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O
PERATIONS STRATEGY
&
SUSTAINABILITY PRACTICES
Master Thesis for MSc. Technology Management
MSc. Technology Management University of Groningen
Faculty of Economics and Business Jerry van de Velde
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Contents
Abstract ... 3
1. Introduction... 4
2. Theoretical background and research questions ... 8
2.1. Sustainability as independent variable ... 8
2.2. Competitive priorities – Low Cost ... 9
2.2.1. Competitive priorities – Quality, Flexibility and delivery ... 11
2.3. Concluding the literature review ... 11
2.3.1. Research questions ... 11
2.3.2. Conceptual model ... 12
3. Research Methodology ... 13
3.1. Questionnaire design and measures... 13
3.1.1. Cronbach Alpha - Social sustainability... 16
3.1.2. Cronbach Alpha - Environmental sustainability ... 16
3.1.3. Cronbach Alpha - Flexibility... 16
3.1.4. Cronbach Alpha - Quality ... 16
3.1.5. Cronbach Alpha - Delivery ... 17
3.1.6. Cronbach Alpha – Low cost ... 17
3.2. Participants ... 17
3.3. Sample and data collection ... 19
3.4. Methodology ... 19
4. Data analysis and results ... 21
4.1. Performing the hierarchical cluster analysis ... 21
4.2. The operational strategy configuration characteristics of the clusters ... 22
4.3. Computing the environmental sustainability and environmental sustainability variables ... 23
5. Discussion and conclusion ... 24
6. References ... 30
7. Appendix ... 33
7.1. Appendix – Analysis of clusters and underlying characteristics ... 33
7.2. Cronbach Alpha - Social sustainability ... 35
7.3. Cronbach Alpha - Environmental sustainability ... 36
7.4. Cronbach Alpha - Flexibility ... 38
7.5. Cronbach Alpha - Quality ... 39
7.6. Cronbach Alpha – Delivery ... 40
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Abstract
Purpose - The purpose of this paper is to analyze the complex interrelationships among competitive priorities of operations strategy and the degree of sustainability practices within manufacturing firms.
Design/methodology/approach - A survey was sent to a sample of managers in manufacturing firms in West-Europe. Data were analyzed using SPSS to provide answers to several research questions.
Findings - Results show that the combination of all priorities might have a relation to sustainability. The combination of the completive priorities showed similar scoring results for each cluster as the scoring of both environmental and social sustainability.
Research limitations/implications - One of the main limitations of this study is the use of a small set of data (33). The main contribution of the paper is to analyze how empirical combinations of traditional operations strategy priorities within manufacturing companies align with their environmental and social sustainability implementations. As further research, the author suggest the replication of this study on a larger scale and to challenge the individual relations that are present in the current literature.
Practical implications - This study provides guidance to managers in the pursuit of implementing sustainability within their firms, given their current operations strategy configurations. For example, it shows that when operations strategy is focused on quality there is likely to be a good match with social sustainability. Indicating that either the current strategy needs to be adjusted or different sustainability needs to be pursued.
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1. Introduction
Sustainability has been a trending topic in the past decade, which can been seen all over the media. Recent examples show that various manufacturing companies are either applauded for their efforts or reprimanded due to their sustainability practices that are considered as not acceptable in the eyes of both the public and governments alike. Sustainability can be defined as the combination of both environmental and social sustainability. Environmental sustainability is defined as consuming natural resources at a rate below the natural regeneration or consuming a substitute, generating limited emissions, and not being engaged in activities that can degrade the ecosystem (Kleindorfer et al., 2005). For example, Siemens has been applauded for its focus on environmental sustainability practices for clean energy and intelligent infrastructure products. These products, bundled under the Siemens’ Environmental Portfolio has led to a revenue for 42 percent of its total business in 2012 and rose to €33 billion. These products and services are considered to be the company’s fastest-growing unit (Accenture, 2014).
On the other hand, there is Volkswagen which has been reprimanded. Volkswagen long considered as a leader for sustainability practices within the automotive industry, which due to their violation of environmental sustainability has not only led to drops in stock prices and enormous fines but also has put the whole automotive industry under increased scrutiny (CNBC, 2015; Bloomberg, 2015; Forbes 2016). Social sustainability is defined as actively supporting the preservation and creation of skills as well as the capabilities of future generations, promoting health and supporting equal and democratic treatments that allow for good quality of life both inside and outside of the company context (McKenzie, 2004). Social sustainability issues are also of current interest in the media, a well-known example is that of Apple which took quite a hit in their stock prices (SeekingAlpha, 2012) due to their association to Foxconn, whom with their labor practices had shown a lack of social sustainability within their company (The New York Times, 2012).
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world via marketing and even participate in global rating initiatives such as those of the Global 100 by Corporate Knights (Forbes.com, 2013; ManufacturingGlobal.com, 2015). Apart from the sustainability initiatives that can be seen at the companies within these rankings, the increased awareness for sustainability within companies is also reflected in other facets within the companies themselves, such as the increased attention for reporting on sustainability practices by the companies within their annual reporting. The research by KPMG that focuses on sustainability reporting within annual reporting has shown that reporting of sustainability by companies has increased from 12 percent in 1993 to over 64 percent in 2011 (KPMG, 2011). The previous examples and trends show that the increased attention and awareness for sustainability directly impacts companies in terms of their brand and their market value. As such, the attention and awareness of sustainability forces companies to continuously evaluate and implement their sustainability practices to an acceptable level to cover the associated risks to their brand and market value and to increase their competitive advantage over competitors (Nidumolu et al., 2009; Bonn and Fisher, 2011; Orlitzky et al., 2011).
As the previous examples and the various initiatives show, manufacturing companies are now more often confronted with the demands for sustainability. Some companies can cope with these demands better than others, this research argues this ability to cope with these demands originates from the strategic viewpoint the companies have towards sustainability. The strategic viewpoints of manufacturing companies determine how managers allocate time and resources within their processes. The priority given to environmental and social sustainability must be placed alongside, and balanced against, the traditional operations strategy objectives of cost, quality, delivery, flexibility (Galeazzo and Klassen, 2015; Boyer and Lewis 2002). The competitive priorities are considered as the key decision variables within strategy development that in turn structure the capabilities of a company to increase its competitive advantage (Hayes and Wheelwright 1984; Ward, McCreery, Ritzman, & Sharma 1998).
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priorities. The first stream of theories suggest that the underlying competitive priorities of operations strategy could be extended with innovation, service and even sustainability itself as a priority (Schmenner and Swink 1998; Ward, McCreery, Ritzman, and Sharma 1998; de Burgos Jiménez and Lorente, 2001). However, there has been little empirical research that validates these newer priorities and sometimes even prove contradictory to each other. The other stream, which looked to integrate sustainability into the 4 traditional competitive priorities and to investigate new relations between the variables, led to validating various established configuration models while considering how both environmental and social sustainability can be integrated into the operations strategy itself. The findings within this stream of research indicate that while operations strategy can provide positive support for sustainability, it is not yet a sufficient condition to successfully implement sustainable practices (Longoni and Cagliano, 2014; Galeazzo and Klassen, 2015; Russell and Millar, 2014; Atkin, Gilinsky and Newton, 2012). Hence, indicating that there is no substantial evidence to suggest that sustainability can be indeed be integrated within current operations strategies priorities.
Although some conceptual studies suggest to extend the priorities in some way, empirical research and strategy theories consistently stress the four basic capabilities (Schmenner and Swink 1998; Ward, McCreery, Ritzman, and Sharma 1998; Boyer & Lewis, 2002). Similarly, there is general agreement that the effectiveness of an operations strategy is determined by the degree of consistency between emphasized competitive priorities and corresponding decisions regarding operational structure and infrastructure (Leong, Snyder, and Ward 1990). As such, operations strategy is defined within the research as the combination of the traditional competitive priorities of cost, quality, flexibility, and delivery.
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will be called operations strategy configurations) shape the operations strategy within a company and can influence the implementation of sustainability. By empirically exploring these combinations, this study aims to provide insights in which operations strategy configurations can be found in manufacturing.Also, it extends the above mentioned literature by researching how different operations strategy configurations relate to the degree of implementation of environmental and social sustainability within manufacturing companies. Any relations that can be identified might offer more insight into the potential integration or extension of operation strategies and sustainability that might have been overlooked previously. For example, a focus on the quality priority might correlate positively with social sustainability while on the other hand the effect positive might be diminished as a company also incorporates low cost priorities. This study, which focuses on the operations strategy configurations, could potentially help managers by assessing the viability of pursuing a form sustainability within their company, given the strategy that is in place.
Objective & Research question
To summarize, although current literature suggests that there are relations between a single competitive priority and a sustainability variables, it is noted that the possible relation between the implementation of (both environmental and social) sustainability and its connection to operations strategy configurations (consisting out of a specific combination of competitive priorities) is not clearly identified within the current literature. As such, the current literature shows no direct indication as how the implementation of sustainability practices are influenced by the combinations of various competitive priorities a company already pursues. To address this caveat this study explores how the degree to which sustainability practices are implemented might differ for various operations strategy configurations and thus in turn possibly influence the potential a company has for successfully pursuing sustainability practices differently.
Therefore, the objective of this study is to provide insight into which combinations of competitive priorities (operations strategy configurations), are related to what degree of sustainability implementations within manufacturing companies. This leads to the following research question:
8 Structure
This thesis is organized as follows: Firstly the theoretical framework will be described (chapter two). Within this chapter literature on Operations Strategy and Sustainability are described. In the theoretical background section the sub research questions and model are formulated. These questions will be used to answer the main research question that was introduced in the previous chapter. Finally, a conceptual model is presented for analytical purposes. In chapter three the research design is elaborated upon. Chapter four provides an overview of the data analysis, operationalization of the variables, and the validity and reliability of the study are analyzed. Due to the exploratory character of this study the cluster analysis is used to explore the relation between the variables, results of the analyses are described in last section. Lastly the concluding chapter (five) entails the discussion and conclusion of the results.
2. Theoretical background and research questions
This section will further elaborate on the theoretical background of the relations between the concepts that are used within this research; those of operations strategy and its underlying competitive priorities, as well as the currently identified relations within the literature between these competitive priorities and environmental and social sustainability. To this end, during the following conceptualization of the literature review, the identified relations between the individual variables that are present in the current literature (LH1-6) are used to develop the research questions for this study (Q1-4). As opposed to the current literature, the research questions of this study do not focus on the individual relation between a competitive priority and sustainability but focus on the relation between the combination of the competitive priorities (defined as operations strategy configuration) and sustainability. Thereafter the research model is introduced.
2.1. Sustainability as independent variable
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advantages or increased performance. The notion that (sustainable) technology could lead to creating competitive advantage in itself is also supported in other literature by (Shrivastava, 1995; Christmann 2000). Furthermore, various research propose that environmental practices can be naturally implemented as part of operations strategy and in turn have a positive effect (Longoni and Cagliano, 2014; Galeazzo and Klassen, 2015; Russell and Millar, 2014; Atkin, Gilinsky and Newton, 2012). However, Galeazzo and Klassen (2015) also noted that there are potential trade-offs between the environmental and social sustainability practices if they are implemented simultaneously: Managers reported a perceived trade-off between strategies that focus on sustainability aspects versus one that emphasizes cost. Where Longoni and Cagliano (2014) have tried to determine how sustainability can be integrated within the various traditional operations strategies that are established within the literature. In addition, the literature by Galeazzo and Klassen (2015) has focused on the relations between the implementation of sustainability and strategy. The research led to validating the traditional configuration models of competitive priorities while considering how these can be complemented by both environmental and social sustainability. They stated that while manufacturing strategy can provide positive support for sustainability, it is not yet a sufficient condition to implement sustainable practices. This could indicate that traditional competitive priorities do not have a direct relation to the degree of sustainability practices within a company, but perhaps that sustainability in itself could be a priority.
LH1 There is no direct relation between traditional competitive priorities and the implementation of sustainability practices within companies.
2.2. Competitive priorities – Low Cost
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sustainability in cases where companies implement processes that are focused on reducing costs by reducing waste (Curkovic et al., 2000; Wu and Pagell 2011).
LH2 ‘Low cost’ priority has a positive relation with environmental sustainability practices.
Galeazzo and Klassen (2015) also state that environmental sustainability practices fit better with a relatively greater emphasis on flexibility and delivery as opposed to the other priorities. Given this insight it this appears that current literature suggests that companies that emphasize flexibility and/or delivery as a competitive priorities have a higher rate of sustainability implementations. Furthermore, when pursuing sustainability they suggest that a combination of emphasis on flexibility and delivery will help by bringing a more balanced approach within the strategic priorities. Other research by Dangelico and Pujari (2010) shows that there are similar relations between operations strategy and the implementation of environmental sustainability.
LH3 Flexibility and delivery priorities have a positive relation with environmental sustainability practices.
LH4 Flexibility and delivery priorities have a positive relation with social sustainability practices.
Very little research is available that relates socials sustainability to competitive priorities. The work by (Gimenez et al., 2012) looked at the impact both environmental and socials sustainability have at the triple bottom line (environmental, social and economic performance). They indicate that the impact of social sustainability on the triple bottom line is not straightforward. It can improve both environmental and social performance but also increase manufacturing costs. Therefore it is likely that companies that focus on low cost are less likely to implement social sustainability. This if further supported by (Gimenez et al., 2012; Wu and Pagell, 2010) in which the effect of sustainability deployments on low cost has proven to have controversial results. The evidence suggests that social sustainability priorities may sometimes be difficult to include in price-oriented operations strategy.
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2.2.1. Competitive priorities – Quality, Flexibility and delivery
Galeazzo and Klassen (2015) also found that social sustainability practices seems to relate more with a quality oriented strategy. They state that it is likely that social sustainability is emphasized within certain companies that experience greater external pressures (such as regulation and culture). In order to cope with these changing pressures they propose that companies with an increased focus on flexibility are better suited to implement social sustainability.
LH6 Quality priorities have a positive relation with social sustainability practices.
2.3. Concluding the literature review
2.3.1. Research questions
As mentioned in the previous section there appear to be priorities that have a direct relation with the implementation of either social or environmental sustainability. However, possible alignment between a combination of these priorities and their relations to sustainability are currently not clearly defined in the literature. As indicated in the introduction section, the objective of this study is therefore to provide insight to what degree the combination of the 4 competitive priorities (defined as operations strategy configurations in this study), are related to the degree of sustainability implementations within manufacturing companies. To this end the following research question is formulated:
“How are different operations strategy configurations and the degree of sustainability practices within manufacturing companies related?”
And more specific the following sub questions: “Which operations strategy configurations have a higher or lower degree of environmental sustainability?” and “Which operations strategy configurations have a higher or lower degree of social sustainability?”
To answer these research questions the overview of insights that were obtained out of the literature (LH1-6) in the previous section are used to specify the sub research questions below.
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Q1 Do operations strategy configurations that are focused on ‘low cost’, flexibility and delivery priorities lead to a higher degree of environmental practices within manufacturing companies (LH2 & 3)?
And for social sustainability:
Q2 Do operations strategy configurations that are focused on quality, flexibility and delivery priorities lead to a higher degree of social sustainability practices within
manufacturing companies (LH4 & 6)?
Q3 Do operations strategy configuration that are focused on ‘low cost’ priority lead to a lower degree of social sustainability practices within manufacturing companies (LH5)? The first hypothesis (LH1) that was mentioned in the literature seems to suggest the possibility that there is no direct relation between the traditional competitive priorities and the implementation of sustainability practices within companies. To assess if this is the case the following question is drafted:
Q4 Is there a direct relation between combinations of traditional competitive priorities and the implementation of sustainability practices within manufacturing companies (based on LH1)?
To answer the research questions this research follows a deductive approach (Saunders, Lewis & Thornhill, 2009) in which empirical data is collected through surveys. Thirty-three international manufacturing companies have completed a survey about sustainability and competitive priorities. The collected empirical data is used to test the propositions and provide an explanation to answer the research question.
2.3.2. Conceptual model
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3. Research Methodology
This section will first elaborate on the questionnaire design and measures. After which the characteristics of the respondents are described and how these were approached. Finally this section will elaborate on the cluster analysis methodology that was used to analyze the collected data.
Figure 1: Conceptual model
3.1. Questionnaire design and measures
In order to perform the analysis in this study the survey instrument was designed and developed based on a literature review. Within the survey several constructs and items are used. These are adapted from current literature and are included below. A pre-test was conducted with academic peers in order to check wording of the questions and to confirm that the understanding of the questions was appropriate. This resulted in minor changes within the survey items. All questions are answered using a five-point Likert scale, these scale anchors are also included in the following overview of constructs and items:
Low Cost – This construct consists out of:
Item Anchor
P1 - Produce products with low costs Far Worse/Better P2 - Produce products with low inventory costs Far Worse/Better P3 - Produce products with low overheads costs Far Worse/Better P4 - Lower selling prices Not/Very important
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Flexibility – This construct consists of multiple items:
Item Anchor
F1 - Be able to rapidly change production volume Far Worse/Better F2 - Produce customized product features Far Worse/Better F3 - Produce broad product specifications within
same facility Far Worse/Better
F4 - The capability to make rapid product mix
changes Far Worse/Better
F5 - Offer more product customization Not/Very important F6 - Wider product range Not/Very important F7 - Offer new products more frequently Not/Very important F8 - Greater order size flexibility Not/Very important
Items are adapted from (Kim, J.S. and Arnold, P. 1996; Ward, et. al., 1998; Frohlich and Dixon, 2001; Miller and Roth, 1994).
Delivery – The construct focuses on the delivery side of the products and consists of:
Item Anchor
D1 - Provide correct quantity with the right kind of
products Far Worse/Better
D2 - Deliver products quickly or short lead-time Far Worse/Better D3 - Provide on-time delivery to our customers Far Worse/Better D4 - Provide reliable delivery to our customers Far Worse/Better D5 - Reduce customer order taking time Far Worse/Better D6 - More reliable deliveries Not/Very important
D7 - Faster deliveries Not/Very important
D8 - Superior product assistance/support
(after-sales and/or technical support) Not/Very important D9 - Superior customer service (training,
information, help-desk) Not/Very important
These were adapted from (Jabbour et al., 2012; Kim, J.S. and Arnold, P. 1996; Ward, et. al., 1998; Frohlich and Dixon, 2001; Miller and Roth, 1994).
Quality – Consists out of the following:
Item Anchor
Q1 - Offer high performance products that meet
customer needs Far Worse/Better
Q2 - Produce consistent quality products with low
defects Far Worse/Better
Q3 - Offer high reliable products that meet
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customer needs Far Worse/Better
Q5 - Better product design and quality Not/Very important Q6 - Better conformance to customer
specifications Not/Very important
Q7 - Offer products that are more innovative Not/Very important
This construct and items were adapted from (Galeazzo, A. and Klassen, R.D., 2015; Kim, J.S. and Arnold, P., 1996).
Environmental Sustainability – This construct contains the following items;
Item Anchor
ES1 - Reduction of air emissions Far Worse/Better ES2 - Reduction of waste water Far Worse/Better ES3 - Reduction of solid wastes Far Worse/Better ES4 - Decrease of consumption for
hazardous/harmful/toxic materials Far Worse/Better ES5 - Decrease of frequency of environmental
accidents Far Worse/Better
ES6 - More environmentally sound products and
processes Not/Very important
These items and the construct were adopted from (Jabbour et al., 2012; Fairfield, Harmon and Benson, 2010; Melnyk, et al., 2003; Galeazzo and Klassen, 2015).
Social Sustainability – Consists out of the following items;
Item Anchor
SS1 - Safety and labor conditions in our facilities Far Worse/Better SS2 - Compliance with human rights Far Worse/Better SS3 - Compliance with child labor employment Far Worse/Better
SS4 - Social reputation Far Worse/Better
SS5 - Decrease in the number of industrial
accidents Far Worse/Better
SS6 - Higher contribution to the development and
welfare of the society Not/Very important
SS7 - More safe and health respectful processes Not/Very important
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reliable and consistent we ensured that all items were similarly coded and performed the Cronbach Alpha analysis. The results of this analysis are described below.
3.1.1. Cronbach Alpha - Social sustainability
From our calculations, we can see that Cronbach's alpha is 0.882, which indicates a very good level of internal consistency for our scale with this specific sample. We noted that removal of any question, except for the question of ‘More safe and health respectful processes (SS7), would result in a lower Cronbach's alpha. Removal of the aforementioned question would lead to a very small improvement in Cronbach's alpha (.009). Given that the internal consistency very slightly increases by the removal of the item, we chose not remove it. Please refer to appendix 7.2 Social sustainability for the results table.
3.1.2. Cronbach Alpha - Environmental sustainability
For environmental sustainability we calculated that Cronbach's alpha is 0.879 over the items that were used, which indicates an excellent level of internal consistency for our scale with this specific sample. We noted that removal of any question, except for the question of ‘More environmentally sound products and processes (ES6)’, would result in a lower Cronbach's alpha. Removal of the aforementioned question would lead to a reasonable improvement in Cronbach's alpha, resulting in 0.900. Given that the internal consistency considerably increases by the removal of the item, we chose to remove it. Please refer to appendix 7.3 for the results table.
3.1.3. Cronbach Alpha - Flexibility
From our calculations, we can see that Cronbach's alpha is 0.856, which indicates a good level of internal consistency for our scale with this specific sample.
We noted that removal of any question would result in a lower Cronbach's alpha, please refer to, and please refer to appendix 7.4 Flexibility.
3.1.4. Cronbach Alpha - Quality
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3.1.5. Cronbach Alpha - Delivery
From our calculations, we can see that Cronbach's alpha is 0.873, which indicates a sufficient level of internal consistency for our scale with this specific sample, please refer to appendix 7.6 Delivery.
3.1.6. Cronbach Alpha – Low cost
From our calculations, we can see that Cronbach's alpha is 0.752, which indicates a very good level of internal consistency for our scale with this specific sample. We noted that removal of any question, except for the question of ‘Lower selling prices (P4)’, would result in a lower Cronbach's alpha. Removal of the aforementioned question would lead to a reasonable improvement in Cronbach's alpha, resulting in 0.870. Given that the internal consistency considerably increases by the removal of the item, we chose to remove it. Please refer to appendix 7.7 for the results table.
3.2. Participants
Data from a sample of Spanish, Portuguese and French manufacturing firms was collected between April and July 2016. Managers and up were chosen to fill in the questionnaire because they have the knowledge and access to relevant information that is needed. The sample consisted of decision makers (manager up) of 33 international manufacturing companies at least 50 employees. As the study is focused on operations strategy configurations, the NACE codings from 10 to 17 and from 20 to 31 were used for selecting companies to ensure that they were active within the manufacturing sector:
Food products (10); beverages (11); tobacco products (12); textile (NACE codes 13-15); wood and wood products (16); paper and paper products (17); chemical (20);
pharmaceutical (21); electronics (26-27);
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motor vehicles, trailers and semi-trailers (29); other transport equipment (30); and
furniture (31).
Out of this selection 150 companies have been approached. A procedure was followed aimed to minimize key-informant bias; the first step was to research each firm via LinkedIn to identify the most suitable respondent with respect to operations strategy and sustainability. These persons were then contacted by phone and email to inquire for their participation. Initially 67 companies were willing to participate, 82 were not. However, out of the 67 willing respondents only 33 respondents (22%) have completed the survey within the time frame for this study.
Given the time constraints related to this research, 33 completed surveys from different companies are considered to be sufficient to perform the research an provide an indicative answer to the research question and present an indication of findings about possible relations between the operations strategies configurations and the implementation of sustainability. Table 1 shows the description of the sample:
Table 1: Characteristics respondents
Function Total % of total
CEO 1 3
Plant Manager 5 15
Manager Operations Department 18 55 Manager Environmental Department 2 6
Other Manager 7 21
Industry & Turnover
< 10M € 25 76
Apparel and other finished products made from fabrics and similar materials1 3
Beverages 1 3
Chemicals and allied products 5 15
Construction 1 3
Electronic and electrical equipment and components 8 24
Food beverages 1 3
Furniture 1 3
Leather and leather products 1 3
Machinery and equipment 2 6
Motor vehicles, trailers and semi-trailers 1 3
Other transport equipment 2 6
Textile Mill Products 0
-Transport 1 3
> 10M - 20M € -
-> 20M - 50M € 6 18
Apparel and other finished products made from fabrics and similar materials2 6 Chemicals and allied products 2 6
Machinery and equipment 1 3
Textile Mill Products 1 3
> 50M - 100M € -
-> 100M € 2 6
Electronic and electrical equipment and components 1 3
Transport 1 3
Size
51 - 100 17 52
101 - 500 7 21
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3.3. Sample and data collection
In this study, the managers of 33 international manufacturing companies conducted a survey in order to investigate environmental and social sustainability, as well as competitive priorities. The abovementioned table illustrates the characteristics of the respondents that have completed the survey; the function the respondent performs in the company), in which (NACE coded) industry the company is active, it’s turnover as well as how many FTE are working within the company. The surveys have been completed by the managers via internet to collect quantitative data of these companies. Every survey includes the same questions. A survey is chosen because it provides an efficient way of collecting responses from a large sample for quantitative analysis (Saunders, Lewis & Thornhill, 2009). Analysis of the collected data through surveys will be quantitative.
3.4. Methodology
There are several forms of analysis that can be used within a research. Previous literature studies, such as those mentioned in chapter 2, mainly focused on the regression type analyses to identify the various possible relations between the competitive priorities and sustainability and to validate these (Boyer and Lewis, 2002; Miller and Roth, 1994; Frohlich and Dixon, 2001; Jabbour et. Al., 2012). However, as this research focuses on the relation between the operations strategy configuration (the combination of priorities) within a company and how these relate to the degree of implemented sustainability practices within a company, the regression analysis seems unfit. Instead, to analyze the companies and their choices of their operations strategy, the combinations of competitive priorities within the companies need to be identified which leads to a number of operations strategy configurations that are present within the companies of the respondents. As such the study will use cluster analysis.
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Form of cluster analysis
As the used data set is not large (which often calls for a two-step procedure) and it’s not known how many clusters will be present in the sampled companies (which are usually analyzed by k-means), the hierarchical clustering analysis will be used. A hierarchical method approach for the clyster analysis will be used to identify the groups of objects (in this case companies). The identified groups will then be analyzed to see how they are related to both forms of sustainability.
The hierarchical analysis is often used when there is relatively small data set and provides solutions with clusters that are based on the similarity or distance between cases. Distance is a measure of how far apart two cases are, while similarity measures how similar two cases are (based on the selected criterion). For cases that are alike, distance measures are small and similarity measures are large (Everitt et. al, 2011; Burns and Burns, 2008). To identify the similarity or distance between cases we use the items of the 4 competitive priorities (‘Low cost’, Quality, Flexibility and Delivery) that are defined in section 3.1. The combination of how these priorities are distinguished within the companies will create the clusters, whereas we are looking for as much distinction between the combinations of priorities as possible. One of the biggest problems with cluster analysis is identifying the optimum number of clusters. As the fusion process continues, increasingly dissimilar clusters must be fused. Therefore the decision for the clustering is highly dependent on the variables that are used. (Burns and Burns, 2008).
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companies (Hintze, 2000). However, as the survey is conducted under a small set of respondents with a standardized Likert scale the variables of competitive priorities are likely to show little variances. The next stage is to rerun the hierarchical cluster analysis with the selected number of clusters, which enables the allocation of every case in the sample to a particular operations strategy cluster. In the next chapter we will present the data analysis and its results.
4. Data analysis and results
The objective of our study is to explore the relationships between the operations strategy configuration a company has implemented and the degree of sustainability within manufacturing companies. To this end different theoretical constructs (i.e., practices and performance) are used, where our model examines whether specific combinations of traditional operations strategies priorities, which are based on actual strategies within the tested companies, have a relationship with both environmental and social sustainability within (the tested) companies. To test the propositions formulated in the literature review, we conducted our research in the following steps.
4.1. Performing the hierarchical cluster analysis
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Table 2: Agglomeration Schedule and associated dendrogram
The next step of the analysis is to re-run the analysis so that the cases are assigned to one of both clusters. In case the analysis contains a large number of respondents it is advised to proceed by conducting a one-way ANOVA to determine which classifying variables are significantly different between the groups (Burns and Burns, 2008). However as there are only 33 cases in this study the ANOVA has no added value for illustrating how distinct the defined clusters are.
4.2. The operational strategy configuration characteristics of the clusters
This section will describe the characteristics of the two clusters that are identified in the previous section. In order to illustrate how the operations strategy configuration of both clusters are defined the various items are recalculated into their respective constructs. This is done by using the ‘compute variable’ functionality within SPSS, for each case the mean for each item is calculated and saved as a new variable. In turn, for each cluster the mean was calculated for each of the resulting construct. The results are depicted in table 3.
Cluster 1 Cluster 2 Cluster 1 Cluster 2
1 28 29 0,000 0 0 2 2 22 28 0,000 0 1 3 3 22 24 0,000 2 0 5 4 2 8 0,000 0 0 30 5 22 27 2,400 3 0 6 6 22 26 5,833 5 0 8 7 16 31 9,333 0 0 8 8 16 22 14,000 7 6 12 9 15 17 19,000 0 0 16 10 1 19 25,000 0 0 18 11 6 14 33,000 0 0 16 12 16 30 41,667 8 0 22 13 11 25 53,667 0 0 20 14 23 33 66,167 0 0 28 15 7 18 79,167 0 0 24 16 6 15 94,167 11 9 17 17 6 13 110,567 16 0 25 18 1 12 128,567 10 0 31 19 3 10 146,567 0 0 25 20 9 11 165,900 0 13 24 21 21 32 185,400 0 0 23 22 16 20 208,233 12 0 23 23 16 21 231,733 22 21 27 24 7 9 259,800 15 20 26 25 3 6 288,829 19 17 26 26 3 7 324,917 25 24 30 27 4 16 361,186 0 23 28 28 4 23 413,383 27 14 29 29 4 5 472,167 28 0 31 30 2 3 548,607 4 26 32 31 1 4 633,726 18 29 32 32 1 2 890,364 31 30 0 Agglomeration Schedule
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Table 3: The operational strategy configuration for each cluster
The items that are used for the constructs are based on a 5 point Likert scale (where 1 can be considered as low and 5 as high). Keeping the scale in mind it appears that the operations strategy configuration in cluster 1 scores average (3) on all priorities with very little variety, indicating that the companies within this cluster have spread their operations strategy and do not have a specific focus.
The same lack of variety in priorities is present at cluster 2, however this cluster seems to score higher on all the priorities which likely indicates that these companies put more emphasis on overall performance.
4.3. Computing the environmental sustainability and environmental sustainability
variables
As the Cronbach Alpha analyses have shown in the previous chapter, the multi-item constructs were internally consistent. As such, we have also computed these items into their respective constructs for further use in our analysis. We added the computed variables for both environmental sustainability and social sustainability to the list of variables. This allowed for the next stage of the analysis that was focused on the relation between the identified clusters of operations strategy configurations and the degree of sustainability within a manufacturing company. As each cluster is compiled out of homogeneous companies, for which the four priorities have been calculated, we now calculate the mean for each sustainability variable within a cluster. To calculate the mean for the cluster the average of the two computed sustainability variables is taken for each clusters, the results have been have been illustrated in table 4. Table 4 now shows the complete overview of the operations strategy configurations and the degree of sustainability within the clusters.
Cluster Delivery Flexibility Low Cost Quality
1 3,0 2,8 2,7 2,9
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Table 4: The operational strategy configuration in relation to sustainability for each cluster
5. Discussion and conclusion
This research started off by identifying that there are little to no studies that pay attention to the combination of competitive priorities and their relation to sustainability. However, current literature did identify several relations between the individual variables. As such, that was the starting point for this research to identify which combinations might be empirically present within manufacturing companies and how these relate to sustainability. The previous chapter contains the various results from the hierarchical cluster analysis that was performed and have been summarized in table 4. Despite the different industries that were present amongst the respondents there is little variance between the competitive priorities of a cluster. As a result both clusters contain no specific combination or operations strategy configurations that can explicitly be identified that influence the degree of sustainability within that cluster.
Research questions
This research set out to provide an answer to its research questions and used 4 sub questions that were deducted from literature to do so. The first sub question is focused on a positive relation between three priorities and environmental sustainability:
Q1 Do operations strategy configurations that are focused on ‘low cost’, flexibility and delivery priorities lead to a higher degree of environmental practices within manufacturing companies (LH2 & 3)?
The results that are presented within table show little variance between the various priorities and the degree of environmental sustainability within each cluster. As such, cluster 1 shows a degree of focus on the ‘low cost’, flexibility and delivery of around 3 which is also score for environmental sustainability. A similar relation can be seen in cluster 2 where it scores 4 on both aspects. Therefore there appears that the answer is yes. However, there is one side note as the fourth priority of quality also scores similarly which might imply that this priority is also of importance.
Cluster Delivery Flexibility Low Cost Quality Environmental sustainbility sustainabilitySocial
1 3,0 2,8 2,7 2,9 2,9 3,0
25
The next sub questions are related to social sustainability, either positive (Q2) or negative (Q3).
Q2 Do operations strategy configurations that are focused on quality, flexibility and delivery priorities lead to a higher degree of social sustainability practices within
manufacturing companies (LH4 & 6)?
The results are similar to those presented at Q1; based on the results for both clusters that are presented in table 4 it appears that the answer is yes. Quality, flexibility and delivery priorities score as high as the scores on social sustainability. Again, the fourth priority that is not addressed in the question directly, but also scores also similarly high and might also indicate a role of importance.
Finally, the one negative relation that was suggested on an individual relation level:
Q3 Do operations strategy configurations that are focused on ‘low cost’ priority leads to a lower degree of social sustainability practices within manufacturing companies (LH5)?
When looking at the results for both clusters there seems to be no direct negative relation between the ‘low cost’ priority and sustainability. Therefore, the answer appears to be no. The fourth sub question suggests that there might not be a direct relation between operations strategy configurations and sustainability within a (manufacturing) company:
Q4 Is there a direct relation between combinations of traditional competitive priorities and the implementation of sustainability practices within manufacturing companies (based on LH1)?
26
Using the insights provided by the results the research questions can only be partly answered. The sub questions “Which operations strategy configurations have a higher or lower degree of social/environmental sustainability?” cannot be answered as there were 2 clusters that showed similar characteristics in their scoring. Due to this scoring there are no inverse relations identified within this research that could be used to provide an answer. However for the main research question “How are different operations strategy configurations and the degree of sustainability practices within manufacturing companies related?” there might be one useful insight; considering that both clusters score (almost) equally on all priorities and sustainability it might suggest that the whole of the operations strategy configurations has a relation with sustainability within a manufacturing company.
One thing that is significantly visible is the difference in overall scores between both clusters. Cluster 1 appears to be scoring consistently lower then cluster 2. These differences are not addressed by the research questions within this research. To provide a possible explanation for the difference this research also considered other possible factors at play such as the characteristics of a company.
Difference between clusters
To explain the difference between the clusters various characteristics of the companies (like their country, size and industry) were analyzed if they potentially influence the identified clusters. First the characteristics have been integrated into the overall overview of table 4, which has led to several new tables. Please refer to appendix 7.1.
27
Another explanation can potentially be found in table 6 of appendix 7.1. Table 6 looks at the country in which the company is located. Like industries countries can impose certain rules that will influence the choice for either specific competitive priorities and/or sustainability. Looking at the table Portugal scores the lowest rates in both clusters while Spain scores the highest. Again, given the limitation of the number of respondents no conclusive answers can be given.
Finally the size of the company was considered in table 7. Cluster 1 shows very little variance over the sizes, however in cluster 2 it appears that large (>500) companies have the least focus on any priority while the mid-sized companies have the highest focus on all priorities. If we assume that the small number of respondents is not of any particular influence we can potentially assume that it appears that size is not directly related to the different scores of the clusters as both clusters show different properties for the small/mid/large sized companies.
Limitations
Prior research has not focused on impact the compositions of traditional competitive priorities of operations strategy can have on sustainability within firms. In addition, the focus on social sustainability has been quite recent. This in itself has proven a difficult factor when aiming to conduct a research in this area; constructs and variables are sparsely available and few are focused on traditional operations strategy.
The main limitations of this research appears to be the size of the samples. As the research was based on a cluster analysis with a population of 33 surveys little distinguishing clusters could be identified. Any differences are hard to substantiate or explain as some characteristics such as industry are merely represented by one respondent. In addition, likely do the size of samples there are just 2 clusters. Due to the limited amount of clusters there were no clusters that showed negative relations to sustainability which could be very helpful in identifying and substantiating the relation between operations strategy configurations and sustainability. Future research
28
sets that were used for researching the individual relations between the variables in the same context or by providing survey’s to a larger set of respondents. It would be interesting to analyze if within a data set there are significant differences between relations that are identified on an individual level versus those that can be identified through the cluster analysis. This might provide more insight into the differences between the current literature on individual relations and the results of this study.
In addition, our results show three propositions that can be tested in future research. The first and second research questions (Q1&Q2) showed positive answers. However, as the fourth priority that were not part of the question(s) also scored similarly on both clusters, it could imply that these priorities are also of importance for the relation of an operations strategy configuration to sustainability. This is in line with the answer to the fourth question (Q4) for which both clusters score (almost) equally on all priorities and sustainability. This might suggest that the whole of the operations strategy configurations has a relation with sustainability within a manufacturing company, not just an individual priority. This might prove useful to research in future studies as current studies are mainly focuses on individual relations between priorities and sustainability. Furthermore the discussion related to the difference between the clusters based on company characteristics might prove insightful as countries and industries have outliners (in the current limited dataset).
Finally there is a need for research that focuses on providing answers related to the contradictions that are mentioned in the literature and were encountered here. Perhaps by using large datasets future research can provide a conclusive answer as to which relations are accurate. The limited results that are presented here do provide interesting theoretical implications. Contrary to current (individual) relations that were identified and supported within the literature, the results have shown little or no support for those relations. This could indicate that currently managers that are pursuing sustainability are unable to have appropriate controls for their strategic decision making.
Conclusion
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30
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7. Appendix
7.1. Appendix – Analysis of clusters and underlying characteristics
The below illustrated tables are color coded (per competitive priority and per cluster), which allows for a quick comparison how industries are performing within a cluster on each competitive priority.
Table 5: Cluster vs industry
Cluster & Industry # Of companies Delivery Flexibility Low Cost Quality Environmental Social
Cluster 1 19 3,0 2,8 2,9 2,7 2,9 3,0
Apparel and other finished products made from fabrics and similar materials 1 3,0 3,0 3,0 3,0 3,0 3,0
Chemicals and allied products 5 3,5 2,8 3,0 3,0 3,5 3,4
Construction 1 2,3 1,5 2,1 2,0 1,1 1,6
Electronic and electrical equipment and components 6 2,7 2,9 2,9 2,8 2,9 2,9
Furniture 1 2,9 2,9 3,0 2,0 3,0 3,0
Leather and leather products 1 3,2 3,1 2,9 3,0 3,1 3,4
Machinery and equipment 2 2,8 2,8 2,9 2,3 2,6 2,7
Motor vehicles, trailers and semi-trailers 1 3,1 3,0 2,7 2,0 2,1 2,1
Transport 1 3,2 3,1 3,0 3,0 3,2 3,2
Cluster 2 14 3,9 3,9 4,1 3,7 3,9 3,9
Apparel and other finished products made from fabrics and similar materials 2 4,1 3,9 4,0 3,0 3,9 3,4
Beverages 1 3,4 3,5 3,7 3,0 3,1 3,1
Chemicals and allied products 2 4,2 4,4 4,5 4,2 4,1 4,3
Electronic and electrical equipment and components 3 3,6 3,3 3,8 3,7 3,5 3,5
Food beverages 1 3,7 4,0 4,3 3,7 4,4 4,5
Machinery and equipment 1 3,9 4,0 4,7 3,3 4,5 4,4
Other transport equipment 2 3,8 3,8 4,1 3,7 4,0 3,6
Textile Mill Products 1 5,0 5,0 5,0 5,0 5,0 5,0
Transport 1 4,0 3,5 3,4 4,3 3,6 4,4
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Table 6: Cluster versus country: Shows how companies within a country and cluster are performing for each competitive priority.
Table 7: Cluster versus size and industry: Shows how companies with various sizes are performing for each competitive priority in the cluster.
Cluster and country # Of companies Delivery Flexibility Low Cost Quality Environmental Social
Cluster 1 19 3,0 2,8 2,7 2,9 2,9 3,0 France 7 2,8 2,8 2,6 2,9 2,8 2,9 Portugal 4 3,2 2,5 2,8 2,7 2,5 2,5 Spain 8 3,1 2,9 2,8 3,0 3,2 3,2 Cluster 2 14 3,9 3,9 3,7 4,1 3,9 3,9 France 5 4,1 3,8 3,7 4,0 4,1 4,0 Portugal 6 3,6 4,0 3,3 4,1 3,7 3,7 Spain 3 4,2 3,8 4,4 4,3 4,0 4,2
Size of the company (FTE) # Of companies Delivery Flexibility Low Cost Quality Environmental Social
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7.2. Cronbach Alpha - Social sustainability
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items
,882 ,883 7 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted SS1 - Safety and labor
conditions in our facilities 20,06 24,996 ,451 . ,891
SS2 - Compliance with
human rights 20,21 24,297 ,524 . ,882
SS3 - Compliance with
child labor employment 20,24 22,002 ,828 . ,845
SS4 - Social reputation 19,97 23,218 ,669 . ,864 SS5 - Decrease in the number of industrial accidents 20,24 22,002 ,828 . ,845 SS6 - Higher contribution to the development and welfare of the society
19,85 22,820 ,601 . ,874
SS7 - More safe and health respectful processes
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7.3. Cronbach Alpha - Environmental sustainability
Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items ,879 ,883 6 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted ES1 - Reduction of air
emissions 16,909 19,148 ,700 ,837 ,856
ES2 - Reduction of waste
water 17,000 19,750 ,666 ,881 ,862
ES3 - Reduction of solid
37 ES4 - Decrease of consumption for hazardous/harmful/toxic materials 17,121 19,485 ,775 ,765 ,845 ES5 - Decrease of frequency of environmental accidents 17,455 18,756 ,700 ,776 ,857 ES6 - More environmentally sound products and processes
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7.4. Cronbach Alpha - Flexibility
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items
,856 ,858 8 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted F1 - Be able to rapidly change production
volume 22,85 29,883 ,461 ,745 ,853
F2 - Produce customized product features 23,00 27,500 ,614 ,582 ,837
F3 - Produce broad product specifications within same facility
22,79 27,922 ,542 ,822 ,845
F4 - The capability to make rapid product
mix changes 22,82 28,278 ,667 ,629 ,833
F5 - Offer more product customization 22,94 27,871 ,500 ,767 ,851
F6 - Wider product range 22,64 25,864 ,646 ,807 ,833
F7 - Offer new products more frequently 22,76 26,002 ,685 ,868 ,828
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7.5. Cronbach Alpha - Quality
Reliability Statistics
Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items
,855 ,864 7 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted Q1 - Offer high performance products that
meet customer needs 20,58 22,752 ,654 ,529 ,832
Q2 - Produce consistent quality products
with low defects 20,73 21,205 ,739 ,789 ,818
Q3 - Offer high reliable products that meet
customer needs 20,82 22,653 ,687 ,797 ,829
Q4 - Produce high quality products that
meet our customer needs 20,48 22,383 ,600 ,772 ,838
Q5 - Better product design and quality 20,09 22,523 ,542 ,563 ,846
Q6 - Better conformance to customer
specifications 20,21 21,860 ,536 ,602 ,849
Q7 - Offer products that are more
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7.6. Cronbach Alpha – Delivery
Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items ,873 ,874 9 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted D1 - Provide correct
quantity with the right kind of products
27,33 30,167 ,638 ,782 ,857
D2 - Deliver products
quickly or short lead-time 27,39 31,559 ,672 ,736 ,854
D3 - Provide on-time
delivery to our customers 27,36 31,114 ,599 ,600 ,860
D4 - Provide reliable delivery to our customers
27,33 30,917 ,692 ,613 ,852
D5 - Reduce customer
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7.7. Cronbach Alpha – Low cost
Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items ,752 ,768 4 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted P1 - Produce products with low
costs 9,88 4,547 ,642 ,561 ,639
P2 - Produce products with low inventory costs
9,85 4,758 ,639 ,560 ,644
P3 - Produce products with low
overheads costs 9,94 4,621 ,779 ,641 ,577
P4 - Lower selling prices 9,42 5,939 ,231 ,131 ,870
43 Reliability Statistics Cronbach's Alpha Cronbach's Alpha Based on Standardized Items N of Items ,870 ,872 3 Item-Total Statistics Scale Mean if Item Deleted Scale Variance if Item Deleted Corrected Item-Total Correlation Squared Multiple Correlation Cronbach's Alpha if Item Deleted P1 - Produce products
with low costs 6,27 2,642 ,744 ,558 ,826
P2 - Produce products
with low inventory costs 6,24 2,814 ,742 ,556 ,824
P3 - Produce products
