners.
However, power mechanisms should also be considered, especially with regard to the alignment of actions (coordination). In order to manage supply chain networks successfully the knowledge of different power mechanisms is essential. Depending on the origin of the power mechanism, different effects on cooperation and coordination are possible. They can destroy a strategic partnership or even help solve cooperation or coordination problems. As power has a significant influence on coordination, it is more likely that power mechanisms have an effect on the alignment of actions than on the alignment of interests. Knowledge of these effects should be used wisely for effective management of supply chain networks. It is always important to keep in mind that power is not only a negative tool, but has similar characteristics as trust mechanisms. It does not only include monetary and non-monetary incentives/rewards but also, for example, solution-oriented support by the focal firm or preferential treatment in the future.
In addition, emphasis should be placed on coordination mechanisms to align the actions of the network members when the focal firm takes measures to achieve and maintain competitive parity.
Even though the influence of resources and capabilities was not significant in our model, they must be deployed in a targeted manner. Resources and capabilities are scarce. Perhaps it is because it is natural for entrepreneurs to target resources and capabilities, and much is done subconsciously, so the results do not show a clear influence of these constructs. In terms of capabilities, focal firms should be aware of the capabilities that are needed to manage supply chain networks successfully. In general, it is of major importance, that managers allocate resources and capabilities that the firm possesses efficiently and effectively. Also, they need to have knowledge of which resources and capabilities are needed for certain outcomes.
It is important to understand the role the competitive parity idea can play in enriching our view of known concepts in the field of strategic management, such as Porter’s generic strategies (cost leadership, differentiation, cost focus or differentiation focus), transaction costs etc.
Focal firms seek to achieve competitive advantage by identifying customer value propositions to differentiate themselves from competitors, which should lead to competitive advantage. Competitive advantage is defined by the use of the company’s resources and capabilities to create customer value. Second movers or late movers imitate the resource base and try to create the same customer value. If they succeed, competitive parity is created. Focal firms strive for competitive parity in terms of specific product attributes, but also in terms of social values or standards (e.g. sustainability) that they must adhere to in order to avoid reputational damage. This is not just about cost advantages; it is also about the management trying to allocate resources efficiently and not investing too much or too little resources and capabilities. Competitive advantages can be achieved by means of cost leadership or differentiation. Competitive parity can be achieved through cost management and, at the same time, the achievement of similar product characteristics with which other market participants (competitors) attempt to differentiate while using as few resources and capabilities as possible.
Based on the transaction cost perspective and vertical coordination, we can assume that as the resource or capability is critical for the firm to gain a competitive advantage, the more likely the firm is to operate in a vertically integrated manner and to produce the product itself. If competitive parity is the desired outcome and the attribute is critical because the reputation of the focal firm can be strongly influenced, the chosen governance mechanism will be a hybrid form where the buyer (the focal firm) can still influence product quality through contracts, incentives and control mechanisms. In hybrid forms, other resources are required (e.g. capability to monitor and control). As for basic product characteristics, it is sufficient to buy the product on the spot market. To summarize, high product quality tends to require vertical integration, medium to high product quality requires vertical coordination (especially when reliability and availability are crucial), and low to medium product requirements require spot market procurement.
7. Conclusion
The aim of this research was to examine which factors influence the construct of competitive parity. The model was tested using the PLS technique, which does not only assess the structural model (the assumed causal connection between the dependent and independent constructs), but also evaluates the measurement model (loadings of indicators on the latent constructs). The technique was chosen due to its suitability for prediction and theory building. Given that competitive parity is a new latent construct that has been included in our analysis and the effects of cooperation and coordination on this construct have not been tested up to now, PLS seems to be an appropriate tool. Furthermore, the constructs included in the model have never been analyzed simultaneously. The PLS approach allows such an analysis and makes it possible to draw conclusions about the model as a whole.
From the analysis of the structural equation model it became clear: competitive parity plays a role in the strategic management of firms working together in value chains and should also find its own place in the literature on strategic management and chain management. In order to achieve and ensure competitive parity, managers of focal firms need to be aware of measures of cooperation and coordination based on trust or power so that they can consciously use them to manage their business networks. They should also be aware of the different strengths of influence of the respective factors in order to develop adequate mechanisms in dealing with business partners and to remain competitive in the long term.
For the first application of the model, wine producers from Germany, Austria, Switzerland and South Tyrol were interviewed. Due to the similarity in vertical coordination between the wine sector and other agri-food sectors, the results, however, are not industry-specific; the tendency regarding the general importance of competitive parity in chain management and influence of the single factors should be similar in other agri-food industries. Whereas the measures of cooperation have a stronger effect on achieving a competitive advantage, the measures of coordination have a stronger effect on achieving competitive parity. The constructs of power and trust — in contrast to the existing basis of resources and capabilities in the company — seem to have a significant influence on cooperation and coordination.
This paper contributes to the research on the construct of competitive parity in the context of chain management. It is the first that investigates the strategic relevance of competitive parity in the agri-food business empirically. There are several factors influencing the achievement of competitive parity. This research also indicates that a different strategy is required to achieve or maintain competitive parity than to achieve a competitive advantage.
The study has several limitations. First, the empirical study is focused on the wine sector and has been conducted in German speaking countries or regions only. However, in terms of vertical coordination or in other words, with regard to supply chain networks, the structures seem to be similar to other (European) agri-food industries. A high level of customer involvement (process knowledge) is required, as sector-dependent questions are asked. The results achieved are therefore customer-dependent, as competitive advantages or competitive parity arise from the customer’s perspective. Despite the operationalization of the questions in the wine sector and the focus on the German-speaking area (which contributes to a better understanding of the questions), the basic idea and the model itself can also be transferred to other sectors in the agri-food business. Second, the R² value of the construct competitive advantage falls below the threshold value for a weak assessment and the R² value of 0.229 of the construct competitive parity is weak, which means the construct is not explained very well by the predictor constructs (COOP and COOR) in the structure equation model. Overall, the rather moderate and weak R² values of the endogenous latent variables demonstrate the complexity of the research model and the low number of predictor constructs. Nevertheless, the forecasting quality of the explorative model can be classified as moderate. Third, this research has focused on the focal firm (network leader). By including perspectives of other network members, the results with regard to competitive parity may be different because questions might be rated differently. Within the scope of this study, which focuses on the effect that competitive parity has on strategic chain management, it was important, though, to limit the group of respondents to the management of focal firms. Lastly, there might be a social desirability bias, as questions regarding power and trust mechanisms have been posed in the questionnaire.
In future research, after having transferred the sector-dependent questions to the respective sector, the model should be applied to other agri-food industries in order to investigate whether the importance of the individual factors is distributed differently. For this purpose, the same overall structure of the questionnaire should be used that was used for the survey in the wine industry. In a next step, the results should be compared with each other. In addition, the influence of the respective items on the latent constructs can be explored and evaluated more deeply.
Acknowledgements
This article was written as part of the doctoral dissertation of Barbara Richter. The doctorate is carried out at Hochschule Geisenheim University and supported by a member of the Faculty of Agriculture, Rheinische Friedrich-Wilhelms-Universität Bonn. The research on which this article is based, was supported by a scholarship of the Hanns Seidel Foundation with funds from the Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung, BMBF). We acknowledge the support by the Open Access Publishing Fund of Geisenheim University.
References
Ambrosini, V. and C. Bowman. 2009. What are dynamic capabilities and are they a useful construct in strategic management? International Journal of Management Reviews 11 (1): 29–49. https://doi.org/10.1111/j.1468-2370.2008.00251.x
Arya, B. and Z. Lin. 2007. Understanding collaboration outcomes from an extended resource – based view perspective: the roles of organizational characteristics, partner attributes, and network structures. Journal of Management 33 (5): 697–723. https://doi.org/10.1177/0149206307305561
Bachmann, R. 2001. Trust, Power and control in trans–organizational relations. Organization Studies 22 (2): 337–365. https://doi.org/10.1177/0170840601222007
Backhaus, K., B. Erichson, W. Plinke and R. Weiber|. 2018. Multivariate Analysemethoden. Eine anwendungsorientierte Einführung. Springer, Berlin.
Barney, J. 1991. Firm resources and sustained competitive advantage. Journal of Management 17 (1): 99–120. https://doi.org/10.1177/014920639101700108
Barney, J.B. 1995. Looking inside for competitive advantage. Academy of Management Executive 9 (4): 49–61.
Barney, J.B., D.J. Ketchen and M. Wright. 2021. Resource-based theory and the value creation framework. Journal of Management 47 (7): 1936–1955. https://doi.org/10.1177/01492063211021655
Belaya, V. and J.H. Hanf. 2012a. Managing Russian agri-food supply chain networks with power. Journal on Chain and Network Science 12 (3): 215–230. https://doi.org/10.3920/JCNS2012.x217
Belaya, V. and J.H. Hanf. 2012b. Power and Supply Chain Management — Insights from Russia. In: A. Balmann and L. Arens (eds.) Unternehmerische Landwirtschaft zwischen Marktanforderungen und gesellschaftlichen Erwartungen, 51. Jahrestagung der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V. vom 28. bis 30. September. Landwirtschaftsverlag, Münster, pp. 235–247.
Belaya, V. and J.H. Hanf. 2013. Using power in business relationships — old wine in new skins? Management & Marketing Challenges for the Knowledge Society 8 (1): 3–24.
Belaya, V., T. Gagalyuk and J. Hanf. 2009. Measuring Asymmetrical Power Distribution in Supply Chain Networks: What Is the Appropriate Method? Journal of Relationship Marketing 8 (2): 165–193. https://doi.org/10.1080/15332660902876927
Bitsch, L. and J.H. Hanf. 2022. The perfect match: interpersonal relationships and their impact on chain management. International Food and Agribusiness Management Review 25 (3): 489–508. https://doi.org/10.22434/IFAMR2020.0204
Blomqvist, K. 1997. The many faces of trust. Scandinavian Journal of Managment 13 (3): 271–286.
Bloodgood, J.M. 2019. Knowledge acquisition and firm competitiveness: the role of complements and knowledge source. Journal of Knowledge Management 23 (1): 46–66. https://doi.org/10.1108/JKM-09-2017-0430.
Bowers, A.H., H.R. Greve, H. Mitsuhashi and J.A.C. Baum. 2014. Competitive parity, status disparity, and mutual forbearance: securities analysts’ competition for investor attention. Academy of Management Journal 57 (1): 38–62. https://doi.org/10.5465/amj.2011.0818
Brito, R.P. and P.L.S. Miguel. 2017. Power, Governance, and Value in Collaboration: Differences between Buyer and Supplier Perspectives. Journal of Supply Chain Management 53 (2): 61–87. https://doi.org/10.1111/jscm.12134
Chae, S., T.Y. Choi and D. Hur. 2017. Buyer Power and supplier relationship commitment: a cognitive evaluation theory perspective. Journal of Supply Chain Management 53 (2): 39–60. https://doi.org/10.1111/jscm.12138
Chin, W.W. 2014. The partial least squares approach to structural equation modeling. In: G.A. Marcoulides (ed.) Modern methods for business research. Psychology Press, London, pp. 295–336.
de Vries, J.R., J.A. Turner, S. Finlay–Smits, A. Ryan and L. Klerkx. 2023. Trust in agri–food value chains: a systematic review. International Food and Agribusiness Management Review 26 (2): 175–197. https://doi.org/10.22434/IFAMR2022.0032
Diemand, S. 2023. Ökologische Lebensmittel: Bio boomt nicht mehr. Frankfurter Allgemeine Zeitung (FAZ). Available online at https://www.faz.net/aktuell/wirtschaft/unternehmen/lebensmittel-bio-boomt-nicht-mehr-18617226.html
Dongoski, R. 2019. How vertical integration is impacting food and agribusiness. Available online at https://www.ey.com/en_us/consumer-products-retail/how-vertical-integration-is-impacting-food-and-agribusiness.
Dyer, J.H. and H. Singh. 1998. The Relational view: cooperative strategy and sources of interorganizational competitive advantage. The Academy of Management Review 23 (4): 660–679. https://doi.org/10.2307/259056
Dyer, J.H., H. Singh and W.S. Hesterly. 2018. The relational view revisited: A dynamic perspective on value creation and value capture. Strategic Management Journal 39 (12): 3140–3162. https://doi.org/10.1002/smj.2785
French, J.R. and B.H. Raven. 1959. The bases of social power. In: D. Cartwright (ed.) Studies in social power. Research Center for Group Dynamics, Institute for Social Research, University of Michigan, Ann Arbor, MI, pp. 150–167.
Gagalyuk, T. 2012. Goal achievement in supply chain networks. A study of the Ukrainian agri – food business. Zugl.: Halle, Univ., Naturwissenschaftliche Fakultät III, Disseration, 2011. Universitäts– und Landesbibliothek Sachsen–Anhalt; IAMO, Halle (Saale).
Gagalyuk, T. and J.H. Hanf. 2011. Success for the whole food chain: testing the model of network success in Ukraine. In: P. Weingarten, M. Banse, H. Gömann, F. Isermeyer, H. Nieberg, F. Offermann and H. Wendt (eds.) Möglichkeiten und Grenzen der wissenschaftlichen Politikanalyse. 50. Jahrestagung der Gesellschaft für Wirtschafts– und Sozialwissenschaften des Landbaues e. V. vom 29. September bis 1. Oktober 2010. Landwirtschaftsverlag, Münster, pp. 123–134.
Gagalyuk, T. and J.H. Hanf. 2013. Networks and their goals: implications for strategic chain management. In: T. Ehrmann, J. Windsperger, G. Cliquet and G. Hendrikse (eds.) Network Governance. Springer, Berlin, pp. 107–123.
Gagalyuk, T., J.H. Hanf and M. Hingley. 2013. Firm and whole chain success: network management in the Ukrainian food industry. Journal on Chain and Network Science 13 (1): 47–70. https://doi.org/10.3920/JCNS2013.x226
Garbade, P., S. Omta and F. Fortuin. 2016. The interplay of structural and relational governance in innovation alliances. Journal on Chain and Network Science 16 (2): 117–134. https://doi.org/10.3920/JCNS2014.x016
Giachetti, C. and G. Lanzolla. 2016. Product technology imitation over the product diffusion cycle: which companies and product innovations do competitors imitate more quickly? Long Range Planning 49 (2): 250–264. https://doi.org/10.1016/j.lrp.2015.05.001
Gulati, R., P.R. Lawrence and P. Puranam. 2005. Adaptation in vertical relationships: beyond incentive conflict. Strategic Management Journal 26 (5): 415–440.
Gulati, R., F. Wohlgezogen and P. Zhelyazkov. 2012. The Two Facets of Collaboration: Cooperation and Coordination in Strategic Alliances. Academy of Management Annals 6 (1): 531–583. https://doi.org/10.5465/19416520.2012.691646
Gyau, A. and A. Spiller. 2009. An integrated model of buyer–seller relationship performance in agribusiness: the partial least squares approach. Journal on Chain and Network Science 9 (1): 25–41. https://doi.org/10.3920/JCNS2009.x145
Hair, J.F., W.C. Black, B.J. Babin, Anderson R.E. and R.L. Tatham. 1998. Multivariate data analysis. Prentice Hall, Upper Saddle River, NJ.
Hair, J.F., C.M. Ringle and M. Sarstedt. 2011. PLS–SEM: indeed a silver bullet. Journal of Marketing Theory and Practice 19 (2): 139–152. https://doi.org/10.2753/MTP1069-6679190202
Hair, J.F., G.T.M. Hult, C.M. Ringle, M. Sarstedt, N.F. Richter and S. Hauff. 2017. Partial Least Squares Strukturgleichungsmodellierung. Eine anwendungsorientierte Einführung. Verlag Franz Vahlen, München.
Hair, J.F., J.J. Risher, M. Sarstedt and C.M. Ringle. 2019a. When to use and how to report the results of PLS–SEM. European Business Review 31 (1): 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair, J.F., M. Sarstedt and C.M. Ringle. 2019b. Rethinking some of the rethinking of partial least squares. European Journal of Marketing 53 (4): 566–584. https://doi.org/10.1108/EJM-10-2018-0665
Hair, J.F., G.T.M. Hult, C.M. Ringle, M. Sarstedt, N.P. Danks and S. Ray. 2021. Partial Least Squares Structural Equation Modeling (PLS–SEM) Using R. Springer International Publishing, Cham.
Hair, J.F., G.T.M. Hult, C.M. Ringle and M. Sarstedt. 2022. A primer on partial least squares structural equation modeling (PLS–SEM). SAGE, Los Angeles, CA.
Hanf, J. and K. Dautzenberg. 2006. A theoretical framework of chain management. Journal on Chain and Network Science 6 (2): 79–94. https://doi.org/10.3920/JCNS2006.x067
Hanf, J. and C.-H. Hanf. 2007. Does food quality management create a competitive advantage? In: L. Theuvsen, A. Spiller, M. Peupert and G. Jahn (eds.) Quality management in food chains. Wageningen Academic Publishers, Wageningen, pp. 489–502.
Hanf, J.H. and R. Kühl. 2005. Branding and its consequences for German agribusiness. Agribusiness: An International Journal 21 (2): 177–189. https://doi.org/10.1002/agr.20042
Helfat, C.E., S. Finkelstein, W. Mitchell, M.A. Peteraf, H. Singh, D.J. Teece and S.G. Winter. 2007. Dynamic capabilities. Understanding strategic change in organizations. Blackwell, Malden, MA.
Henseler, J. 2018. Partial least squares path modeling: Quo vadis? Quality & Quantity 52 (1): 1–8. https://doi.org/10.1007/s11135-018-0689-6.
Hull, C.E. and J.G. Covin. 2010. Learning Capability, Technological Parity, and Innovation Mode Use*. Journal of Product Innovation Management 27 (1): 97–114. https://doi.org/10.1111/j.1540-5885.2009.00702.x
Ireland, R.D. and J.W. Webb. 2007. A multi‐theoretic perspective on trust and power in strategic supply chains. Journal of Operations Management 25 (2): 482–497. https://doi.org/10.1016/j.jom.2006.05.004
Jamai, K., X. Gellynck, A. Abidar and H. de Steur. 2022. Organisational readiness to participate in a labelled product service cluster: a structural equation model analysis. International Food and Agribusiness Management Review 25 (3): 509–529. https://doi.org/10.22434/IFAMR2021.0124
Lavie, D. 2006. The competitive advantage of interconnected firms: an extension of the resource-based view. The Academy of Management Review 31 (3): 638–658.
Lidl. 2020. Zwei Jahre Kooperation: Lidl weitet Sortiment mit Bioland – Produkten weiter aus. Available online at https://unternehmen.lidl.de/pressreleases/2020/201119_2-jahre-bioland-kooperation
Lidl. 2022. Warum Lidl Bioland ausgesucht hat … und umgekehrt. Available online at https://www.lidl.de/c/bioland/s10007740
Lieberman, M.B. and S. Asaba. 2006. Why do firms imitate each other? The Academy of Management Review 31 (2): 366–385. https://doi.org/10.2307/20159207
Lumineau, F. 2017. How contracts influence trust and distrust. Journal of Management 43 (5): 1553–1577. https://doi.org/10.1177/0149206314556656
Marcoulides, G.A. (ed.). 2014. Modern methods for business research. Psychology Press, London.
Martínez–Filgueira, X.–M., D. Peón and E. López–Iglesias. 2022. Determinants of innovation by agri – food firms in rural Spain: an MCA PLS–SEM analysis. International Food and Agribusiness Management Review 25 (1): 103–119. https://doi.org/10.22434/IFAMR2021.0015
Mentzer, J.T., S. Min and Z.G. Zacharia. 2000. The nature of interfirm partnering in supply chain management. Journal of Retailing 76 (4): 549–568. https://doi.org/10.1016/S0022-4359(00)00040-3
Mintzberg, H. 1973. Strategy-making in three modes. California Management Review 16 (2): 44–53. https://doi.org/10.2307/41164491
Morgan, R.M. and S.D. Hunt. 1994. The commitment-trust theory of relationship marketing. Journal of Marketing 58 (3): 20–38. https://doi.org/10.1177/002224299405800302
Otter, V., A. Engler and L. Theuvsen. 2014. The influence of the interplay of supply chain network relationships on farmers’ performance in the Chilean NTAE sector. Journal on Chain and Network Science 14 (3): 149–169. https://doi.org/10.3920/JCNS2014.x010
Pacheco–de–Almeida, G. and P. Zemsky. 2007. The timing of resource development and sustainable competitive advantage. Management Science 53 (4): 651–666. https://doi.org/10.1287/mnsc.1060.0684
Peteraf, M.A. 1993. The cornerstones of competitive advantage: a resource-based view. Strategic Management Journal 14 (3): 179–191. https://doi.org/10.1002/smj.4250140303
Porter, M.E. 1985. Competitive advantage. Creating and sustaining superior performance. Free Press, New York, NY.
Priesmeyer, R.H., R.J. Seigfried and M.A. Murray. 2012. The supply chain as a wholistic system: a case study. Management & Marketing Challenges for the Knowledge Society 7 (4): 551–564.
Raven, B.H. and A.W. Kruglanski. 1970. Conflict and power. In: P.G. Swingle (ed.) The structure of conflict. Academic Press, New York, NY, pp. 69–109.
Richter, B. and J. Hanf. 2020. Competitive strategies for wine cooperatives in the German wine industry. Wine Economics and Policy 9 (2): 83–98. https://doi.org/10.36253/web-8872
Richter, B. and J.H. Hanf. 2023. Competitive parity as strategic dimension – little to gain, much to lose. International Food and Agribusiness Management Review 26 (4): 597–613. https://doi.org/10.22434/IFAMR2022.0101
Richter, B., L. Bitsch, S. Diemer–De Schepper, E. Schweickert and J.H. Hanf. 2021. Strukturveränderungen in der Weinwirtschaft. Berichte über Landwirtschaft — Zeitschrift für Agrarpolitik und Landwirtschaft 99 (2): 1–29. https://doi.org/10.12767/buel.v99i2.362
Richter, B., L. Bitsch and J.H. Hanf. 2022. What Does Brand Management Mean for Wine Cooperatives? International Journal of Co–operative Accounting and Management 5 (1): 27–40.
Ringle, C.M., S. Wende and J.-M. Becker. 2022. SmartPLS 4 — Discriminant Validity Assessment and Heterotrait–monotrait Ratio of Correlations (HTMT). Available online at https://www.smartpls.com/documentation/algorithms-and-techniques/discriminant-validity-assessment/
Rintamäki, T., H. Kuusela and L. Mitronen. 2007. Identifying competitive customer value propositions in retailing. Managing Service Quality: An International Journal 17 (6): 621–634. https://doi.org/10.1108/09604520710834975
Rowe, W.G. and J.G. Barnes. 1998. Relationship Marketing and Sustained Competitive Advantage. Journal of Market Focused Management 2: 281–297.
Sako, M. 1992. Prices, quality and trust. Inter-firm relations in Britain and Japan. Cambridge University Press, Cambridge.
Sarstedt, M. and J.–H. Cheah. 2019. Partial least squares structural equation modeling using SmartPLS: a software review. Journal of Marketing Analytics 7 (3): 196–202. https://doi.org/10.1057/s41270-019-00058-3
Sarstedt, M., J.F. Hair, C.M. Ringle, K.O. Thiele and S.P. Gudergan. 2016. Estimation issues with PLS and CBSEM: where the bias lies! Journal of Business Research 69 (10): 3998–4010. https://doi.org/10.1016/j.jbusres.2016.06.007
Schaack, D. 2021. Deutsche pushen Transformation: Historisches Umsatzwachstum am Bio–Markt. Umsatzentwicklung von Bio–Lebensmitteln. Available online at https://www.boelw.de/themen/zahlen-fakten/handel/artikel/umsatz-bio-2020/
Schaack, D. 2022. Bio–Markt weiter in Fahrt mit Umsatzplus von 5,8 %. Umsatzentwicklung von Bio-Lebensmitteln. Available online at https://www.boelw.de/themen/zahlen-fakten/handel/artikel/umsatz-bio-2021/.
Shmueli, G. and O.R. Koppius. 2011. Predictive analytics in information systems research. MIS Quarterly 35 (3): 553–572.
Swinnen, J.F.M. and M. Maertens. 2007. Globalization, privatization, and vertical coordination in food value chains in developing and transition countries. Agricultural Economics 37: 89–102. https://doi.org/10.1111/j.1574-0862.2007.00237.x
Tan, S. and W. Chen. 2021. Building consumer trust in online food marketplaces: the role of WeChat marketing. International Food and Agribusiness Management Review 24 (5): 845–862. https://doi.org/10.22434/IFAMR2020.0159
Thorelli, H.B. 1986. Networks: between markets and hierarchies. Strategic Management Journal 7 (1): 37–51. https://doi.org/10.1002/smj.4250070105
Wagner, M., P. Stanbury, T. Dietrich, J. Döring, J. Ewert, C. Foerster, M. Freund, M. Friedel, C. Kammann, M. Koch, T. Owtram, H.R. Schultz, K. Voss–Fels and J. Hanf. 2023. Developing a Sustainability vision for the global wine industry. Sustainability 15 (13): 10487. https://doi.org/10.3390/su151310487
Weiber, R. and M. Sarstedt. 2021. Strukturgleichungsmodellierung. Eine anwendungsorientierte Einführung in die Kausalanalyse mit Hilfe von AMOS, SmartPLS und SPSS. Springer Fachmedien, Wiesbaden.
Wernerfelt, B. 1984. A resource-based view of the firm. Strategic Management Journal 5 (2): 171–180.
Williamson, O.E. 1979. Transaction-cost economics: the governance of contractual relations. The Journal of Law and Economics 22 (2): 233–261. https://doi.org/10.1086/466942
Xhoxhi, O., D. Imami, J. Hanf and E. Gjokaj. 2022. Too much power or no power: when does intermediary’s power result into better wine and happier farmers? International Food and Agribusiness Management Review 25 (5): 771–787. https://doi.org/10.22434/IFAMR2021.0143
Appendix
Operationalization of variables.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
Source: Authors.Question for each item.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
Source: Authors. Note that the table does not correspond to the order of the questions in the questionnaire. Questions have been translated from German into English language. The complete questionnaire (in German language) can be shared upon request.Factor loadings of the indicators (or items).
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
Source: Authors.Results of the evaluation of the measurement model.
Citation: International Food and Agribusiness Management Review 27, 3 (2024) ; 10.22434/ifamr2023.0089
Source: Authors.Corresponding author
Legitimate power is closely linked to cultural values. The values or standards must be accepted by the individual so that the exercise of the legitimate influence strategy is possible at all, see French and Raven (1959).
Referent power shows the ability to be attractive to others as a business partner and is closely linked with the charisma and interpersonal skills of the power holder. It is closely related to the identification of a person/group with another person/group. Here, the desire for affiliation predominate, see French and Raven (1959).
The table does not correspond to the order of the questions in the questionnaire. Questions have been translated from German into English language. The complete questionnaire (in German language) can be shared upon request.
The first mailing was sent from 19–23 August 2022. The first reminder was sent between 31 August and 2 September 2022, the second reminder in the period 8–12 September 2022.
