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Article

The Effect of Green Purchasing Practices on Financial Performance under the Mediating Role of Environmental Performance: Evidence from Türkiye

by
Ali Ibrahim Balin
1 and
Kazim Sari
2,*
1
Institute of Graduate Studies, Beykent University, Beyoglu, Istanbul 34433, Türkiye
2
Industrial Engineering Department, Beykent University, Sariyer, Istanbul 34396, Türkiye
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(4), 3617; https://doi.org/10.3390/su15043617
Submission received: 12 January 2023 / Revised: 10 February 2023 / Accepted: 13 February 2023 / Published: 16 February 2023
(This article belongs to the Special Issue The New Era of Sustainable Public Procurement)
Browse Figure
Versions Notes

Abstract

:
This study aims to examine the impact of green purchasing practices (GPP) on the financial performance (FP) of companies and the mediating role of environmental performance (EP) in the context of Türkiye, a developing country. GPP are represented by their all-related activities, such as green supplier selection (GSS), green supplier development (GSD), green supplier collaboration (GSC), and green supplier evaluation (GSE). Although much attention has been paid to GPP and their impact on FP in recent years, empirical evidence is still needed, particularly for companies in developing countries. For this purpose, this study explores the effect of GPP on FP and the mediating role of EP with survey data obtained from 455 companies in Türkiye. The findings reveal that GPP, in general, make a positive contribution to both the EP and FP of companies in Türkiye. However, the two GPP sub activities, GSD and GSE, do not have a significant effect on EP, contrary to our expectation. In addition, it is found that EP causes a significant increase in FP and plays a partial mediating role in the effect of GPP on FP. The insight gained from this study is that the increase in FP brought directly by GPP and indirectly through the increase in EP may offset the additional cost posed by GPP. In fact, this is an important finding in terms of overcoming the obstacles encountered in the transition to GPP in developing countries.

1. Introduction

Environmental changes and the concerns raised to mitigate their unfavorable effects have made new legal regulations a necessity. With the gradual depletion of resources, as well as an increase in environmental accidents, governments have become more focused on environmental regulations than before [1]. Namely, various protocols have been prepared for this purpose, such as the Montreal Protocol, the Basel Convention, the Kyoto Protocol or the 2002/96/EC Directive on Waste Electrical and Electronic Equipment (WEEE) [2]. At present, a company’s environmental performance (EP) has become a criterion for customer preference and can affect its export transactions, particularly with developed countries. This has propelled companies in developing countries such as Türkiye to new arrangements in terms of environmental awareness and has popularized the concept of “Green” [3]. Green supply chain management (GSCM), which is very popular today, did not have a clear definition until the 1990s and the entire structure was referred to as green purchasing practices (GPP). Since the late 1990s, GPP have been extended to include reverse logistics, internal environmental management, eco-design, and environmental collaborations with customers and, then, was named GSCM [4].
Suppliers have a very important role in GSCM; namely, wrongful supplier behavior can cause incidents that can damage companies’ public image and reputation [5]. For this reason, GPP are regarded as the most crucial part of GSCM adoption. GPP seek raw materials and semi-finished products that have no environmental impact, requiring a radical change in purchasing practices [6,7]. There are several practices that exist under GPP. Research Yee et al. [8] categorized them as green supplier selection (GSS), green supplier development (GSD), green supplier collaboration (GSC), and green supplier evaluation (GSE). However, the relevant literature shows that not all GPP subgroups are equally adopted. For instance, Finger and Lima-Junior [9] have noted that GSD was neglected, while the most attention was given to GSS. Similarly, Hąbek and Lavios Villahoz [10] have stated that GSS, GSC, and GSE were prioritized in the existing studies.
In terms of the impact of GPP on company performance, they are mostly reported to have a positive effect on EP, while studies showing their benefit on FP are less common [11,12]. This may be because GPP result in additional costs for companies and, thus, their financial benefits are not visible for all cases, particularly in developing countries [13]. In addition, the level of GPP adoption and the environmental awareness of customers in developing countries is not as high as in developed countries [14,15]. For example, a recent study in the Turkish food industry shows that although the green criterion is considered as a factor in supplier selection, it is one of the least important [16]. Moreover, a study conducted in Jordan reveals that companies competing in cost show limited attention to GPP [17]. In fact, there are some other studies in developing countries showing similar results [18]. In addition, a study conducted in Europe, Asia and North America reveals that there is no relationship between GPP and FP unless green purchasing behavior of customers is added as a moderator variable to the research model [19]. In contrast, a study conducted in India, an emerging country, indicates that GPP produce increased FP for companies [20]. In fact, all of these examples show that providing empirical evidence about the effect of GPP on FP is crucial in the context of developing countries. To this end, this study aims to shed more light on this topic to contribute to the existing literature on GPP and their benefits in developing countries.
The remainder of the paper is organized as follows. Section 2 covers the theoretical background of GPP, EP, and FP, along with the research model. In Section 3, the research method is explained. The data analysis and results are presented in Section 4 and Section 5 with discussions. Finally, in Section 6, the conclusion is presented.

2. Theoretical Background

This section gives a theoretical review of the research on GPP within the scope of EP and FP. Hazaea et al. [21] examined the green purchasing articles published between 1998–2021 in the Scopus database and classified them according to the theory bases explaining green purchasing. The theory of planned behavior and the theory of reasoned action are reported as the most used theories in the prior studies. This study is suitable for the signal theory, which argues that companies make their environmental contributions visible to the public, as the focus is not on individuals but on the purchasing departments of companies.

2.1. Green Purchasing Practices (GPP)

GPP are evaluated by four main activities: GSS, GSD, GSC, and GSE. Choosing the right supplier has always been a crucial decision as nearly 70% of the total production costs come from procurement [22,23]. Due to the increasing importance of compliance with environmental standards, the green factor has now become an important criterion in supplier selection [24]. GSS requires working with suppliers who are good at reducing the environmental issues of their operations.
GSD is the process of helping suppliers improve their environmental skills and capabilities [25]. This assistance can take many forms, such as pressuring, training, mentoring, and employee transfer. In this way, GSD creates opportunities for environmental compliance [26,27]. GSD is also seen as a critical factor for sustainable supply chain structuring.
GSC represents the joint work between a company and its suppliers to anticipate and solve environmental issues on both sides [28]. GSC requires cooperation with suppliers in activities such as reducing energy consumption, reusing materials, making environmentally friendly packaging, and developing waste minimization and disposal programs. It is reported that GSC is mandatory for companies to achieve their EP targets [29].
GSE is a process to measure and monitor the green performance of suppliers. In line with the sustainability transformation goal, periodically measuring the performance of suppliers and determining the evaluation method is one of the critical challenges faced by purchasing managers [30]. The adoption of GPP, guided by a good GSE system, can improve company-supplier relations on the axis of FP [31].

2.2. Company Performance

EP and FP are the most frequently discussed performance criteria in studies on GSCM. EP refers to the total environmental impact of companies by offering energy saving, pollution reduction, and environmentally friendly products and/or services [32]. Negative effects caused by companies on the environment due to their purchasing, production, distribution, and marketing operations generate negative implications on their EPs [33]. The existence of a green organizational culture in companies creates awareness about the resources used, the wastes produced, and the energy consumed, and this positively affects the EP [34,35].
FP represents the financial gains obtained from the implementation of GPP. According to the studies in [36,37,38], FP can be characterized as a reduction in material purchase cost, energy consumption cost, waste treatment cost, waste discharge cost, and environmental accident penalties due to the deployment of GPP.

2.3. Hypothesis Development and Conceptual Model

Discussions about the environmental and financial impacts of GPP date back decades. In terms of their environmental impacts, whether in the service or manufacturing industry, it is intuitively expected that GPP should have a positive impact on EP. At this point, the studies in [32,39,40,41,42] have indicated that companies would see an upward trend in their EP thanks to GPP. Considering GSS as part of GPP only, Bu et al. [43] indicated that choosing the right supplier with environmental awareness will enable companies to improve their EP. In a similar manner, it has been pointed out that GSC have a significant impact on the EPs of companies in direct proportion to their willingness to learn how one another operates [44,45,46]. In addition, the studies in [47,48] stated that GSD provides higher EP by putting pressure on suppliers and supporting them with technical advice on environmental issues. Finally, a significant increase is observed in EP when suppliers are monitored under a GSE system [49]. Thus, the following hypotheses can be presented accordingly:
H1. 
GPP have a significant impact on EP.
H1a. 
GSS has a significant impact on EP.
H1b. 
GSD has a significant impact on EP.
H1c. 
GSC has a significant impact on EP.
H1d. 
GSE has a significant impact on EP.
However, when it comes to the direct impacts of GPP on FP, the situation is not so clear as with EP. For instance, Choudhary and Sangwan [50] have suggested that GPP negatively affects FP by causing an increase in purchasing costs, while the studies in [51,52] have reported an increase in FP with GPP adoption. With a focus in each GPP group, it has been reported that GSS could provide a competitive power, leading to higher FP [53,54]. Similarly, GSC can provide a gain to FP by ensuring a reduction in transportation costs [55]. In addition, Zhang et al. [56] have mentioned that GSC may lead to increases in FP. Finally, it has been revealed that companies could improve their FP significantly through GSD adoption [57]. Thus, the following hypotheses are presented accordingly:
H2. 
GPP have a significant impact on FP.
H2a. 
GSS has a significant impact on FP.
H2b. 
GSD has a significant impact on FP.
H2c. 
GSC has a significant impact on FP.
H2d. 
GSE has a significant impact on FP.
The effect of EP on FP is of great interest to researchers. More than two decades ago, the studies in [58,59] pointed out that EP success would result in a positive effect on FP. In addition, Seuring and Müller [60] argued that a positive effect of EP on FP could be established with a long-term GSCM. More recently, Vanalle et al. [61] reported that EP and FP paved the way for each other and mentioned an interaction between them. According to Yook et al. [62], an increase in a company’s EP due to GPP, followed by an increase in its other achievements in FP, indicates a positive effect. The outputs of Altaf et al. [51] confirmed a positive correlation between EP and FP. The studies in [63,64] found that EP has a strong positive effect on FP and that every increase in EP improves the FP of firms. Thus, the following hypothesis is presented:
H3. 
EP has a significant impact on FP.
Finally, it is also interesting to investigate the mediating effect of EP between GPP and FP. In this context, Yang et al. [65] argued that EP indirectly affects the relationship between GSC and firms’ competition. Similarly, Choudhary and Sangwan [50] stated that EP is an indicator of decisions to invest in green activities and could affect FP. Awaliyah and Haryanto [34] have found that green organizational culture and green innovation have a significant impact on the competitive advantage by mediating EP. Finally, it has been reported that EP may act as a multiplier for FP as it is inherently linked to a future competitive advantage [66]. Accordingly, the following hypothesis is presented:
H4. 
EP mediates the effect of GPP on FP.
The conceptual model of the study is presented in Figure 1.

3. Research Method

3.1. Method and Tool of Data Collection

Survey data were collected through a Computer-Assisted Telephonic Interview (CATI) data collection method to avoid paper consumption and manage the process faster. The target population was very large as it consisted of all the companies operating in Türkiye. Namely, according to the Turkish Statistical Institute, the number of companies operating in Turkey in 2021 is 3,568,000. The guideline recommended by Taherdoost [67] was used in determining the required sample size. Accordingly, the required sample size was determined as 384 at a 95% confidence level. The survey was conducted between last quarter of 2020 and first quarter of 2021. A purchasing manager from each company was invited to answer the questionnaire. The Turkish sample was obtained by ensuring that the survey had covered all regions of the country. The study sample predominantly consisted of SMEs who play an important role in purchasing with their expertise. The demographic information of the respondents and their company profile are provided in Table 1.
Focusing on GPP and their effects on EP and FP, the related literature was collected for the timeframe between 1993 and 2022. Questionnaire items were created for GPP by examining the relevant academic resources and taking the opinions of academicians and practitioners. On the other hand, dependent variable questions, which have been used multiple times before for EP and FP, were maintained and used [36,37,38]. For measurement, a 5-point Likert scale was used.

3.2. Pilot Study

A pilot test was performed to test and improve the survey. The reliability test began when 32 questionnaires were completed. Normality was handled through skewness-kurtosis values, one of the ways of measuring normal distribution where similar restrictions apply in surveys that use the Likert scale [68]; the skewness-kurtosis values for the independent and dependent variables were within the acceptable range of ±2.0 [69]. In the reliability analyses, 100 more questionnaires were obtained during the ongoing pilot survey, and then validity tests were performed.

3.3. Reliability and Validity Analyses

Using the results of the pre-test conducted as a part of the pilot study, the issues of questionnaire item clarity, item completeness, and adding more items were worked on. GPP were determined as 47 questions in total, EP and FP was determined as 11 questions, and the questionnaire was composed of 58 questions in total. At the end of interviews, held with one person from each company, the study sample was established with 457 companies, above the minimum sample size of 384. The testing performed using the IBM software package SPSS (Statistical Product and Service Solutions) version 22 resulted in terms of reliability. Through outliers’ analysis, the 406th and 407th questionnaires were removed, starting from the one with the highest number of extreme values [69], and the repeated reliability testing resulted in Cronbach Alpha (α) values over 0.70 [38] for all of the variables and required ranges for normality (±2.0) were achieved [70]. Validity testing for the GPP variables was performed using the responses given to 47 survey questions. Items with a factor load below 0.50 were dropped off from the scale as they did not contribute to the applicability of the scale [71]. The repeated validity test after item elimination resulted in a KMO sampling adequacy value above 0.90 for GPP, EP, and FP [50]. According to Bartlett’s Test, Sig. 0.000 is meaningful, and a high correlation (p < 0.01) appears to exist between all of the variables [72]. As a result of these analyzes, 22 items were obtained for the GPP, shown in Appendix A.

3.4. Exploratory (EFA) and Confirmatory Factor Analyses (CFA)

The scale’s GPP variables, consisting of 4 sub-dimensions and 22 items in total, were analyzed using the IBM software package SPSS Amos version 24. The results are presented in Table 2.
According to the EFA and CFA results in Table 2, the model satisfied the requirement of CMIN/DF < 5 for the goodness of fit index [73]. In addition, the CFI, GFI, and NFI indices were at acceptable levels with values of 0.90 and above. A GFI index exceeding 0.90 is considered to indicate a good model [74]. The AGFI index also conformed to the expectation of a value above 0.80 and close to 1 [75]. The RMSEA and RMR indices, considered key values to identify a lack of fit, also conformed to the requirement of being less than 0.05 and 0.08, respectively, which indicate optimal or sufficient levels [69]. The requirement of p < 0.01 was satisfied and deemed compatible with the data structure of this proposed model; therefore, all of the sub-parameters of the measurement model were accepted.
Similarly, the EFA and CFA analysis results for the dependent variables of the scale are presented in Table 3. Accordingly, the model’s goodness of fit index satisfied the requirement of CMIN/DF < 5. The CA, CR, and AVE values show that the reliability, convergent, and discriminant validity requirements were all satisfied. They show that the theoretical structures exhibit satisfactory psychometric properties [34,76].

4. Data Analysis and Results

The relationships between the variables were identified through correlation analysis via IBM software package SPSS version 22; subsequently, the extent to which the independent variables explain the dependent variables was identified through linear regression analysis [69]. Correlation analyses for the mediating effect performed via the IBM software package SPSS Amos version 24 were conducted on 455 questionnaires without any missing value. The Fornell-Larcker criterion was used to ensure the discriminant validity. For this, the values on the diagonal in Table 4, the square roots of the AVE shown in Table 2 and Table 3, were compared with the correlations in the respective rows and columns. Accordingly, discriminant validity was accepted because the values on the diagonal were greater than or equal to the correlations [77].
H1 Hypothesis: As a result of the correlation analysis in Table 4, a positive and significant relationship was found between GPP and EP (H1). The linear regression between GPP and EP in Table 5 indicates that 46% of the GPP variances (R2) seemed to be attributable to EP, and GPP have a significant and positive (β = 0.68, p < 0.01) effect on EP.
H1a-b-c-d Sub-hypotheses: It is revealed in Table 4 that there is a positive and significant relationship between GSS, GSD, GSC, GSE, and EP. The linear regression between GSS, GSD, GSC, GSE, and EP in Table 5 shows that 54.20% of variances (R2) were EP-attributable. As for the linearity verification, a VIF value below 10 demonstrates that there is no multi-correlation or multicollinearity problem, while tolerance statistics above 0.20 are also at a satisfactory level in this construct [69]. It shows that GSS (β = 0.48, p < 0.01) and GSC (β = 0.36, p < 0.01) have significant and positive effects on EP, while there is no significant effect of GSD on EP, or GSE on EP, and (p > 0.1).
H2 Hypothesis:Table 4 indicates a positive and significant relationship between GPP and FP. The linear regression between GPP and FP in Table 5 shows that 49.50% of the GPP variances (R2) were attributable to FP. GPP have a significant and positive (β = 0.70, p < 0.01) effect on FP.
H2a-b-c-d Sub-hypotheses: It is shown in Table 4 that there is a positive and significant relationship between GSS, GSD, GSC, GSE, EP, and FP. The linear regression between GSS, GSD, GSC, GSE, and FP in Table 5 shows that 52.30% of the variances (R2) seem to be attributable to FP. As linearity verification, a VIF value below 10 demonstrates that there is no multi-correlation or multicollinearity problem, while tolerance statistics above 0.20 are also at a satisfactory level. In addition, GSS (β = 0.42, p < 0.01), GSD (β = 0.14, p < 0.01), GSC (β = 0.20, p < 0.01), and GSE (β = 0.12, p < 0.01) have significant and positive effects on FP.
H3 Hypothesis:Table 4 shows a positive and significant relationship between EP and FP. The linear regression between EP and FP in Table 5 shows that 55.60% of the variance in EP (R2) was attributable to FP. EP has a significant and positive (β = 0.75, p < 0.01) effect on FP.
H4 Hypothesis: A 3-phase process was performed using IBM AMOS 24. First, the effect of GPP on FP was analyzed. The modified model presents a good fit (CMIN = 14.638, DF = 7, CMIN/DF = 2.091, p = 0.041, RMSEA = 0.049, CFI = 0.995, GFI = 0.989, AGFI = 0.967, NFI = 0.990). The explanatory power (R2) of GPP for FP was 55%. Path (GPP→FP) indicates a significant and positive (β = 0.74, p < 0.01) effect. Later, the contemporary approach was performed by applying bootstrap [78]. Upon the introduction of EP, the obtained model presents a good fit (CMIN = 164.620, DF = 50, CMIN/DF = 3.292, p = 0.000, RMSEA = 0.071, CFI = 0.968, GFI = 0.942, AGFI = 0.909, NFI = 0.955). While the explanatory power (R2) of GPP for EP was 53%, the explanatory power (R2) of GPP for FP increased to 73%. As path (GPP→EP) (β = 0.73, p < 0.01) and path (EP→FP) (β = 0.62, p < 0.01) were positive and significant, the β value decreased from 0.74 to 0.29 in the path (GPP→FP) (β = 0.29, p < 0.01), a partial mediating effect can be mentioned [73]. In the final step, the model was re-run in a 95% confidence interval and on 5000 samples, and the significance of the partial mediating effect was confirmed [71].

5. Discussion

The results of the hypotheses testing, which was performed using the GPP scale created as a part of the study, are presented in Table 6, along with their summarized values.
The results for H1 and H2 shows that GPP provide significant benefits for companies in Türkiye in terms of EP and FP in general. This suggests that executives in Türkiye need to focus more on GPP while doing their job. When H1 and H2 is elaborated, the expected positive effect of GSS on EP (H1a) and FP (H2a) occurred in parallel with the relevant literature. In addition, the effect of GSC on EP (H1c) and FP (H2c) was positive, as expected. Similarly, GSD and GSE showed their expected positive effect on FP (H2b) and FP (H2d), respectively. Surprisingly, GSD had no significant effect on EP (H1b) and GSE had no significant effect on EP (H1d). This can be explained with the geographical barriers between companies and suppliers in Türkiye, as this could hinder GSE and GSD, which companies address when managing their suppliers [79]. Considering that production in Türkiye is sourced through imports, it is likely to create a disadvantage in terms of GSD and GSE. It has also been reported that GSE may not have a direct effect on manufacturing companies’ EP [79,80] and that the practical relevance of GSD is far from what it deserves [81]. This reveals that more attention should be paid to GSD and GSE as they are crucial for developing long term GPP behavior by managers.
In the case of H3, EP showed a positive effect on FP, as expected. This reveals that managers in Türkiye can see the financial benefits of a having good EP. This result is important because one of the barriers to GPP are the perception of some company executives in developing countries that GPP only have environmental benefits, not financial benefits. However, this study shows that increased EP also results in increased FP.
When the analysis results for H4 are considered, it was supported due to EP’s partial mediating effect. It shows that the cost burden generated by GPP have been balanced with the increase in FP, which has been achieved because of EP. There are studies showing that the lack of information and experience in implementing GPP can cause an increase in costs associated with GPP and a decrease in the competitive power [62]. The direct effect of GPP on FP along, with the secondary FP, increase as a result of the generated EP; this shows that senior management in companies can have a key role in overcoming the GPP barrier caused by cost-related concerns.

6. Conclusions

Although there are many studies on GPP and their effect on company performance, it is still a controversial issue among academia whether companies should be proactive in GPP or implement them at a minimum level to meet the requirements, particularly in developing countries. This is partly because not all research results show that GPP lead to significant benefits for companies. For this purpose, in this research, a reliable and valid measurement scale of GPP is developed to explore their impacts on EP and FP. The detailed statistical analyses of the survey data obtained from 455 purchasing managers in Türkiye reveal important insights.
First, GPP directly provide significant benefits for both EP and FP. When considered in detail, it is realized that GSS and GSC lead to positive reflections on EP and FP. Surprisingly, no direct effect arising from GSD and GSE is observed on EP, while they have positive contributions to FP. This indicates that GSD and GSE are used as leverage for FP rather than EP.
Second, EP has an effect on FP in a positive direction. This demonstrates that companies in Türkiye are rewarded with a financial contribution for their efforts and struggles on environmental issues.
Third, EP partially mediates the effect of GPP on FP, meaning that the contribution of GPP on EP creates a stronger increase in FP.
Finally, this study has some limitations, which must be mentioned. The purchasing managers responding to the survey might have presented their ideas from their own epistemological perspective to appear more eco-friendly. The study is limited to Türkiye, and the competence level of the executors performing the purchasing practices being investigated is important. The attempt to approach profiles in a narrowed field, such as expert or higher-level personnel who simultaneously hold decision-making authority, can be a limiting factor in terms of the scope and size of the sample.

Author Contributions

Conceptualization, A.I.B. and K.S.; Methodology, A.I.B. and K.S.; Formal analysis, A.I.B.; Investigation, A.I.B.; Writing—original draft, A.I.B.; Writing—review & editing, A.I.B. and K.S.; Supervision, K.S.; Project administration, K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Final set of survey questions are presented in Table A1.
Table
Table A1. Final Set of Survey Questions.
Table A1. Final Set of Survey Questions.
Green Supplier Selection (GSS)Source
GSS1Suppliers with capabilities of eco-friendly technology and eco-design.[22,50,53,82,83,84,85]
GSS3Suppliers with Green Image/Appearance.
GSS4Suppliers with environmental management system certification (e.g., ISO 14001).
GSS9Suppliers’ ability to use environmental packaging materials (lighter, biodegradable, and non-hazardous) for their products.
GSS12Suppliers with pollution control activities.
GSS13Suppliers with waste management systems.
Green Supplier Development (GSD)Source
GSD1To provide suppliers with design features/specifications including environmental requirements for purchased products.[33,59,76,84,86,87]
GSD2To put pressure on suppliers for taking environmental measures.
GSD3To transfer employees with expertise on environmental issues to suppliers.
GSD6To train suppliers to reduce non-recyclable packaging.
GSD7To make field visits to supplier facilities to support their environmental performance development.
GSD8To provide advice to suppliers on eco-design product development.
GSD10To support suppliers in achieving their environmental targets.
GSD11To bring suppliers from the same industry together and allow them to learn about each other’s environmental problems.
Green Supplier Collaboration (GSC)Source
GSC1To cooperate with suppliers to achieve environmental objectives.[36,39,50,56,76,88,89,90]
GSC5To collaborate with suppliers during product design stage to minimize damages caused to the environment.
GSC11To cooperate with suppliers to minimize or eliminate packaging/wrapping materials along with their respective processes.
GSC12To build a return system with suppliers for recycling and reuse of used and defective products.
GSC14To cooperate with suppliers for investment recovery through resale of scrap and used materials.
Green Supplier Evaluation (GSE)Source
GSE1To control possible negative environmental effects by suppliers through monitoring with evaluation programs.[33,36,76,84,86,91,92]
GSE3To make surveys with suppliers to evaluate their environmental compliance and performance.
GSE7To evaluate eco-friendly practices by secondary suppliers (suppliers of suppliers).
Environmental Performance (EP)Source
EP1Reduction in air emission.[36,37,38]
EP2Reduction in wastewater.
EP3Reduction in solid wastes.
EP4Decrease in consumption for hazardous/harmful/toxic materials.
EP5Improve an enterprise’s environmental situation.
EP6Decrease in frequency for environmental accident.
Financial Performance (FP)
FP1Decrease in cost for materials purchasing.
FP2Decrease in cost for energy consumption.
FP3Decrease in fee for waste treatment.
FP4Decrease in fee for waste discharge.
FP5Decrease in fine for environmental accidents.

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Sustainability 15 03617 g001 550
Figure 1. Conceptual Model.
Figure 1. Conceptual Model.
Sustainability 15 03617 g001
Table
Table 1. Sample Composition.
Table 1. Sample Composition.
Number%
GenderFemale12126.59
Male33473.41
Work Period0–4 years224.84
5–9 years5912.97
10–14 years17338.02
15–19 years16736.7
20 years and above347.47
Job LevelExpert419.01
Chief11324.84
Mid-Level Manager22248.79
Senior Manager7917.36
Number of Employees1–49 persons22048.35
50–249 persons16335.82
250–499 persons4810.55
500–999 persons132.86
1000 and above112.42
SuppliedGoods/Materials33172.75
Service6113.41
Both6313.85
Environmental Management System Certification (ISO 14001 etc.)No38985.49
Yes6614.51
IndustryTextile, Ready-to-Wear, Leather9220.22
Food8719.12
Chemistry, Petroleum, Rubber, and Plastics6414.07
Tourism, Accommodation, Catering Services439.45
Woodwork, Paper, and Paper Products327.03
Other13730.11
In-Country Multiple BranchesNo38484.4
Yes7115.6
Abroad AffiliatesNo42292.75
Yes337.25
Table
Table 2. Factor Analysis for GPP.
Table 2. Factor Analysis for GPP.
Factors EFACFA
ItemStd. LoadsAVαCRAVE
Green Purchasing Practices (GPP) 0.930.910.54
Green Supplier Selection (GSS)GSS10.6920.28%0.880.880.55
GSS30.66
GSS40.76
GSS90.67
GSS120.60
GSS130.61
Green Supplier Development (GSD)GSD10.5917.51%0.900.900.53
GSD20.80
GSD30.76
GSD60.52
GSD70.73
GSD80.53
GSD100.63
GSD110.76
Green Supplier Collaboration (GSC)GSC10.7214.43%0.800.810.46
GSC50.74
GSC110.55
GSC120.68
GSC140.60
Green Supplier Evaluation (GSE)GSE10.9311.24%0.870.890.73
GSE30.94
GSE70.72
Total AV63.46%Goodness of Fit Index
KMO0.94CMIN492.830CFI0.95
Bartlett’s Test of Sphericity DF203GFI0.91
  Chi-square5638.863CMIN/DF2.428AGFI0.89
  Degrees of Freedom (df)231P0.000 *NFI0.91
  Significance (Sig.)0.000 *RMSEA0.056
N = 455; * p < 0.01.
Table
Table 3. Factor Analysis for EP and FP.
Table 3. Factor Analysis for EP and FP.
FactorsEFACFA
ItemStd. LoadsAVαCRAVE
Company Performance 0.930.940.60
Environmental Performance (EP)EP10.7337.33%0.900.900.60
EP20.74
EP30.81
EP40.79
EP50.74
EP60.70
Financial Performance (FP)FP10.8130.79%0.880.890.61
FP20.84
FP30.79
FP40.67
FP50.57
Total AV68.12%Goodness of Fit Index
KMO0.93CMIN158.520CFI0.96
Bartlett’s Test of Sphericity DF41GFI0.94
  Chi-square3236.520CMIN/DF3.866AGFI0.90
  Degrees of Freedom (df)55P0.000 *NFI0.95
  Significance (Sig.)0.000 *RMSEA0.079
N = 455; * p < 0.01.
Table
Table 4. Correlation Analyses and Discriminant Validity.
Table 4. Correlation Analyses and Discriminant Validity.
FactorsGSSGSDGSCGSE EPFPMeanSD
GSS0.74 3.970.69330
GSD0.73 *0.73 3.830.72375
GSC0.67 *0.61 *0.68 3.960.63460
GSE0.24 *0.22 *0.34 *0.85 4.060.75599
EP0.68 *0.52 *0.66 *0.28 *0.77 3.850.89972
FP0.68 *0.59 *0.60 *0.31 *0.75 *0.783.820.92415
GPP0.68 *0.70 *3.960.54074
N = 455; * p < 0.01.
Table
Table 5. Regression Analyses.
Table 5. Regression Analyses.
HypothesesModel
Summary		ANOVACoefficients
R2Sig.FBStd.
Error		βTpToleranceVIF
H10.4640.000 *392.2581.1330.060.6819.8050.000 *1.0001.000
H1a0.5420.000 *133.1640.620.070.489.2620.000 *0.392.582
H1b−0.070.06−0.06−1.2420.2150.442.283
H1c0.510.070.367.8090.000 *0.492.044
H1d0.070.040.061.6260.1050.891.129
H20.4950.000 *444.3741.2030.060.7021.0800.000 *1.0001.000
H2a0.5230.000 *123.4040.560.070.427.9810.000 *0.392.582
H2b0.180.060.142.8250.005 *0.442.283
H2c0.290.070.204.2310.000 *0.492.044
H2d0.150.040.123.4990.000 *0.891.129
H30.5560.000 *568.2160.770.030.7523.8370.000 *1.0001.000
N = 455; * p < 0.01.
Table
Table 6. Hypotheses Testing Results.
Table 6. Hypotheses Testing Results.
HypothesesResults
H1, GPP→EP, β = 0.68, p < 0.01Partially Supported
  • H1a, GSS→EP, β = 0.48, p < 0.01
Supported
  • H1b, GSD→EP, p > 0.1
Not Supported
  • H1c, GSC→EP, β = 0.36, p < 0.01
Supported
  • H1d, GSE→EP, p > 0.1
Not Supported
H2, GPP→FP, β = 0.70, p < 0.01Supported
  • H2a, GSS→FP, β = 0.42, p < 0.01
Supported
  • H2b, GSD→FP, β = 0.14, p < 0.01
Supported
  • H2c, GSC→FP, β = 0.20, p < 0.01
Supported
  • H2d, GSE→FP, β = 0.12, p < 0.01
Supported
H3, EP→FP, β = 0.75, p < 0.01Supported
H4, GPP→EP→FPSupported
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Balin, A.I.; Sari, K. The Effect of Green Purchasing Practices on Financial Performance under the Mediating Role of Environmental Performance: Evidence from Türkiye. Sustainability 2023, 15, 3617. https://doi.org/10.3390/su15043617

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Balin AI, Sari K. The Effect of Green Purchasing Practices on Financial Performance under the Mediating Role of Environmental Performance: Evidence from Türkiye. Sustainability. 2023; 15(4):3617. https://doi.org/10.3390/su15043617

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Balin, Ali Ibrahim, and Kazim Sari. 2023. "The Effect of Green Purchasing Practices on Financial Performance under the Mediating Role of Environmental Performance: Evidence from Türkiye" Sustainability 15, no. 4: 3617. https://doi.org/10.3390/su15043617

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