Exploring the relationship between unethical practices, buyer–supplier relationships and green design for sustainability

Abstract

Sustainable procurement practices have gained popularity amongst both researchers and supply chain practitioners. However, ethical practices in sustainable procurement have been the topic of discussion in the recent years. The goal of the study is to scientifically build a green procurement framework by exploring relationship between buyer–supplier relationships, unethical practices and green design for sustainability. The study was conducted considering samples from South African steel and engineering sector. Data show high rate of wastages and losses occurring at various stages of steel and engineering supply chains. Every steel and engineering company aims to reduce such losses for improving their profitability and achieve sustainability. The empirical findings show that such wastage and losses can be minimised through efficient eco-design and involvement of key suppliers at the design stage so that disassembly, recycling and reuse options not only prove successful but as well cost-effective for the organisation. Also we find that buyer deceitful practices are a strong determinant of green design for sustainability. Buyers making up a second source of supply for green components and preferring green suppliers being approved by top management is helpful for mitigation of supply risks under green procurement process. The present study is distinctive in terms of coverage and its contribution to supply chain theory.

Keywords:

• Buyer–supplier relationships
• green design
• green procurement
• sustainability
• unethical practices

View correction statement:

Corrigendum

1. Introduction

Procurement management has become a subject of strategic relevance in every organisation for its capability in building strength and reducing vulnerabilities (Spekman 1981, 1985; Rajagopal and Bernard 1993; Ordanini and Rubera 2008; Roehrich, Grosvold, and Hoejmose 2014; Weele and Raaij 2014). With the rich quality of research outputs and progress happening in the field of procurement management has lead to creation of new links and theories (Mahoney and Pandian 1992; Carter and Dresner 2001; Chen and Paulraj 2004; Pagell and Wu 2009; Bai and Sarkis 2010; Sarkis, Zhu, and Lai 2011; Tate, Ellram, and Dooley 2012; Akhavan and Beckmann 2017). Since last two decades, the popularity of sustainable procurement/green procurement research has increased significantly (Preuss 2009; Burritt and Schaltegger 2012; Dubey et al. 2013; Amann et al. 2014; Grosvold, Hoejmose, and Roehrich 2014; Hoejmose, Roehrich, and Grosvold 2014). Existing green procurement publications show that initially focus was made by researchers mainly in identifying enablers and barriers of green procurement (Walker, Di Sisto, and McBain 2008; Mudgal et al. 2010; Luthra et al. 2011; Balasubramanian 2012; Mathiyazhagan et al. 2013; Zhu and Geng 2013; Diabat, Kannan, and Mathiyazhagan 2014; Govindan et al. 2014). Simultaneously, previous researchers also considered other important dimensions such as green supplier selection, green supplier development and green supplier performance measurements (Humphreys, Wong, and Chan 2003; Jabbour and Jabbour 2009; Lee et al. 2009; Bai and Sarkis 2010; Kuo, Wang, and Tien 2010; Govindan et al. 2015; Awasthi and Kannan 2016). Recently, supply chain scholars have started talking about supplier involvement in green design and joint buyer and suppliers’ decision-making and planning on sustainability parameters for remanufacturing, disassembly and closed-loop design (Rahimifard et al. 2009; Pettersen 2015; Sheldrick and Rahimifard 2015; Moon 2017). During these collaborative processes, the buyers sometimes adopt certain deceitful and subtle practices which may influence the organisational performance (Carter 2000b). However, this area has so far been under researched and further motivated authors to fill the gap in the existing literature.

1.1. Background

In today’s highly volatile market place, every steel and engineering company is putting hard effort to reduce overheads, minimise losses and stop unnecessary expenses (Bag 2016b). Most of these companies have taken a major leap towards adoption of sustainability practices. Dubey, Bag, and Ali (2014) argued that good supplier relationships and waste reduction through joint buyer–supplier action can positively enhance business performance. They also suggested that using advanced technology and joint buyer–supplier planning and actions improve environmental performance. Therefore, it is evident that waste reduction is possible through efficient green design and collaboration with suppliers in the design stage. It is a general practice in South African engineering companies where the design of mould, reinforcements and products are discussed in details with key supplier before releasing the drawings for production. At a later stage, buyers submit the Auto CAD and IGS format drawings and as well nesting of plates to suppliers along with the purchase order for taking manufacturing action. Focus is always given towards optimisation of material usage. Moreover, simulation models are run by the design office to understand the percentage of wastages and losses and its implications on the physical and chemical properties of final products.

However, the buyer gets involved with certain deceitful and subtle practices which influence the supplier satisfaction levels and may indirectly impact the environmental and business performance (Carter 2000b).

Based on the above discussions, we develop our first research question as under

RQ1: What are the key variables to buyer unethical practices (Buyer deceitful and Buyer subtle activities)?

Green design for sustainability is a concern among steel and engineering sector and every buying organisation is focusing on collaborative relationships and key supplier developments to improve the performance in terms of design and product developments from remanufacturing and recycling perspective. However, there are multiple challenges faced by buyers during such green supplier development programmes. Therefore, we develop our second and third research questions as under to answer the call of previous researchers.

RQ2: What are the key variables to buyer supplier-relationships?

RQ3: What are the key variables to green design for sustainability?

Finally, we aim to explore the relationship between buyer–supplier relationships, unethical practices and green design for sustainability and thus develop our final research question as under:

RQ4: What is the relationship between buyer unethical practices, buyer-supplier relationships and green design for sustainability?

The rest of the paper is organised as follows: Section 2 covers the review of extant literature where we indentified and defined the key variables. Research methods used in the present study has been presented in Section 3. The data analysis and findings of this research study are presented in Section 4. Discussion has been covered under Section 5. Conclusions drawn from the study have been provided in Section 6 followed by managerial implications; limitations and scope for future research directions.

2. Literature review

Here, the review of selected papers has been done to identify the variables and further building the foundation of the empirical study.

2.1. Ethical practices

In this section, we present the review of ethics literature to understand the progress in this area. Ethical decisions are the decisions which lie within legal range and are morally acceptable to a larger community. External factors such as work, personal, professional, governmental, legal and social factors join a human being’s personality to influence the ethical decision-making process (Jones 1991).

Rest (1986) presented a theory which explains individual ethical decision-making process and this theory can easily be generalised to business situations. Therefore, we can further argue that ethics has got its theoretical foundation from the branch of sociology and psychology and can be borrowed to extend supply chain knowledge base.

Ethics is an important dimension of procurement management and forms a part of course curriculum in procurement management courses. Birou, Lutz, and Zsidisin (2016) has done a survey on procurement management courses and their training methods. It was found that basic understanding of ethical and professional principles among procurement professionals is part of the learning objectives for undergraduate procurement management courses and normally instructors utilise average 3.25% of class time covering the topic on ethics.

It is worth to understand what basically influences procurement ethical decisions. We know that values are one of the important dimensions of culture. Certain values particularly professional responsibilities are driving performance of procurement professionals. Other values such as truth and justice are also important. There is linkage of values to behaviours and it is important to understand ethical vs. unethical behaviours in terms of values and value trade-offs (Landeros and Plank 1996).

Several researchers used innovative tools to capture the data more accurately. Wood (1995) instead of using traditional open-ended questionnaire, used an interesting methodology to capture richer insights of the organisational factors which influence ethical practice. Respondents were asked to mention one or multiple situations they have experienced where ethics were a major concern. Further they were also asked to explain how they resolved that situation effectively.

Cooper, Frank, and Kemp (1997) conducted a study to firstly identify the key ethical issues faced by procurement professionals and secondly to identify various organisational factors which are helpful or presenting challenges to their working environment for acting ethically.

Carter (2000a) found that internal (organisational culture) and external (inter-organisational) factors influence ethical practices in buyer–supplier relationships.

Carter (2000b) also argued that a buyer’s deceitful activities impact supplier satisfaction negatively. Another study by Ellis and Higgins (2006) offered an evaluation of the existing codes of practice which guide ethical behaviour in procurement management. Karjalainen, Kemppainen, and Van Raaij (2009) further explored the area of maverick buying and gave us some rich insights. Another interesting work by Eltantawy, Fox, and Giunipero (2009) suggested that skills of procurement personnel and their reputation play an important role in determining performance. Therefore, ethical practices are linked with buyer–supplier relationships and supplier development performance.

2.2. Buyer–supplier relationship

In the early nineties, buyer–supplier relationship management started gaining more attention from supply chain researchers. Buyers gradually started reducing the supplier base and focused specifically on developing a handful number of strategic vendors. In that process, buyers treated key suppliers as close partners and started sharing confidential information, discussed long-term growth and developmental plans with them. This lead to enhanced mutual dependency and ultimately more satisfaction is derived from the relationship (Moeller, Fassnacht, and Klose 2006; Yazici 2013; Hooshangi, Fazli, and Mirhosseini 2016).

Burki and Buvik (2010) found that relationship period is a key factor in overcoming buying firm’s complications and also enhancing quality of relationships. Rutherford et al. (2008) argued that buyer’s like to do long-term business with those selling firms who keep promises and employs good natured sales personnel. Steward, Wu, and Hartley (2010) shows that buyer intraprenuerial ability and skills positively influence the quality of internal and external supplier relationships. Soft skills of green procurement workforce are as well essential for managing the green practices effectively.

Buyer–supplier relationship management can influence organisation performance (Revilla, Sáenz, and Knoppen 2013). Therefore, supplier selection and evaluation criteria’s must be carefully considered before finalising a supplier. Perez-Arostegui, Benitez-Amado, and Huertas-Perez (2012) recommended using reliability and quality as key criteria while selecting suppliers. It is evident that high level of satisfaction derived from buyer–supplier relationship generally leads to successful green projects.

2.3. Green design for sustainability

Green Design for re-use, recycling and sustainability is one of the greatest challenges in front of the steel and engineering industry. Hundal (2000) suggested that the first challenge is for materials which combine high performance and recyclability and secondly, the design for re-manufacturability. The design stage is the most critical for any product since it determines the impact on the product and the environment. Normally, the decisions of material selection and production process are also determined in this stage. Recycling of components reduces the requirement for virgin material and can be cost-effective solution for companies. Engineers must focus on the design decisions for disassembly and perfectly frame the remanufacturing methods to be employed by firm. Remanufacturing design follows certain basic principles and rules to make the steps easy in each of these stages. Design for disassembly should aim to reduce the amount of disassembly steps and make it easier to lower costs involved at these stages (Hundal 2000).

Therefore, the objective of green design is to use material optimally and minimise waste generation. Companies need to adopt advanced tools and green design approaches to create a sustainable future (Reuter 2011).

Today, all world-class engineering companies focuses on green design, end of life products, recycling and waste processing right from the design stage. Three interconnected cycles (Life cycle, Resource cycle and Technology cycle) that among others must be synchronised thermodynamically and technologically to reduce wastage and losses.

The life cycle involves society, customer and nature. The resource cycle involves energy, material waste, residues and renewables. The technology cycle includes economies, legislation, science, design, engineering, technology and end of life products.

Drawing office engineers and purchase engineers must have knowledge and understanding of material chemical properties, physical properties and its link to recyclable product design, thermodynamics and process knowledge (Reuter 2011). Various well-known tools such as life cycle assessment and material flow accounting can be used successfully by design engineers. This will also help in selecting and developing capabilities of green suppliers who would be involved in producing the recyclable products using advanced technologies.

Baumann, Boons, and Bragd (2002) argued using a systems perspective to optimise usage of natural resources and minimise emissions. Therefore, it is clear that green design is critical for sustainable development of steel and engineering sector.

2.4. Theoretical perspectives on unethical practices, buyer–supplier relationships and green design for sustainability

Today in this fierce market competition, manufacturers manufacture a wider variety of products to meet customer needs. In that process, manufacturers frequently change the manufacturing recipes which make the assembly and disassembly of components much more complicated. Tseng, Chang, and Li (2008) suggested for adjusted design proposal and use of less-polluting material to be used in modular design. Moreover, inefficient product design can lead to high wastage during production, low life and high costs associated with disposal (Eichner and Pethig 2001). Promoting recyclability by greening product design is a better approach to sustainability. Green design requires involvement of multiple parties (suppliers and waste management service providers) in the decision-making process (Hundal 2000). As mentioned earlier, involvement of suppliers is necessary in the green design stage. Companies must improve supplier relationships for successful green product development. Dubey and Bag (2013) found that buyer–supplier relationships positively influence environmental performance. Green design is a key driver in green procurement projects. Green design seeks to follow a number of safeguards and checks in the procurement process to positively assist in the areas such as end of products, percentage of wastage, scrap generation, toxicity levels and environmental impacts.

Readers may wonder how unethical practices are linked with buyer–supplier relationships and green design for sustainability. Buyers working in the engineering and steel sector most of the times get involved in certain deceitful and subtle practices in order to save cost of company or extract some advantages which may be beneficial for the buying firm. Buyers may even get involved in such practices simply to gain savings and score high points in their annual performance assessments. However, in this process buyers fail to see the long-term implications. The current study aims to explore this area which has so far been under researched and needs immediate attention by researchers.

Based on the above discussion, we develop the theoretical structure for the current study which is presented in Figure 1.

Figure 1. Theoretical framework. Source: Authors own compilation.

2.5. Hypotheses development

The research hypotheses have been developed based on the review and conversation with industry professional for further empirical testing.

Green supplier management involves managing key suppliers effectively with the aim of achieving green programme goals. It involves motivating and developing the capability of suppliers for increased innovation. In such process, a collaborative relationship is developed between the buyer and supplier organisation which leads to development of green design for components and products which can be easily recycled and reused at end of its life. Literature has evidences that most of the engineering companies face difficulties in disassembly and handling during reverse logistics process which can be eliminated by preparing a blueprint right at the product design stage based on the inputs from design engineers and expert suppliers (Pujari, Wright, and Peattie 2003; Pujari 2006; Zhu and Sarkis 2007; Ageron, Gunasekaran, and Spalanzani 2012; Green et al. 2012).

We therefore hypothesise:

H₁: Green supplier management (GSM) positively influences Green design for sustainability (GDS).

Collaborative relationships between buyer and supplier organisations play a critical role in successful development of green design which is ultimately sustainable. We argue that a good relationship is the basis for development of trust and bonding between buyer and suppliers which is a result of enhanced communication and transparency between both parties. Previous studies has shown that buyers involving key suppliers in product design, development and joint planning activities achieve the green results sustainably (Kannan and Choon Tan 2006; Vachon and Klassen 2006; Carter and Rogers 2008; Hollos, Blome, and Foerstl 2012).We therefore hypothesise:

H₂: Buyer-supplier relationships (BSR) positively influence Green design for sustainability (GDS).

Buyer deceitful activities involves using an alternate supply source to manage supply risks and also using top management approved suppliers for making buying decisions. In today’s dynamic business scenario, it is essential to make up a second source of supply for greener products which some existing suppliers may not like for fear of losing business. However, buyers focusing on developing alternate source simultaneously manage risks in a better fashion. Also suppliers selected based on certain criteria’s set by top management always yields better results. Therefore, BDP can actually result into manage green design in a sustainable manner.

The below hypothesis has been framed based on opinion of experts from industry.

H₃: Buyer deceitful practices (BDP) positively influence Green design for sustainability (GDS).

Supplier development is a long exercise where time, investment of fund and resource utilisation is high initially but yields superior results in the long run. Through supplier development programmes continuous inputs are provided to selected suppliers and performance is monitored over a period of time. The selected suppliers develops green components by following the buying firm’s standard operating procedure (SOP) and quality assurance process (QAP) and as well implement the code of conduct which is essential for long term sustainability. Buyer firm conducts joint planning with these suppliers and through regular visits ultimately develops the blueprint successfully. These leads to gaining of more confidence and developing strong bonding which helps in the improving design and maintaining business confidentiality which is sustainable in the long term (Pujari, Wright, and Peattie 2003; Pujari 2006; Zhu and Sarkis 2007; Carter and Rogers 2008; Ageron, Gunasekaran, and Spalanzani 2012; Green et al. 2012).

We therefore hypothesise:

H₄: Supplier development (SD) positively influences Green design for sustainability (GDS).

Buyers sometime involves in subtle practices when they know that it is going to benefit the firm without causing much harm to existing suppliers. It may happen that some of the suppliers in the development stage may perform better than existing suppliers. So, it may be helpful to provide them with specifications which will help them in developing the components quick and of much better quality. We therefore argue that such practices lead to improved green design for sustainability. Also it is seen that some sales man are extraordinary in terms of maintaining transparency and critical information sharing of their internal operations which help buyers with better visibility and improved planning.

We therefore hypothesise:

H₅: Buyer subtle practices (BSP) positively influence Green design for sustainability (GDS).

3. Research methods

This section comprises the sampling plan and survey strategy adopted for this study.

3.1. Sample and survey description

The survey instrument included dimensions of green supplier management, buyer–supplier relationships, supplier development, unethical practices and green design for sustainability.

The procurement managers of steel and engineering industry were targeted to involve in the survey. We considered this particular industry basically for two reasons. Firstly, the steel and engineering sector is the backbone of country’s economic development. Secondly, based on Reconstruction and Development Programme the steel and engineering industry has recently adopted various sustainable practices which would be interesting to study.

As per our sampling strategy, the data were collected applying the technique of convenience sampling plan. Email survey was used to send the initial cover letter along with questionnaire to all potential respondents. The contact details were compiled from Steel and Engineering Industries Federation of Southern Africa database. To avoid any problems in the main survey, we conducted a pilot survey among 30 respondents and then found that five items were similar in nature and may pose multicollinearity problems. Therefore, these items were removed from the questionnaire before the commencing final survey. We emailed questionnaire to 250 South African steel and engineering firms. The final survey took almost two and half months for completion and we received 120 completed questionnaires, which means that the effective response rate for our survey was about 48% (Table 1).

Table 1. Sample overview.

Type of companies	Annual turnover	n	Percentage
Exempted micro enterprise	<R10 million	10	8.33
Qualifying small enterprise	<R50 million	45	37.5
Generic	>R50 million	65	54.16

4. Data analysis and findings

In the first step, we focused on data cleaning procedure. Next exploratory factor analysis (EFA) was applied. Finally, the output of EFA was used as an input in the hierarchical multiple regression analysis. Refer Figures A1 and A2 under Appendix 1.

4.1. Data cleaning

The data were initially checked for any missing frequency. Also authors checked normality and existence of any outlier in the data which was not found and authors further proceeded with the data analysis.

4.2. Reliability test

The reliability test output (Table 2) shows that the instrument used in the study is reliable for study.

Table 2. Reliability output.

Cronbach’s alpha	No. of items
.789	28

We also present the Item total statistics in Table 3.

Table 3. Item total statistics.

Variables	Scale mean if item deleted	Scale variance if item deleted	Corrected item total correlation	Cronbach’s alpha if item deleted
VAR00001	59.9000	94.746	−.131	.817
VAR00002	57.1750	101.843	−.547	.819
VAR00003	57.1667	102.728	−.616	.820
VAR00004	57.2500	101.483	−.519	.818
VAR00005	60.0833	92.329	−.031	.806
VAR00006	57.1250	91.203	.103	.791
VAR00007	57.6333	94.856	−.149	.802
VAR00008	60.3583	83.660	.618	.770
VAR00009	60.4667	83.041	.727	.766
VAR00010	59.9417	83.988	.568	.771
VAR00011	60.3500	83.204	.661	.768
VAR00012	60.4917	84.571	.691	.770
VAR00013	58.8083	89.047	.096	.799
VAR00014	60.5500	83.544	.793	.767
VAR00015	60.4917	82.823	.739	.766
VAR00016	60.5583	82.400	.865	.763
VAR00017	60.5333	83.377	.766	.767
VAR00018	60.4583	83.897	.709	.768
VAR00019	60.4583	84.334	.646	.770
VAR00020	58.8667	87.192	.169	.794
VAR00021	59.0583	84.576	.297	.785
VAR00022	60.0250	86.462	.446	.778
VAR00023	59.7417	83.504	.477	.774
VAR00024	60.1250	84.194	.548	.772
VAR00025	60.1833	83.546	.662	.769
VAR00026	60.2083	83.360	.673	.768
VAR00027	60.1750	82.314	.723	.765
VAR00028	60.1917	82.156	.733	.765

4.3. Exploratory factor analysis

Here, exploratory factor analysis (EFA) was conducted to reduce the number of dimensions and extract relevant factors.

In Table 4, the results of KMO and Bartlett’s test is presented which shows that value is high (KMO = .889) and suitable for factor analysis.

Table 4. KMO and Bartlett’s test output.

Kaiser–Meyer–Olkin measure of sampling adequacy		.889
Bartlett’s test of sphericity	Approx. chi-square	3847.375
	df	378
	Sig.	.000

EFA was run in SPSS 22.0 version using PCA method and the summary is presented in Table 5. In the EFA, only six factors were retained after conducting varimax rotation. The first factor explains 19.76% of variance and total cumulative variance explained by six factors is accounting to 79%.

Table 5. Total variance explained.

Component	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %	Total	% of variance	Cumulative %
1	12.127	43.311	43.311	12.127	43.311	43.311	5.535	19.769	19.769
2	4.999	17.852	61.163	4.999	17.852	61.163	5.395	19.268	39.038
3	1.647	5.882	67.045	1.647	5.882	67.045	5.012	17.900	56.938
4	1.218	4.351	71.396	1.218	4.351	71.396	2.742	9.791	66.729
5	1.146	4.093	75.489	1.146	4.093	75.489	2.086	7.448	74.178
6	1.045	3.732	79.221	1.045	3.732	79.221	1.412	5.043	79.221
7	.874	3.120	82.342
8	.656	2.343	84.685
9	.597	2.133	86.818
10	.522	1.864	88.682
11	.405	1.448	90.131
12	.352	1.258	91.389
13	.329	1.175	92.563
14	.308	1.102	93.665
15	.278	.992	94.656
16	.257	.917	95.573
17	.234	.837	96.411
18	.197	.703	97.114
19	.179	.639	97.753
20	.156	.556	98.309
21	.130	.465	98.774
22	.100	.357	99.132
23	.081	.291	99.422
24	.053	.188	99.611
25	.050	.180	99.791
26	.029	.104	99.895
27	.022	.080	99.975
28	.007	.025	100.000

The rotated component matrix has been presented in Table 6.

Table 6. Rotated component matrix.

	Component
	1	2	3	4	5	6
VAR00025	.922
VAR00026	.918
VAR00027	.870
VAR00028	.866
VAR00024	.701
VAR00023	.574
VAR00003		−.848
VAR00002		−.846
VAR00004		−.810
VAR00020		.771
VAR00021		.724
VAR00019		.563
VAR00011			.811
VAR00012			.746
VAR00010			.720
VAR00016			.655
VAR00018			.643
VAR00014			.612
VAR00017			.582
VAR00015			.547
VAR00009			.508
VAR00001				.852
VAR00005				.835
VAR00013				−.662
VAR00022					.851
VAR00008					.531
VAR00006						.856
VAR00007						.750

The final factors extracted and retained from EFA are presented along with sources in Table 7.

Table 7. Final factors.

Factors	Variables	Source
Green supplier management (GSM)	Our purchasing department motivates suppliers for increased innovativeness	Walton, Handfield, and Melnyk (1998), Eltayeb and Zailani (2010), Reuter et al. (2010), Zhu, Dou, and Sarkis (2010), Govindan et al. (2015), Bag (2016a) and Roehrich, Hoejmose, and Overland (2017)
	Our purchasing department cooperates with suppliers for environmental objectives
	Our purchasing department performs environmental audit for suppliers’ internal management
	Our purchasing department actively involves in eco-labelling and supplier evaluation
	Our purchasing department seeks suppliers with low energy consumption
	Our purchasing department instructs suppliers to focus on continuous waste reduction programmes
Green design for sustainability (GDS)	Active participation of purchasing department in designing of components for easy disassembly	Carter and Jennings (2004), Carter (2005), Bakker et al. (2010), Liu, Leat, and Smith (2011), Dubey et al. (2013), De Pauw, Kandachar, and Karana (2015), Bag and Gupta (2017) and Habidin et al. (2017)
	Active participation of purchasing department in designing of components for recycling and reuse at end of life cycle
	More costs are shared equally in joint green design process when compared to others
Buyer–supplier relationships (BSR)	Buyer involves key suppliers in innovative product developmental activities	Carr and Pearson (1999), Shin, Collier, and Wilson (2000), O’Toole and Donaldson (2002), Carter and Jennings (2004), Carter (2005), Narasimham, Venkatasubbaiah, and Avadhani (2012), Giannakis (2012) and Dubey et al. (2013)
	Buyer conduct joint planning and direct communication with key suppliers
	Senior management interacts with key suppliers on important issues
	Company shows loyalty to key suppliers
	The long-term profitability of this relationship is higher in comparison to alternatives
	Quality is our first critical parameter in supplier selection process
	Buyer–supplier collaboration produce increased flexibility
	We depend on few specialist suppliers
	We sign annual contracts with suppliers who have demonstrated superior performance
Buyer activities: deceitful practices (BDP)	Buyer develops up a alternate source of supply to manage risks	Carter (2000b)
	Buyer gives preference to suppliers approved by top management
Supplier development (SD)	Our purchasing department through joint action with key suppliers contributes to reduction of packaging material	Hahn, Watts, and Kim (1990), Bai and Sarkis (2010), Fu, Zhu, and Sarkis (2012), Blome, Hollos, and Paulraj (2014) and Akman (2015)
	Buyer strive to establish long-term relationship for development of specialist suppliers
Buyer activities: subtle practices (BSP)	Buyer permit personalities of the suppliers’ salesperson to influence decisions	Carter (2000b)
	Buyer provide specifications that may help a particular supplier

The output of EFA has been used as an input in the hierarchical multiple regression analysis.

4.4. Hierarchical regression analysis

We have used hierarchical regression analysis which is basically a way to see if our variables of interest explain a statistically significant amount of variance in our outcome variable after accounting for all other variables. There are certain advantages of using this technique and has been used extensively by past researchers in operations management research (Boyer et al. 1997; Rosenzweig, Roth, and Dean 2003; Droge, Jayaram, and Vickery 2004; Zhu and Sarkis 2004; Dubey, Gunasekaran, and Ali 2015).

The summary of the regression output is presented in Table 8.

Table 8. Model summary.

Model	R	R square	Adjusted R square	Std. error of the estimate	Change statistics					Durbin–Watson
					R square change	F change	df1	df2	Sig. F change
1	.392^a	.153	.139	.89174	.153	10.594	2	117	.000
2	.663^b	.439	.415	.73521	.286	19.375	3	114	.000	1.687

^a Predictors: (Constant), BSP, BDP.

^b Predictors: (Constant), BSP, BDP, SD, BSR, GSM.

We need to see if the model is able to predict the outcome it wants to predict. We check the model summary in Table 8 first. We see that in block 1 the variables (BSP, BDP) we want to control for and block 1 variables account for 15% (R square = .153) of the variability in the outcome (GDS). Now, we look at block 2 variables (BSP, BDP, SD, BSR, GSM) which are actually the predictor variables. We can see that the model as a whole now explains 43.9% (R square = .439) of the variability in green design for sustainability (GDS). It is important to know that the second R square value include all the variables from both blocks and not just those in the second block. So, now we can incorporate all five variables. We are controlling for two variables in the first block and seeing what effects all variables have together after they are controlled. So, to find out how much of this overall variance is explained by our predictor variables, we are interested in SD, BSR and GSM. After the effects of BDP and BSP have been removed, we can look into column label R square change under line marked model 2. The R square change value is .286, so this means that our predictor variables or independent variables we are interested in explain an additional 28% of the variance in the outcome. We can see that the model is statistically significant predictor of GDS. We have again done controlling of two confounding variables and then we have added three of our predictor variables and now we find that the model is statistically significant of prediction of outcome variables.

It is desired that value of Durbin–Watson should lie between 1.5 and 2.5 (Hair et al. 1998). In this case, the value of Durbin-Watson is coming 1.687 which means that the error deviation is uncorrelated.

Now if we look at the ANOVA Table 9 and see model 2 Sig. value which again tells us how the model as a whole is able to predict including our five variables (BSP, BDP, SD, BSR, GSM).

Table 9. ANOVA summary.

Model		Sum of squares	df	Mean square	F	Sig.
1	Regression	16.849	2	8.424	10.594	.000^a
	Residual	93.039	117	.795
	Total	109.888	119
2	Regression	48.268	5	9.654	17.859	.000^b
	Residual	61.620	114	.541
	Total	109.888	119

^a Predictors: (Constant), BSP, BDP.

^b Predictors: (Constant), BSP, BDP, SD, BSR, GSM.

Next step is value weighing for each of the independent variable so as to find out how well each of the variables can contribute in the final model. We need to look into the coefficient Table 10. We shall focus on Model 2 which summarises the results of all the variables entered into the equation. Even when the effects of BSP and BDP have been statistically controlled for and if we look at Sig, F change we can see that it is statistically significant. The Sig. F change is less than .05 which indicates to us that the addition of these two predictor variables now has a statistically significant contribution to predicting the outcome.

Table 10. Coefficients.

Model		Unstandardised coefficients		Standardised coefficients	t	Sig.	Correlations			Collinearity statistics
		B	Std. error	Beta			Zero-order	Partial	Part	Tolerance	VIF
1	(Constant)	3.261	.583		5.590	.000
	BDP	.269	.067	.344	4.040	.000	.330	.350	.344	.996	1.004
	BSP	−.321	.130	−.211	−2.470	.015	−.188	−.223	−.210	.996	1.004
2	(Constant)	2.063	.536		3.847	.000
	BDP	.200	.064	.256	3.102	.002	.330	.279	.218	.723	1.383
	BSP	−.222	.108	−.146	−2.053	.042	−.188	−.189	−.144	.979	1.022
	GSM	−.326	.143	−.248	−2.271	.025	.077	−.208	−.159	.413	2.419
	BSR	1.159	.175	.702	6.628	.000	.515	.527	.465	.438	2.284
	SD	−.092	.133	−.062	−.686	.494	.246	−.064	−.048	.598	1.673

^a Dependent Variable: GDS.

The VIF value is less than 5 (Table 10) which means that output model is not suffering from multicollinearity effect.

If we look at the Sig. column in Table 10, only two variables (BDP and BSR) which make a unique statistically significant contribution i.e. the Sig. values are less than .05. BSP, GSM and SD do not make a statistically significant contribution. If we look at the standardised coefficients (Beta value) column and look at each of our variables, then we find that only two variables (BDP = 3.102 and BSR = 6.628) make the most contribution to the model.

Therefore, the final Regression Model can be framed as(1)

5. Discussion

The empirical findings show that high wastage and losses can be minimised through efficient eco-design and involvement of key suppliers at the design stage so that disassembly, recycling and reuse options not only prove successful but as well cost-effective for the organisation. This study is supported by Resource Dependence theory where it suggests that firms within the supply chain network should coordinate and collaborate to achieve superior performance (Sarkis, Zhu, and Lai 2011). From the RDT viewpoint, buyer and supplier relationships are important links for firms to reduce the uncertainty surrounding their operating environment (Carter and Rogers 2008). We argue in light with Attaran and Attaran (2007) that trust building and ethical behaviour is necessary to develop good business relationship with suppliers and integrate them with the buying organisation system. This will ensure collaborative planning, forecasting and replenishment which will provide greater level of visibility and flexibility in operations. It is necessary to develop specialist suppliers and involve them in the green design stage for finalising blueprint for remanufacturing and disassembly (Ayres, Ferrer, and Van Leynseele 1997; Charter and Gray 2008; Subramoniam, Huisingh, and Chinnam 2009; Gunasekaran and Spalanzani 2012).

Vachon (2007) suggested that environmental collaboration with suppliers is positively associated with greater investment in pollution prevention technologies. Previous seminal studies by Carter and Carter (1998), Bowen et al. (2001), Vachon and Klassen (2006) and Dubey et al. (2013) also suggest that buying organisation environmental and logistical collaboration with suppliers positively influences adoption of green purchasing practices.

The study by Eltayeb, Zailani, and Ramayah (2011) and Green et al. (2012) also supports that environmental collaboration and monitoring practices among supply chain network partners’ results to improved environmental performance and organisational performance. However, the new facts which emerged from this study are that buyer deceitful practices are a strong determinant of green design for sustainability. Buyers making up a second source of supply for green components and preferring green suppliers being approved by top management is helpful for mitigation of supply risks under green procurement process.

6. Conclusion

The study commences with brief review of the literature on ethical practices followed by buyer–supplier relationship and finally on green design for sustainability. The review resulted in identifying important variables which were further used in developing the links and instrument for gathering data among survey respondents. Author has specially chosen this topic to shed some light in the unexplored area and the final analysis shows directions for further research. The key finding is that buyer–supplier relationship is a strong determinant of green design for sustainability. Collaboration and coordination with key suppliers is required at the design stage to finalise the green design and further minimise the wastage and environmental impact. The second finding which is very interesting indeed is that buyer deceitful practices (BDP) positively influence green design for sustainability in South African context. Here, the items considered under BDP are buyers developing a second source of supply and buyers giving first choice of suppliers who are approved by top management.

However, buyer subtle practices negatively influence the green design for recycling and sustainability. Companies can avoid such unethical practices and confusion by framing a green procurement policy and including the ethical decisions and guidelines for buyers to follow the document strictly. Companies must provide a toll-free number and email id on the website for external stakeholders to report complaint of any unethical cases faced by them at any point of time. Moreover, companies must develop a healthy culture by breaking the silos within departments and also involve employees in continuous education and training programmes. Companies must use environmental information systems to capture all data related to green product developments; wastage percentage; basis of supplier selection; green supplier performance review and environmental audits of suppliers. Finally, supplier management must be done by buyers without purchasing decisions being influenced by any other departments. The supplier selection must be done by buyers based on all the parameters including green manufacturing capability and innovativeness. The current findings corroborate with previous study conducted by (Bag 2016a) where we find that supplier integration leads to improved green procurement performance by promoting trust and developing flexibility in the system. We reiterate that building trust and good relationships with key suppliers is essential for successful green design and improving and green procurement performance which will ultimately enhance business performance. However, buyers must adopt certain deceitful practices such as developing alternate supply sources for green components without depending on a single source and only use approved green suppliers for different green projects for achieving sustainability.

6.1. Managerial implications

The study provides rich insights for supply chain managers involved in green decisions and green capacity building of green suppliers. The key to success in development of sustainable design is collaboration with specialist suppliers and constantly put effort in innovation activities. However, the key learning from the study is that buyers must develop alternate suppliers for managing supplier risks and uncertainty. Senior management involvement makes the green supplier selection and evaluation process rigorous and ultimately more effective. With their rich experience perfect decisions are made during selection and evaluation process of green suppliers. Therefore, buyers must always involve key suppliers in green programmes recommended by top management for better results. Buyers must be careful while making any ethical decisions and must check the procurement policy to avoid any mistakes. Purchasing managers must frame procurement policy which should be revised from time to time depending on the changes in organisation goals. Junior buyers must be educated and trained on a continuous basis to develop the soft skills for managing purchasing function decently. Managers must define the key performance areas of all buyers in purchase department which will provide them visibility for effective annual performance measurement.

6.2. Limitations and future research directions

In the present study, we basically find few limitations such as unethical practice dimensions considered were limited to certain specific deceitful and/or subtle practices; secondly the sample size considered were small (120 responses) in size and thirdly the usage of cross-sectional data. Therefore, generalisability of results for other sectors may not possible at this stage. This research is just the beginning of exploration in the direction of unethical practices and green procurement. More high quality research output is required in this area to explore new links and extend the theory.

We further suggest future researchers to focus in the following areas, such as:

•	Use fuzzy TISM and fuzzy DEMATEL techniques to study the interrelationships among the variables considered in our study.
•	Consider additional variables which we might have missed in this study and build more robust model.
•	The model can also be examined under moderating effect of procurement structure, product complexity and intellectual capital.
•	Study can be conducted to understand how unethical practices influence operational flexibility and creativity performance of green suppliers.
•	Explore the links between maverick buying and green procurement.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes on contributors

Surajit Bag is currently pursuing his advanced research program under University of Johannesburg in the area of Technology and Operations Management. He completed his PhD in 2014 in the discipline of Logistics and Supply Chain Management from University of Petroleum & Energy Studies. He has attended Management Development Program in Multivariate data analysis from Indian Institute of Technology, Kharagpur. He has also attended Management Development Program in Operations Research from Banaras Hindu University. He has trained in Case Study Teaching from Indian Institute of Management, Calcutta. He has also trained in Total Quality Management from Indian Institute of Technology, Kanpur. He has got more than eight years of industry experience in procurement and logistics function. His areas of research interest are Industrial supply chain automation, Buyer-Supplier relationship, Sustainable supply chain management, Supply chain risk management, Humanitarian logistics strategy and Business excellence. He has attended several National and International conferences and has published more than 50 papers in top SCM journals. His articles are in the spotlight with 135 citations, h-index 7 and i10-index 5 (Source: Google scholar). He is the proud recipient of ‘AIMS-IRMA Young Management Researcher Award 2016’ for his significant contribution towards management research. He is an Editorial board member of International Journal of Applied Logistics, IRJ Science and Amity Journal of Operations Management. He is also serving as the International Advisory Board Member of International Journal of Social Ecology and Sustainable Development. He is a regular reviewer of several prestigious journals such as JCP, ANOR, JHLSCM, VISION, IJIS, IJAL, IJORIS and IJPM. He is a life member of professional bodies such as Indian Rubber Institute (IRI), Operational Research Society of India (ORSI), Society of Operations Management (SOM), AIMS International and Asian Council of Logistics Management (ACLM).

Shivam Gupta is an assistant professor in the domain of Information Systems at IIM Sambalpur. Prior to joining IIM Sambalpur, he was working as postdoctoral fellow at South University of Science and Technology of China (SUSTC), and previously he has been a Postdoctoral Researcher at Freie Universität Berlin, Germany. He has pursued his PhD research from Indian Institute of Technology (IIT) Kanpur, India. He has been working in the domain of Technology and Operations Management and has published research papers in reputed journals. He has been the recipient of “Best Paper Award” at the Administrative Sciences Association of Canada (ASAC) conference at Halifax, Canada 2015. He has been reviewing for some of the reputed journals like ANOR, IJPR, IJLM, GJFSM, JOCM and IJQRM.

Arnesh Telukdarie is a senior lecturer in the domain of Technology and Operations Management under University of Johannesburg, South Africa. His publication appears in reputed journals like Journal of Cleaner Production and Industrial and Engineering Chemistry Research. His areas of research interest are Manufacturing systems, Systems Engineering, Enterprise Optimisation and Industry 4.0 IIoT.

Appendix 1

See Figures A1 and A2.

Figure A1. Normal P-P plot. Source: SPSS output.

Figure A2. Scatter plot. Source: SPSS output.