1. Introduction
Increased competitiveness, globalization and an increasingly turbulent environment make it difficult for companies to obtain sustainable competitive advantages over time [
1]. In this context, organizations are focusing their interests on organizational capabilities and routines that allow them to differentiate themselves from their competitors and, as a consequence, obtain superior performance [
2]. In addition to the factors described above, the wine industry has to face challenges specific to the sector that threaten its survival, such as global warming, energy scarcity and water scarcity [
3]. Faced with this situation, wineries are beginning to align their economic interests with social and environmental ones, since their survival depends on caring for and respecting the environment and the society in which they operate [
4].
In order to protect the environment while achieving economic performance, wineries can develop different capabilities to reduce and reuse the resources used in their production process, thus reducing their operating costs and increasing their differentiation in the market [
5]. In fact, according to the Natural Resource-Based View (NRBV), resources and organizational capabilities aimed at protecting the environment represent the main source of competitive advantage, since they allow cost savings and differentiation to be achieved at the same time [
6].
Organizational capabilities linked to environmental protection can be achieved through the generation of green knowledge of a human, structural and relational nature. This set of green intangibles was coined in the academic literature as Green Intellectual Capital (GIC), referring to the sum of knowledge and skills of the company oriented to environmental protection, and being divided into three blocks: Green Human Capital (GHC), Green Structural Capital (GSC) and Green Relational Capital (GRC) [
7]. This set of green intangibles has different benefits for companies, which can be understood through the joint comprehension of the NRBV and the Intellectual Capital-Based View (ICBV). On the one hand, the ICBV holds that the intangible assets of companies have a high strategic character, given that they are difficult to imitate and reproduce due to their intangible nature, resulting in the improvement of their competitiveness [
8]. On the other hand, the NRBV considers that the environmental actions developed by companies can become a source of competitive advantage, thus guaranteeing their survival in the market [
9]. GIC allows them to combine both benefits, catalyzing the generation of new organizational capabilities, as well as performance in its triple dimension, i.e., Sustainable Performance (SP).
The exploitation of GIC by companies can lead to the improvement of Green Supply Chain Management (GSCM) by providing new knowledge to improve environmental management at different stages of the production process [
10]. GSCM is defined as the set of activities focused on improving the environment at different stages of the production process [
11]. GSCM can involve different actions at different stages of the production process, such as requiring green certificates from suppliers (provisioning), developing environmentally friendly products and processes (production) or developing green innovations for product packaging (distribution). For this reason, GSCM requires the linking of organizational capabilities with the development of green know-how [
12].
GSCM, in turn, can improve the SP of companies as a result of the savings in operating costs and the improved positioning and reputation that such management implies [
13]. In the wine context, GSCM involves the introduction of environmental actions in the stages of viticulture, winemaking and distribution of wine, allowing for improvements in the performance of wineries in its triple dimension through different ways such as cost reduction, increased competitiveness, improved differentiation and consumer positioning, among others. In this sense, the Green Agriculture (GA) actions carried out by wineries play a decisive role in enhancing the GSCM–SP relationship, since, on the one hand, it improves the environmental development of viticulture and, on the other hand, it enables the production of organic, natural and biodynamic wines in the market, with the consequent benefit that this entails [
14].
There are previous studies that point to the existence of a positive relationship between GIC and SP, as well as the positive influence of GIC on GSCM [
15,
16,
17]. However, to the best of our knowledge, there have been no previous studies that have addressed these relationships in the wine context. Moreover, no study has analyzed the moderating role that GA may play in the GSCM–SP relationship. To overcome these gaps in the academic literature, the study aims to analyze the effect of GIC on SP, as well as the mediating impact of GSCM and the moderating role of GA in this linkage. Therefore, the article is intended to answer the following three research questions (RQs): RQ1 does GIC have a positive effect on the SP of wineries?; RQ2 does GSCM mediate the GIC–SP relationship in wineries?; and RQ3 does GA moderate the GSCM–SP relationship in wineries? These questions are answered by testing the theoretical model proposed in this research using structural equation modeling.
To facilitate an adequate understanding of the study, it is structured as follows. First, after this brief introduction,
Section 2 sets out the theoretical model to be tested,
Section 3 presents the methodology followed to achieve the research objectives,
Section 4 shows the main results,
Section 5 discusses these results and, finally,
Section 6 reflects on the main conclusions, limitations and future lines of research.
4. Results
Given the multidimensional nature of the variables used, a two-stage model based on the scoring of latent variables is used for the study [
60]. Thus, first, the latent scores of each of the first-order variables are calculated and, second, these scores are considered as indicators of the second-order variables. The results are structured following the recommendations of Hair et al. [
61], who advise reporting the results in three stages: (1) the evaluation of the global model, (2) the evaluation of the measurement model and (3) the evaluation of the structural model.
First, as regards the evaluation of the global model, it is possible to affirm that the model presents an adequate fit, since the Standardized Root Mean Square Residual (SRMSR) is less than 0.08 (0.068 < 0.080), which implies that the model is able to explain the phenomena analyzed and, therefore, cannot be rejected.
Table 2 shows the results relative to this evaluation, demonstrating both the SRMR and the values relative to the unweighted least squares discrepancy (d_ULS) and the geodesic discrepancy (d_G). As can be seen, these last two indicators are within the confidence intervals after bootstrapping, being therefore below HI95 and HI99.
Second, regarding the analysis of the measurement model, it should be noted that the criteria established by Hair et al. [
61] are based on the analysis of the reliability of the indicators, the evaluation of the internal consistency, the verification of the convergent validity and the evaluation of the discriminant validity.
Table 3 shows the individual confidence of the indicators that make up the constructs, since the loads exceed the value of 0.707 established in the academic literature [
62]. Furthermore, the loads are statistically significant after applying the bootstrapping procedure. This table also makes it possible to demonstrate the existence of internal consistency and convergent validity. On the one hand, internal consistency refers to the degree of association between the indicators that form the same construct [
63]. Values greater than 0.8 relative to Cronbach’s alpha, composite reliability (Pc) and the Dijkstra–Henseler (Pa) criterion allow us to corroborate the existence of internal consistency [
64]. On the other hand, convergent validity refers to the degree to which a measure is positively correlated with alternative measures of the same construct, this type of validity existing when the Average Variance Extracted (AVE) exceeds the 0.5 level [
65]. As can be seen in
Table 3, the AVE values for the four constructs analyzed are greater than 0.5.
For the analysis of discriminant validity, for its part, the Heterotrait–Monotrait (HTMT) criterion was followed, allowing us to know to what extent the constructs were different from each other [
66].
Table 4 shows the values relative to the HTMT ratio, these being clearly less than 0.85. This means that the constructs analyzed in the research are different from each other and, therefore, capture different realities [
67].
Third, once the reliability and validity of the constructs had been verified, the structural model was evaluated. This evaluation, following the recommendations of Hair et al. [
61], consisted of the analysis of the path coefficients, and the predictive relevance of Q2. On the one hand,
Figure 2 shows the data regarding the path coefficient based on a bootstrap test with 5000 subsamples and the R-values. This shows that all the direct and indirect relationships are positive and statistically significant. This implies that GSCM partially mediates the relationship between GIC and SP, since both the direct (0.302) and indirect (0.178) effects are positive and statistically significant, with a total effect of GIC on SP of 0.480 (
p < 0.000). The moderating relationship is positive but not significant, so the results for this relationship cannot be extrapolated to the study population.
The results of the model allow us to verify four of the five hypotheses, given that there is a positive and significant effect of GIC on SP (H1. β = 0.302;
p < 0.000), there is a positive and significant effect of GIC on GSCM (H2. β = 0.427;
p < 0.000), there is a positive and significant effect of GSCM on SP (H3. β = 0.417;
p < 0.000), GSCM mediates the GIC–SP relationship (H4. β = 0.178;
p < 0.000) and GA shows a positive but non-significant moderation effect in the GSCM–SP relationship (H5. β = 0.086;
p < 0.195). The results show that the GIC developed by wineries is the strongest predictor of GSCM. The strongest predictor of the SP variable is, in turn, GSCM (see
Table 5). As for the control variables, the results show that while winery size has a positive and significant on SP (β = 0.138;
p < 0.003), PDO membership (β = −0.040;
p < 0.429) and age (β = −0.005;
p < 0.942) show a negative and non-significant relationship. Finally, to analyze the quality of the model, the Geisser test (Q2) was performed, which must present estimated values greater than 0 (Q2 > 0). According to Hair et al. [
61], Q2 values greater than 0, 0.25 and 0.50 show, respectively, situations of small, medium and large predictive relevance.
Table 6 shows the medium predictive relevance of the model, given that the values were greater than 0.25 [
63].
5. Discussion
The results offered in this research are very useful for both academics and professionals in the wine sector who wish to learn about the mechanisms through which the economic, social and environmental performance of Spanish wineries can be improved. In particular, the study empirically demonstrates the antecedent role of GIC and GSCM to improve SP, highlighting the importance of developing environmental intangibles of a human, structural and relational nature in order to improve the performance of wineries in its triple dimension.
The set of winery intangibles aimed at improving the environment can improve SP for several reasons. Firstly, as employees’ environmental knowledge increases, the winery’s environmental management will improve, reducing the materials and resources used in the production process and, consequently, improving the winery’s environmental performance. However, this improvement in the winery’s environmental actions may represent not only an improvement in environmental performance, but also an improvement in social and economic performance, given that, on the one hand, workers will be happier to work in a company with high environmental awareness and, on the other hand, these actions may lead to an improvement in business differentiation, with the consequent improvement in organizational performance. Secondly, the different elements of GSC, such as collaborative culture, decentralized organizational structure or linking the brand to sustainability, allow the institutionalization of the wineries’ sustainable approach, providing them with mechanisms for acquiring, transferring and applying new green knowledge that will improve SP. Thirdly, the relationships that wineries establish with the rest of their stakeholders with the aim of improving the environment can lead to the acquisition of green knowledge, as well as the generation of business opportunities that result in improved business performance. The results derived from the research are in line with the research of Yusoff et al. [
19], Malik et al. [
8] and Ullah et al. [
24], who demonstrate the existence of a positive and significant relationship between the two variables in the manufacturing context of Malaysia, Pakistan and China, respectively.
In this sense, the GIC of wineries can also improve their GSCM, since the incorporation of sustainable practices into the different stages of the wine value chain can be achieved through the increased environmental knowledge of employees, the codification of this knowledge so that it is accessible to the entire company and the imposition of environmental requirements on suppliers with whom wineries cooperate. The improvement of GSCM, in turn, can lead to the improvement of SP, since the improved sustainability of the wine chain implies improvement in its efficiency, with positive repercussions in economic, social and environmental terms. Regarding the moderating role of GA in the GSCM–SP relationship, the study points to the existence of a positive and significant link. Therefore, although GA exerts a positive moderating effect on this relationship in the sample wineries, this effect cannot be extrapolated to the population under study. This may be due to the fact that GA mainly improves viticulture within GSCM and environmental performance within SP, thus weakening the effect of GA on this relationship. The results concerning the GIC–GSCM–SP sequence are in line with recent research in the field of environmental management, such as those of AL-Khatib and Shuhaiber [
31] and Xi et al. [
68], who contextualize their studies in the manufacturing sectors of Jordan and China, respectively.
6. Conclusions
The present research highlights the importance of GIC in catalyzing both GSCM and SP. It also allows us to demonstrate the positive and significant mediating role of GSCM in the GIC–SP relationship, as well as the moderating effect of GIC on SP.
A series of theoretical and practical implications are derived from the results of the study. With regard to the theoretical implications, the study is pioneering in the contextualization of the model proposed for the Spanish wine industry. Moreover, to the best of our knowledge, there were no previous studies that analyzed the moderating role of GA in the GSCM–SP relationship, so the research represents the generation of new scientific knowledge in the field of environmental management and management. In terms of practical implications, the research may be useful for winemakers who are considering improving their environmental intangibles in their wineries, as well as developing environmental practices along their value chain, since, as demonstrated, this will improve the SP of their wineries. Despite the lack of significance of GA, the study shows its importance in improving environmental practices in viticulture and the environmental performance of wineries, so that winery managers may consider including it in the practices developed in their companies.
Despite the important contributions of the study, it should be noted that the research suffers from certain limitations. First, given that the study was contextualized in the Spanish wine industry, its study is necessary in other wine contexts. In this sense, as a future line of research, it is proposed to contextualize the theoretical model proposed in the Californian wine industry to learn about the similarities and differences between the two wine contexts. Secondly, the study has the limitation of cross-sectional research, since the results correspond to a specific moment in time. In order to overcome this deficiency, as a future line of research we intend to carry out a longitudinal study with the companies in the present sample.