The impact of relational competence on South African...

1 | P a g e

THE IMPACT OF

RELATIONAL COMPETENCE

ON SOUTH AFRICAN

SUPPLY CHAIN RESILIENCE

RESEARCH DISSERTATION

Presented to The Graduate School of Business

University of Cape Town

In partial fulfilment of the requirement of the degree

Master of Business Administration

Submitted by:

Mathias Schütz

Supervisor:

Associate Professor Hamieda Parker

November 2016

Copyright UCT

2 | P a g e

PLAGIARISM DECLARATION

Copyright UCT

3 | P a g e

TABLE OF CONTENTS

Plagiarism declaration ................................................................................................................ 2

Abstract ...................................................................................................................................... 6

Purpose ............................................................................................................................... 6

Design / Methodology / Approach ..................................................................................... 6

Findings .............................................................................................................................. 6

Practical implications ......................................................................................................... 6

Originality / Value .............................................................................................................. 6

1. Research area & problem statement ................................................................................... 7

2. Research questions and scope............................................................................................. 7

3. Research assumptions and limitations ................................................................................ 8

4. Research ethics ................................................................................................................... 8

5. Literature review & hypotheses .......................................................................................... 9

a. Relational competencies ............................................................................................ 10

b. Supply chain resilience.............................................................................................. 10

c. South African supply chain context .......................................................................... 11

d. Hypothesis 1: Antecedents of and effects on agility ................................................. 12

e. Hypothesis 2: Antecedents of and effects on robustness .......................................... 14

f. Hypothesis 3: Effects on value of the SC customer .................................................. 16

g. Conclusion ................................................................................................................. 17

6. Research methodology and data ....................................................................................... 18

a. Research approach and strategy ................................................................................ 18

b. Research design, instruments and data collection methods ...................................... 18

1. Survey (expanded from Wieland et al., 2013) .......................................................... 19

c. Sampling.................................................................................................................... 22

d. Research criteria ........................................................................................................ 22

e. Data analysis methods ............................................................................................... 23

7. Analysis ............................................................................................................................ 24

a. Communication ......................................................................................................... 24

b. Cooperation ............................................................................................................... 25

c. Integration ................................................................................................................. 25

d. Agility........................................................................................................................ 25

e. Robustness ................................................................................................................. 26

Copyright UCT

4 | P a g e

f. Supply Chain ............................................................................................................. 26

g. Structural equation modelling analysis ..................................................................... 27

h. Common-method variance ........................................................................................ 27

8. Interpretation .................................................................................................................... 28

a. Characteristics of the example .................................................................................. 28

b. Measurement Model .................................................................................................. 28

1. Overall model fit ....................................................................................................... 28

2. Parameter estimates ................................................................................................... 28

c. Structural equation model ......................................................................................... 29

1. Testing H1a – H1c (Agility)...................................................................................... 29

2. Testing H2a – H2c (Robustness) ............................................................................... 29

3. Testing H3a and H3b (Value of the SC customer).................................................... 29

d. Correlation analysis ................................................................................................... 30

9. Findings & Discussion...................................................................................................... 31

a. Base case scenario ..................................................................................................... 31

b. Effect of firm experience........................................................................................... 32

c. Discussion ................................................................................................................. 32

1. Communication and effects on agility and robustness .............................................. 33

2. Cooperation, integration and effects on agility and robustness ................................ 34

3. Effects on value of the SC customer ......................................................................... 36

10. Limitations .................................................................................................................... 37

11. Conclusion .................................................................................................................... 37

a. Contributions to the literature ................................................................................... 39

b. Managerial implications ............................................................................................ 39

12. Future research .............................................................................................................. 40

Bibliography ............................................................................................................................ 41

Appendix I – Box and whisker plots........................................................................................ 47

Appendix II – Correlation experience (years) ......................................................................... 51

Appendix III: SEM analysis..................................................................................................... 54

Copyright UCT

5 | P a g e

TABLE OF FIGURES

Figure 1: Structural equation modelling - hypotheses (expanded from Wieland et al., 2013) 23

Figure 2: Structural equation modelling - supported and unsupported hypotheses ................. 33

Figure 3: Box & whisker plot – Communication..................................................................... 47

Figure 4: Box & whisker plot – Cooperation........................................................................... 47

Figure 5: Box & whisker plot - Integration ............................................................................. 48

Figure 6: Box & whisker plot - Agility .................................................................................... 48

Figure 7: Box & whisker plot - Robustness ............................................................................. 49

Figure 8: Box & whisker plot - Supply chain value of a customer.......................................... 49

Figure 9: Normal probability plot - normalised residuals ........................................................ 50

TABLE OF TABLES

Table 1: 7 Vulnerability factors (adapted from Pettit et al., 2010) .......................................... 15

Table 2: 14 Capability factors (adapted from Pettit et al., 2010) ............................................. 15

Table 3: Cronbach's α .............................................................................................................. 23

Table 4: Coding of questions and sub-questions ..................................................................... 24

Table 5: Communication variable analysis .............................................................................. 24

Table 6: Cooperation variable analysis .................................................................................... 25

Table 7: Integration variable analysis ...................................................................................... 25

Table 8: Agility variable analysis ............................................................................................ 26

Table 9: Robustness variable analysis ..................................................................................... 26

Table 10: Supply chain customer value variable analysis ....................................................... 26

Table 11: Overview supported and unsupported hypotheses .................................................. 27

Table 12: Constructs‟ reliability .............................................................................................. 28

Table 13: Work experience ...................................................................................................... 31

Table 14: Work experience in current firm .............................................................................. 32

Table 15: Correlations (CM/CP/IT) and (AD/RB) - Years of experience 1 ............................ 51

Table 16: Correlations (RB/AD) and SC - Years of experience 1 ........................................... 51

Table 17: Correlations (CM/CP/IT) and (AD/RB) - Years of experience 2 ............................ 52

Table 18: Correlations (RB/AD) and SC - Years of experience 1 ........................................... 53

Table 19: Structural equation model estimates ........................................................................ 54

Table 20: Noncentrality Fit Indices ......................................................................................... 55

Table 21: Single Sample Fit Indices ........................................................................................ 55

Copyright UCT

6 | P a g e

ABSTRACT

PURPOSE

The purpose of this research is to test findings of a pilot study of Wieland and Wallenburg

(2013) on Germanic markets with regards to supply chain resilience in the South African

context to identify, analyse and explain differences. Both studies explore the impact of

relational competence on resilience of the supply chain, and for context, how resilience

impacts the value of the customer value a supply chain offers.

DESIGN / METHODOLOGY / APPROACH

Empirical research, with the relational view as founding theory, has been conducted through

analysis of survey data collected from respondents of small, medium and large manufacturing

firms in South Africa. The data collected has been analysed using structural equation

modelling.

FINDINGS

Strong levels of cooperation positively affect resilience. Communication received lower

weighting. No significant effect of integration on resilience could be found. By improving

robustness and agility levels, companies are able to enhance the supply chain customer value.

PRACTICAL IMPLICATIONS

This study supports findings of Wieland et al. (2013), who identified contrasts to existing

theory. Notable findings are that integration appears to have a limited impact on resilience,

and communication is perceived to be less important.

ORIGINALITY / VALUE

This study applied research of mature Germanic markets to the emerging market context of

South Africa to identify potential differences between the 2 environments. It looked at

robustness as proactive measure, and agility as reactive measure, and reviews and compares

the effects of integration, communication and cooperation on these. It further expands on

disadvantages of integration.

Copyright UCT

7 | P a g e

RESEARCH BACKGROUND & THEORETICAL FOUNDATION

1. RESEARCH AREA & PROBLEM STATEMENT

Sufficient and sustainable supply chain resilience in today‟s ever-changing economic climate

is of great relevance to most industries, to remain in business in not only sustainable, but also

beneficial manner (Knemeyer, Zinn, & Eroglu, 2009). Resilience needs to be built up in

against many influencing factors, of high impact and low impact nature (Ambulkar,

Blackhurst, & Grawe, 2015), both inside and outside of the firm (Wei & Wang, 2010), and in

both directions of the supply chain, 1) supply side and 2) demand side (Nagurney, Cruz,

Dong, & Zhang, 2005).

Carpenter, as cited by Carter and Gray (2007), defines relational competence as “attributes of

the individual that serve interpersonal goals and positive relationships” (p.2), or as qualities

that enables one to interact with others in an effective manner (L‟Abate, Cusinato, Maino,

Colesso, & Scilletta, 2010). Wieland and Wallenburg (2013) have conducted explorative

research of the impact of relational competence on supply chain resilience (SCR) in the

context of companies in Switzerland, Germany and Austria. This research is of significance

as it aims to identify if these findings hold up in the context of South African as an emerging

economy, in comparison to established or mature economies inside of Europe. There are not

only different economic circumstances, but also different cultural backgrounds that may

affect the psychological contract of one, both and/or more involved parties (Thomas, Au, &

Ravlin, 2003).

2. RESEARCH QUESTIONS AND SCOPE

The main question of this research aims to identify the effects of relational competence on

supply chain resilience in South Africa. A sub-question will investigate a difference in

outcome for potential effects of cultural variation between Germanic countries and South

Africa on the findings of this research, when compared to the findings of Wieland et al.

(2013). Additionally, as suggested by the authors as a further variable that has not been

explored to date which may affect findings, it will also review disadvantages of integration as

Copyright UCT

8 | P a g e

a part of supply chain resilience through dependencies and asymmetries, and the effects of

speed and visibility on agility, and anticipation and preparedness in view of robustness, as

further influencing factors on supply chain agility and robustness. Other factors, such as

supply and demand side risk, are relevant in the greater context of the interacting variables of

supply chain resilience and relational competencies, but are outside of the scope of this

research.

3. RESEARCH ASSUMPTIONS AND LIMITATIONS

As Wieland et al. (2013) made no clear distinction between the research findings of

Germany, Austria and Switzerland, and reviewed findings of the Germanic market as a single

entity, the research in this paper has followed this approach by focussing on findings out of

South Africa only, as another single entity. Unexplored cultural differences between these

countries (Thomas et al., 2003) may have tilted the accuracy of the findings, should there be a

significant undisclosed difference between the findings of the three Germanic countries

surveyed. This research assumes that no significantly different outcomes have been found;

demographic, geographic and political similarities of the three countries taken into

consideration. I further acknowledge that whilst looking at potential cultural and

psychological differences providing partial explanations to any differences found, other

factors may alter the perspective the findings of this research, as it could have been the case

with the guiding research of Wieland et al. (2013). The large sample size of responses

received for the pilot study should have mitigated the impact of extreme outliers on response

averages and is therefore viewed as sufficiently mitigating internal variations.

4. RESEARCH ETHICS

All queries regarding research ethics have been disclosed on the Ethics clearance form

submitted, which has been submitted to and deemed suitable by the supervisor of this

research, and clearance has been received from the Ethics committee.

Copyright UCT

9 | P a g e

The research initially aimed to survey students of the University of Cape Town that work or

have worked in the sector analysed. This has been deemed unnecessary due to a sufficient

number of respondents outside of the University.

In my proposal, I had initially indicated the intention to offer an incentive to all participants

of the survey in form of a weekend holiday for one of the participants to receive a higher

number of responses, should the first attempt not yield sufficient responses. An independent

UCT GSB faculty member would have been asked to select the respondent at random. The

initial survey did however yield sufficient responses; there was no need for the offer of the

incentive.

5. LITERATURE REVIEW & HYPOTHESES

The below literature review, divided into subsections in line with the theme of the research, is

aimed to be as complete and coherent as possible in context of the quantitative research.

Literature about relational competence as well as the widely researched field of supply chain

resilience has been reviewed for context of this research. Additionally, literature for

consistency, validity and reliability of the research has been consulted, in line with previous

research on the matter. Wieland et al. (2012) performed hypothesis testing on 270 firms to

identify the level of impact that supply chain risk management has on the supply chain‟s

customer value as well as the firms performance, when this function supports robustness as

a proactive measure and agility as a reactive measure. Findings of the authors were that

robustness levels are of higher relevance for supplier side risks, and agility levels of higher

relevance for customer side risks, both of which need to be aligned with the competitive

strategy the company currently follows and intends to follow in the future to maintain

sustainable operations. As per the authors, robustness should however be viewed as a

primary objective of the two aspects. Wieland et al. (2013) further reviewed the impact of

relational competence on supply chain resilience in the context of small, medium and large

manufacturing companies in Germanic regions (Switzerland, Germany and Austria), the

study on which this research is based on.

Copyright UCT

10 | P a g e

A. RELATIONAL COMPETENCIES

Amongst others, integration, communication and cooperation have been viewed as

important relational competencies that have an impact on supply chain resilience (Fabbe-

Costes & Jahre, 2007; Omar, Davis-Sramek, Myers, & Mentzer, 2012; Antony Paulraj, Chen,

& Lado, 2012; Antony Paulraj, Lado, & Chen, 2008). Barratt and Oke (2007) claim to have

observed a tendency of firms to establish and strengthen links through communication by

sharing information with supply chain partners of the firm, something that Modi and Mabert

(2007) describe as the flow of explicit communication. Swink et al. (2007) describe

integration as combining and aligning work and information of firm and suppliers in a useful

manner (Skarmeas, Katsikeas, Spyropoulou, & Salehi-Sangari, 2008), which requires

cooperation as a basic condition. Cooperation has been viewed as active participation in a

mutually beneficial relationship (Morris & Carter, 2005), and not only the active exchange of

information. Previous research has argued that this would require the setup of firms, suppliers

and customers, as much as possible, to be aligned in the most effective manner (Flynn, Huo,

& Zhao, 2010; Frohlich & Westbrook, 2001). There is an assumed benefit in all parties

involved to understand, appreciate and honour the nature of a psychological contract in an

aligned manner (Pesqueux, 2012). Cultural diversity may present a misalignment thereof

(Thomas et al., 2003) that could negatively impact the quality of relational competence of a

firm, weakening the resilience of the supply chain.

B. SUPPLY CHAIN RESILIENCE

A supply chain could be defined as a line, flow or system of entities that a product or service

follows from the “raw material stage” to the end consumer. Some touch points in the supply

chain are integrated within one firm, others are outside of the firm. Each point in the supply

chain, irrespective of inside or outside the firm, has its own level of vulnerability, which may

differ from the levels of other points, each of which can have an effect on other parts of the

supply chain should a change occur, up to an extent where a supply chain can come to a

complete stand still (Norrman & Jansson, 2004), causing severe damage to a firm where risk

is not proactively mitigated and managed sufficiently.

Copyright UCT

11 | P a g e

Some research believes the concept of resilience to be the ability to act proactively, instead

of reactively when it becomes necessary (Hamel & Välikangas, 2003). On the contrary, the

concept has also been described as reactively, with regards to the manner and time in which

the previous status quo can be re-established after an incident (Pecillo, 2016). Wieland and

Wallenburg (2012) view both aspects as correct but differentiate between proactive and

reactive resilience, where the being reactive is described as agility, in line with Swafford,

Ghosh, & Murthy (2006) as well as Braunscheidel and Suresh (2009). Agility in the supply

chain resilience context is believed to be the combination of flexibility and speed (Prater,

Biehl, & Smith, 2001). Proactive resilience is described as robustness, as per Husdal

(2010). A robust supply chain is believed to be able to withstand (Wallace & Choi, 2011) and

endure (Husdal, 2010) changes in their environment, rather than to react or respond to them.

C. SOUTH AFRICAN SUPPLY CHAIN CONTEXT

I believe that the work of Wieland et al. (2013) has yielded results about the impact of

relational competencies which hold true for the Germanic market, all of which are developed

countries with assumed cultural similarities (Thomas et al., 2003), which again, in contrast,

are believed to be different to other cultures (Restubog, Bordia, & Tang, 2007), which would,

by default, include South African culture. Additionally, country-specific frameworks,

regulations and limitations pose challenges for companies and their local suppliers, who are

no exception to the challenges the market faces (Abor & Quartey, 2010), where, including

but not limited to, higher rates of failure of SME occur (Olawale & Garwe, 2010), which in

turn may create higher levels of opportunism (Morris & Carter, 2005) and dependence on the

larger supply chain partner through integration, which has been viewed as counterproductive

before (Manuj & Mentzer, 2008). Frese et al. (2007) conducted a study on psychological

planning in by SME business owners in Namibia, South Africa and Zimbabwe, indicating a

difference in proactive planning habits between smaller companies and larger companies,

where these habits are have been linked not only to cultural variance, but also to levels of

formal and informal business education (Unger, Keith, Hilling, Gielnik, & Frese, 2009).

Copyright UCT

12 | P a g e

D. HYPOTHESIS 1: ANTECEDENTS OF AND EFFECTS ON AGILITY

Christopher & Peck (2004) expand on the view on of Prater et al. (2001) on flexibility, by

adding that it takes both visibility and speed to achieve agility, where visibility is needed for

improved identification of challenges, and speed to improve response time. Challenges can be

identified better through improved communication, which can be achieved through a

conscious and proactive effort to share information inside and outside of the own supply

chain touch point (Barratt & Oke, 2007). It is reasonable to assume that delays in sharing

information can have a damaging effect, but research has shown that firms tend to initially

withhold information about disruptions for the fear of reputational damage or inappropriate

evaluation of the level of impact the disruption may have, at least for a time (Hendricks,

Singhal, & Zhang, 2009). Early communication is believed to aid links in the supply chain to

adapt to changes faster (Ritchie & Brindley, 2007).

Hypothesis 1a: Communication positively affects agility

Carter et al. (2007) argue that external market orientation alone does not suffice to improve

the performance of a business, and always needs to be accompanied by an internal market

focus. This is affected by working relationships in an intra-organisational context, where

relational competence facilitates productive relationships not only inside, but also outside of

the organisation, and improves overall firm performance. This increases visibility, where

errors and changes are made visible without delay, similar to Toyota‟s recently retired yet

famous “andon cord” method, where workers pulled a cord above their work station

immediately upon noticing a problem (Blumenfeld & Inman, 2009). Communicating changes

to the environment early with partners of the business should not only assist the firm, but the

entire supply chain network in acting faster (Ritchie et al., 2007), especially where

commitment to the relationship with partners is present, for example when environmental

changes trigger a need for changes in pricing (Ergun et al., 2010).

Hypothesis 1b: Cooperation positively affects agility

Sambasivan, Siew-Phaik, Abidin Mohamed and Leong (2013) combine various theories in an

attempt to explain strategic alliances, some of which are transaction cost theory, contingency

theory, personal relationship theory and social exchange theory. The authors refer to literature

Copyright UCT

13 | P a g e

that defines transaction cost theory as viewing all aspects of outsourcing cost, inclusive of

the cost to conduct search, retrieve information, bargaining cost and cost to police an

agreement. Contingency theory has been referred to as a method of operating a business not

in a universal model and rather in a model tailored to the needs of its specific environment.

Social exchange theory has been referred to as the mutual target to conduct interactions with

the lowest cost and highest benefits. Empirical research was conducted with a large number

of manufacturers in Malaysia, addressing questions about motives for strategic alliances,

interdependence on relational capital, as well as its mediating role and the environment

related to such. The authors assume that their findings improve knowledge of strategic

alliance, aid in identifying success factors of strategic alliances and to provide guidance for

the development of valuable partnerships that deserve mutual commitment, similar to the

ones identified in a case study performed by Ergun et al. (2010). Additionally, companies are

more likely to receive superior service and willing commitment from their partners upon

investment in cooperation and socialisation, perhaps through regular supplier conferences,

site visits and constant exchange on how buyer/supplier relationships can be enhanced

(Bruce, Daly, & Towers, 2004; Cousins & Menguc, 2006).

Hypothesis 1c: Integration positively affects agility

Wei et al. (2010) argue that strategic values can be created through the use of dynamic

capabilities to better understand the role that visibility plays in a supply chain. The authors

identify and review concepts of supply chain visibility and their implications in order to

improve the level of adjustability of the supply chain, subsequently improving its strategic

performance. Swafford et al. (2006) discuss agility as a valuable yet rarely researched topic in

the supply chain context. Through presentation of a flexibility framework of a firm, the

article displays integration factors that positively affect sourcing, manufacturing and

distribution, both individually and in combination. In contrast, Matopoulos, Vlachopoulou,

Manthou, and Manos (2007) warn about the negative impact integration can have if power in

the relationship is not equally distributed. Additionally, aside from power asymmetries,

integration has been viewed as a challenge for level governance, potentially affecting agility

and robustness throughout the supply chain (Crona & Bodin, 2010).

Copyright UCT

14 | P a g e

E. HYPOTHESIS 2: ANTECEDENTS OF AND EFFECTS ON ROBUSTNESS

Robustness of a supply chain can be seen as the ability of a company to proactively - rather

than reactively through speed and visibility of process steps - expect disruptions and

formulate approaches for changes that may occur (Hendricks et al., 2009; Yang, Aydın,

Babich, & Beil, 2009).

Hypothesis 2a: Communication positively affects robustness

Ambulkar et al. (2015) find that supply chain orientation does not suffice for a firm to

develop adequate resilience – risk management infrastructures need to be in place for low

impact disruptions, and the ability to reconfigure resources to address high impact

disruptions. Knemeyer et al. (2009), as well as Jüttner and Maklan (2011) see a steady

increase in vulnerability, with less room to adapt for firms for catastrophic events, as supply

chains tend to be operated leaner than in the past, where inventory levels and work force are

kept at a minimum level. Catastrophic events may have a lower probability but a high impact

if they occur. Inter-dependent relationships, both internal as well as external and up and down

the supply chain, are assumed to have an effect on the quality of this process. Ponomarov et

al. (2009) created a conceptual model to provide an integrated view on resilience based on

literature about ecosystems and developmental psychology. Findings were that supply chains

capable of finding relevant countermeasures are ones that constantly aim to prepare for a

variety of high impact and low impact events.

Hypothesis 2b: Cooperation positively affects robustness

Pettit et al. (2010) argue, based on their findings from a focus group, that supply chain

resilience can be assessed based on capabilities and vulnerabilities, where the firm should aim

to be in a “zone of resilience”, where a balance between the two dimensions will ensure the

most sustainable profitability. The article offers findings of seven vulnerability factors:

Copyright UCT

15 | P a g e

Table 1: 7 Vulnerability factors (adapted from Pettit et al., 2010)

Threat Definition

1 Turbulence Regular changes outside of your control

2 Deliberate threats Intentional attacks

3 External pressures Barriers to the firm that are not aimed exclusively at the firm

4 Resource limits Limited availability of factors of production

5 Sensitivity Control of conditions for integrity of product and process

6 Connectivity Level of dependence on outsiders

7 Supp/Cust disruption Level of vulnerability of S/C to outside factors

It further offers 14 capability factors, which have been designed into the questionnaire of this

research with the aim to learn more about the role of visibility from a resilience perspective.

Chen et al. (2004) view firms as links in a supply chain network. This created a requirement

of interdependent relationships built and maintained through strategic collaboration. The

authors reviewed 400 articles about various tools and models used in the pursuit of advancing

SCM theory.

Table 2: 14 Capability factors (adapted from Pettit et al., 2010)

Capabilities Definition

1 Sourcing flexibility Changing inputs or modes thereof

2 Order fulfilment flexibility Changing outputs or modes thereof

3 Capacity Asset availability for sustainable production levels

4 Efficiency Achieve desired outcome with minimum resources

5 Visibility Awareness of status quo of environment

6 Adaptability Modifying operations to meet changes

7 Anticipation Foresee future events

8 Recovery Time take to return to initial state

9 Dispersion Decentralisation

10 Collaboration Effectively work with outsiders with mutual benefits

11 Organisation HR, culture and policies

12 Position in the market Company status in market compared to competitors

13 Security Defending outside attacks

14 Financial backing Absorb cash flow fluctuations

Hypothesis 2c: Integration positively affects robustness

Manuj et al. (2008) see global supply chain at a higher risk than domestic ones, due to

additional links that have additional and different exposure to disruption; including changes

Copyright UCT

16 | P a g e

in other economies, perhaps even of political nature, or wider exposure to disasters. The

authors view integration as vital to the speed of a supply chain, which in return enables the

firm to react to disruption faster. Frohlich et al. (2001) interview weighting of integration of

suppliers and customers into the supply chain, based studies of 322 international

manufacturers. A scale was developed to measure this, yielding 5 findings as to which

strategies were applied. An adaptation of this scale will be used in the survey of this research.

As previously mentioned, opposing views have been documented about the downside of

integration where asymmetries in power are present (Matopoulos et al., 2007; Parker et al.,

2015).

F. HYPOTHESIS 3: EFFECTS ON VALUE OF THE SC CUSTOMER

Agility, broken down into speed and visibility (Braunscheidel & Suresh, 2009), is believed to

have a positive effect on the value the supply chain customer receives (Barratt & Oke, 2007).

Hypothesis 3a: Agility positively affects the value of the supply chain customer

Braunscheidel et al. (2009) have found that market orientation and learning orientation of the

firm has a significant impact on a supply chains‟ agility, amongst volume and mix flexibility,

especially during unforeseen circumstances (Kleindorfer & Saad, 2005) Hamel et al. (2003)

claim that business models can no longer afford to be static, and that the resilience of a

business is largely dependent on its ability to re-invent business models when conditions

change, and to constantly anticipate as to when changes need to happen, as recovery from a

change that impacts a firm unexpectedly is likely to take a relatively long time, but

preparedness can mitigate the impact on firms performance and delivery reliability

(Hendricks et al., 2009).

Hypothesis 3b: Robustness positively affects supply chain customer value

Kroes et al. (2010) view aligned outsourcing strategies as crucial for supply chain

performance and overalls business performance, as benefits of outsourcing may not always

benefit the overall strategy of the firm. The authors have compared five competitive

significances with the strategic “fit” of the firm which they find to support their claim. Hamel

et al. (2003) believe that a firm also needs to have the space for change to introduce new

business models before it becomes obvious that they have to change, and has to eliminate the

Copyright UCT

17 | P a g e

mental barriers of arrogance, nostalgia and denial, and to make space for small experiments,

and to change perspective to see that constant renewal is just as important as constant

improvement.

G. CONCLUSION

The survey was designed in a manner to address previously researched and new variables,

such as integration, communication and cooperation and their relation to robustness and

agility, of which most are believed to have a positive effect.

This research expands on existing research on the impact of several variables of relational

competencies on supply chain resilience in the South African context, firstly to support the

findings of Wieland et al. (2013) when applied a similar research methodology to an

emerging economy, and to further explore identified disadvantages of integration through

power asymmetries between two or more partners of the supply chain relationship. In

contrast to the assumed positive effects of integration on robustness and agility, it has been

argued that integration may not be exclusively beneficial. Matopoulos, Vlachopoulou,

Manthou and Manos (2007) discuss dependencies in supply chain collaborations in small to

medium enterprises (SME), with regards to imbalances due to power asymmetries negatively

associated with collaboration. Manuj et al. (2008) support this concern. Morris et al. (2005)

argue against this statement, where the authors agree that there is a significant relation of

cooperation to supplier logistics performance but that integrated supplier compliance,

conflicts of functional nature, or tendencies to exit the relationship, have no significant effect

on robustness. As previously mentioned, and in addition to power asymmetries, integration of

smaller entities has been viewed as a challenge to introduce balanced corporate governance,

which could affect robustness and agility throughout the supply chain, not only through

reduced visibility (Crona & Bodin, 2010).

Any differences between both studies will be reviewed in context of cultural and

psychological differences between individual manufacturing supply chain players in South

Africa and Germanic markets, to identify if the weighting of surveyed aspects may be related

to these.

Copyright UCT

18 | P a g e

6. RESEARCH METHODOLOGY AND DATA

A. RESEARCH APPROACH AND STRATEGY

The research, built on a quantitative research strategy, aimed to make an inference based on a

quantitative analysis of responses to a standardised survey issued to general managers and

managers closely related to the field of supply chain. This has been guided by the approach of

Wieland et al. (2013) in order to yield comparable responses.

B. RESEARCH DESIGN, INSTRUMENTS AND DATA COLLECTION

METHODS

Primary data for cross-sectional research (Rindfleisch, Malter, Ganesan, & Moorman, 2008)

has been gathered through an e-mail survey sent to potential respondents with representatives

of the same field. Research based on exclusively qualitative research through interviews may

provide higher levels of context and detail, but would require a significantly longer time

frame for data collection, preparation and analysis if a number of respondents similar to

survey recipients would be interviewed. The survey draft can be found below.

Surveyed aspects have been for factors of communication (Chen et al., 2004), cooperation

(Morris et al., 2005) and integration (Frohlich et al., 2001) to identify applicability of

statements with regards to supplier and customer relationships, and the impact of these

variables on agility (Swafford et al., 2006) to identify responsiveness to change in different

areas of the firm and product cycle, and robustness to identify the perceived longevity of

stability in the firm without having to undergo substantial adaptations (Wieland et al., 2012),

and for the factor of value to a supply chain’s customer (Kroes et al., 2010) in comparison

to competitors.

In addition, this research also aimed to expand the findings of Wieland et al. (2013) who

suggested further scope where integration is creating a disadvantage through the creation

of dependencies. Literature shows that this may happen through power asymmetries

(Matopoulos et al., 2007) which could weaken the supply chain resilience through

imbalanced collaboration requirements imposed by the stronger party (Manuj & Mentzer,

Copyright UCT

19 | P a g e

2008). Others have reviewed these factors in the context of relational capital formation

(Sambasivan et al., 2013).

The overall number of responses of quantitative research has been improved through

reminder mails. As mentioned, the survey aimed for a response rate between 15-20%, which

is based on the findings of Braunscheidel & Suresh (2009) which assume an average response

rate of 15%. The above-average response rate of 19.8 % of Wieland et al. (2013) followed

this guideline as well.

1. SURVEY (EXPANDED FROM WIELAND ET AL., 2013)

Industry experience

How many years of work experience do you have in in supply chain management?

(1) 0-2 years

(2) 3-5 years

(3) 6-10 years

(4) 11 year or more

Supply chain experience at current firm

How many years of work experience in supply chain management do you have in your

current firm?

(1) 0-2 years

(2) 3-5 years

(3) 6-10 years

(4) 11 year or more

Communication (adapted from Chen et al., 2004)

To what extent do the statements apply to the relationship of your company with your

suppliers and customers? (1 – strongly disagree; 7 – strongly agree):

(1) We provide each other with any information that might help us

(2) Exchange of information takes place frequently and in a timely manner

Copyright UCT

20 | P a g e

(3) We keep each other informed about events or changes that may affect the other party

(4) We give each other feedback about our performance

Cooperation (adapted from Morris and Carter, 2005)



(1) No matter who is at fault, problems are joint responsibilities.

(2) One party will not take unfair advantage of a strong bargaining position.

(3) We are willing to make cooperative changes.

(4) We do not mind owing each other favours.

Integration (adapted from Frohlich and Westbrook, 2001)



(1) We have full access to joint planning systems.

(2) We synchronize our production plans.

(3) We carry out joint electronic data interchange.

(4) We have knowledge of inventory mix/levels.

Disadvantages of integration (adapted from Matopoulos et al., 2007)



(1) We had to adapt to collaboration rules in favour of a stronger party

(2) Supply chain partners had to adapt to our collaboration rules due to our position of power

(3) The risk-reward share is not distributed equally between us and our supply chain partners

(4) Unequal distribution of risk-reward sharing has affected trust building

Agility and Speed (adapted from Swafford et al., 2006)

Please indicate the speed of reaction with which your company can engage in the following

activities should changes occur (1 – slow; 7 – fast):

Copyright UCT

21 | P a g e

(1) Adapt manufacturing lead times.

(2) Adapt level of customer service.

(3) Adapt delivery reliability.

(4) Adapt responsiveness to changing market needs.

Robustness (adapted from Wieland and Wallenburg, 2012)

To what extent do the statements apply to your supply chain?

(1 – strongly disagree; 7 – strongly agree):

(1) For a long time, our supply chain retains the same stable situation as it had before

changes occur.

(2) When changes occur, our supply chain grants us much time to consider a reasonable

reaction

(3) Without adaptations being necessary, our supply chain performs well over a wide

variety of possible scenarios.

(4) For a long time, our supply chain is able to carry out its functions despite some damage

done to it.

Anticipation and preparedness

To what extent do the statements apply to your supply chain?

Question (1 – strongly disagree; 7 – strongly agree):

(1) Our supply chain actively and regularly reviews market trends and behaviour to anticipate

changes.

(2) Our supply chain actively and regularly reviews internal processes for visibility of each of

the process steps to improve visibility when problems occur.

(3) Our supply chain actively and regularly adjusts processes and strategies based on market

trends and behaviour to anticipate changes.

Supply chain’s customer value (adapted from Kroes and Ghosh, 2010)

Please indicate the level of your company’s performance along each of the following

dimensions compared to that of your competitors.

Copyright UCT

22 | P a g e

(1 – worse than competitors; 7 – better than competitors):

(1) Missing/wrong/damaged/defective products shipped.

(2) Warranty/returns processing costs.

(3) Conformance to customer specifications.

(4) Customer satisfaction.

C. SAMPLING

Data has been collected from small, medium and large manufacturing firms which are

partially or exclusively based in South Africa. Where these are only partially based in South

Africa, only respondents based in South Africa have been surveyed. Respondents are

currently holding functions within or closely related to supply chain management. Contact

details of the respondents were looked up through an industry database, online research and

my own network. The research aimed to achieve at least 50 responses, representing a

response rate of 15-20% in line with average response rates observed by Braunscheidel et al.

(2009). Therefore, the survey was sent to 350 potential respondents. This has yielded an

overall number of 55 responses. The quality of all responses is good as no questions were left

unanswered. This equates to a response rate of 15.71%, which is considered sufficient to

meet average response rate requirements of Braunscheidel et al. (2009), but is still below the

response rate of 19.8 % achieved by Wieland et al. (2013).

D. RESEARCH CRITERIA

To yield a comparable outcome of the research of Wieland et al. (2013), adapted analysis

methodologies of Chen and Paulraj (2004) for measure of communication, of Morris and

Carter (2005) for cooperation and of Frohlich and Westbrook (2001) for integration have

been used for the analysis of the data received. New research has been conducted to evaluate

disadvantages of integration. Wieland et al. (2013) used their own methodology (2012) for

measurement of the robustness, agility and value to the customer of the respective supply

chain.

Copyright UCT

23 | P a g e

E. DATA ANALYSIS METHODS

This research will measure predictive validity of the study through the use of the model of

Rossiter (2008), in line with the approach of Wieland et al. (2013). Reliability of the

measurement model has been tested further through Cronbach’s α (Morris & Carter, 2005;

Nunnally, 1978).

Table 3: Cronbach's α

Cronbach's alpha Internal consistency

α ≥ 0.9 Excellent

0.7 ≤ α < 0.9 Good

0.6 ≤ α < 0.7 Acceptable

0.5 ≤ α < 0.6 Poor

α < 0.5 Unacceptable

Discriminant and convergent validity have been tested through the model of Fornell et al.

(1981), which has also been used by Wieland et al. (2013).

For further consistency, this has applied structural equation modelling (SEM) for

hypothesis testing (Bagozzi & Yi, 1988; Fornell & Larcker, 1981) by using Amos 20 (Shah

& Goldstein, 2006) for composite reliability.

Figure 1: Structural equation modelling - hypotheses (expanded from Wieland et al., 2013)

Copyright UCT

24 | P a g e

7. ANALYSIS

The variables of the survey have been coded as follows, for interpretation:

Table 4: Coding of questions and sub-questions

Construct Code Sub-Questions

Communication CM CM 1-4

Cooperation CP CP 1-4

Integration IT IT 1-4

Agility AD AD 1-4

Robustness RB RB 1-4

Supply Chain SC SC 1-4

Based on the theoretical measurement model, with the exception of the exogenous variable

CP4 (Cooperation: “We do not mind owing each other favours.”), all variables are,

statistically, significantly associated with the constructs they are hypothesized to be

associated with:

A. COMMUNICATION

In the Structured Equation Model, CM1 – CM4 load significantly on the construct

„Communication‟ with coefficients 0.622, 0.817, 0.618 and 0.308, respectively.

Table 5: Communication variable analysis

Variable Summary for scale: Mean=20.9815 Std.Dv.=3.70869 Valid N:54

Cronbach‟s alpha: .685008 Standardized alpha: .696871

Average inter-item corr.: .372054

Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

CM1 15.64815 8.524348 2.919649 0.503118 0.599107

CM2 15.85185 8.533607 2.921234 0.537875 0.580855

CM3 15.57407 8.059328 2.838896 0.520047 0.585167

CM4 15.87037 8.446158 2.906228 0.343479 0.713549

Copyright UCT

25 | P a g e

B. COOPERATION

In the Structured Equation Model, CP1 – CP3 load significantly on the construct

„Cooperation‟ with respective coefficients 0.701, 0.547 and 0.462.

Table 6: Cooperation variable analysis




Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

CP1 13.65455 8.40793 2.899644 0.382070 0.349847

CP2 13.87273 8.94744 2.991227 0.378481 0.357903

CP3 12.18182 11.31240 3.363391 0.360557 0.416774

CP4 12.81818 10.47603 3.236670 0.141435 0.589681

C. INTEGRATION

In the Structured Equation Model, IT1 – IT4 load significantly on the construct „Integration”

with coefficients 0.856, 0.418, 0.661 and 0.479, respectively.

Table 7: Integration variable analysis




Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

IT1 12.07547 15.76789 3.970880 0.634734 0.558385

IT2 11.20755 19.48523 4.414207 0.413132 0.695327

IT3 11.22641 16.17515 4.021834 0.517546 0.637028

IT4 10.83019 19.19758 4.381504 0.434835 0.683603

D. AGILITY

In the Structured Equation Model, AD1 – AD4 also load significantly on the construct

„Agility‟ with respective coefficients 0.525, 0.731, 0.761 and 0.748.

Copyright UCT

26 | P a g e

Table 8: Agility variable analysis




Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

AD1 12.70909 16.71537 4.088444 0.451151 0.771715

AD2 12.14545 14.56066 3.815843 0.605890 0.691391

AD3 12.12727 15.52926 3.940718 0.593880 0.699485

AD4 13.09091 14.62810 3.824670 0.627904 0.679390

E. ROBUSTNESS

In the Structured Equation Model, RB1 – RB4 load significantly on „Robustness‟ with

coefficients 0.610, 0.640, 0.754 and 0.648, respectively.

Table 9: Robustness variable analysis




Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

RB1 13.01852 13.05521 3.613200 0.515772 0.684494

RB2 13.79630 12.75480 3.571386 0.544044 0.667706

RB3 12.81481 13.22497 3.636615 0.558646 0.660072

RB4 12.70370 13.87517 3.724939 0.495654 0.694983

F. SUPPLY CHAIN

In the Structured Equation Model, SC1 – SC4 load significantly on „Supply Chain‟ with

coefficients 0.678, 0.709, 0.774 and 0.714, respectively.

Table 10: Supply chain customer value variable analysis




Mean if

deleted

Var. if

deleted

StDv. if

deleted

Itm-Totl

Correl.

Alpha if

deleted

SC1 15.07273 11.52198 3.394405 0.575708 0.760544

SC2 15.41818 11.22512 3.350392 0.585366 0.756626

SC3 14.90909 11.35537 3.369773 0.681952 0.710131

SC4 14.81818 11.67603 3.417021 0.590863 0.752633

Copyright UCT

27 | P a g e

G. STRUCTURAL EQUATION MODELLING ANALYSIS

A detailed supporting analysis can be found in Appendix III. Below table comments on

supported hypotheses, as well as their significance, and underlying data used.

Table 11: Overview supported and unsupported hypotheses

Hypothesis Title Supported

Y/N

Supporting

data Comment

H1a

Communication

positively

affects agility Y

Standardised

path coefficient

0.448 (p-value <

0.01)

Supported and

statistically significant

H1b

Cooperation

positively

affects agility

N Results statistically not

significant

H1c

Integration

positively

affects agility

N p < 0.248 Low significance but

negative effect on agility

evident

H2a

Communication

positively

affects

robustness


significant

H2b

Cooperation

positively

affects

robustness


significant

H2c

Integration

positively

affects

robustness

N p < 0.171 Low significance but

negative effect on

robustness evident

H3a

Agility

positively

affects the value

of the supply

chain customer

Y

Standardised

path coefficient

0.278 (p-value <

0.06)

Supported and


H3b

Robustness

positively

affects supply

chain customer

value

Y

Standardised

path coefficient

0.523 (p-value <

0.001)

Supported and


H. COMMON-METHOD VARIANCE

No indications of a non-response or late-response bias could be found, in line with suggested

tests of Williams et al. (2010) and John et al. (1997) that look to identify a common-method

variance.

Copyright UCT

28 | P a g e

8. INTERPRETATION

A. CHARACTERISTICS OF THE EXAMPLE

55 responses were received. All responses were deemed suitable. Most responses received

were from respondents from the Western Cape (63.64%), followed by the Gauteng Region

(27.27%). Only 4 responses were received from KwaZulu-Natal (7.27%), and a single

response from North West (1.81%). The single response from North West reflected no

significant difference to be viewed as an outlier in comparison to other regions, and did not

allow for any regional conclusion to be derived. However, any difference would not be

viewed as significant due to the sample size in the region.

B. MEASUREMENT MODEL

1. OVERALL MODEL FIT

Due to the sensitivity to large sample sizes and departure from normality of the Chi-Square

goodness of fit test (from the Normality plot in figure 1 above, there is an apparent departure

from normality), attention is focused on the Root Mean Square Error (RSME) and the

Goodness of Fit Index (GFI) statistics. While the RSMEA = 0.076 suggests an excellent fit,

the GFI = 0.656 as well as the population Gamma Index of 0.895 suggest a reasonably good

fit.

2. PARAMETER ESTIMATES

The Cronbach‟s alpha values suggest the reliability of the measuring tools. The table below

shows for each construct, the Cronbach‟s alpha and the corresponding composite reliability.

Table 12: Constructs’ reliability

Construct Cronbach’s Alpha Composite Reliability

Communication (CM) 0.69 0.69

Cooperation (CP) 0.51 0.54

Integration (IT) 0.71 0.71

Agility (AD) 0.77 0.79

Robustness (RB) 0.74 0.76

Supply Chain (SC) 0.8 0.81

Copyright UCT

29 | P a g e

C. STRUCTURAL EQUATION MODEL

1. TESTING H1A – H1C (AGILITY)

Again results from the structural equation modelling support the hypothesis H1b that

Cooperation has a positive effect on Agility. The standardized path coefficient is 0.448 (p-

value < 0.01). This gives support to the claim of Sambasivan et al. (2013) that transaction

cost theory, contingency theory, personal relationship theory and social exchange theory are

of value towards overall agility of a supply chain.

However, the relationships found between H1a - Communication and Agility, and H1c -

Integration and Agility, are not statistically significant and therefore neither support nor

disagree with the observations of Ritchie et al. (2007), who see a positive impact of early

communication to fast adaptation of the entire supply chain to changes.

2. TESTING H2A – H2C (ROBUSTNESS)

Analysis of data has found that all three of these hypotheses are not supported by the findings

from the structural equation modelling, i.e. the tests are not significant. However, there

appears to be a negative effect of Integration on both Robustness and Agility, where the p-

values in both these cases are p < 0.171 and p < 0.248, respectively. This result is in contrast

to the claims of Frohlich et al. (2001) who see a positive impact of integration on robustness,

and instead supports the observations of Matopoulos et al. (2007) and Parker et al. (2015)

who see disadvantages of integration in partnerships with a power imbalance in the

relationship, negatively affecting robustness and new product development performance.

3. TESTING H3A AND H3B (VALUE OF THE SC

CUSTOMER)

The structural equation modelling results for the structural model reveal that both H3a

(Agility) and H3b (Robustness) have a positive effect on „Supply Chain‟. The respective

standardized path coefficients are 0.523 (p-value < 0.001) and 0.278 (p-value < 0.06),

respectively. The hypotheses H3a and H3b are thus supported, where H3a in turn supports the

Copyright UCT

30 | P a g e

views of Braunscheidel et al. (2009) who see a complementary effect on agility on the value a

supply chain customer receives, where a stronger focus on market orientation and learning

orientation is present. H3b supports the views of Hamel et al. (2003), who state that a robust

firm that provides room for exploration, design and implementation of new business models,

can have a positive effect on the value a supply chain customer receives.

D. CORRELATION ANALYSIS

Reviewing correlations between the variables (CM, CP, IT) and (AD, RB), separating them

into grouping variables “Years of experience 1” and “Years of experience 2”, it was found

that despite the fact that none of the relationships depicted by the correlation coefficients in

each of the four tables displays statistical significance, there is still an indication that years of

experience affects the strength and nature of the relationship between the constructs the

variables (CM,CP,IT) and (AD,RB).

Taking a closer look at correlations between the variables (RB, AD) and SC, I have observed

that “Years of experience 1” affects the relationships between (AD, RB) and SC with the

relationships being particularly strong and statistically significant for the category “11 years

or more”. From the four tables in Appendix II, one can argue that the more the number of

years of experience 1, the stronger the relationships between both Agility and Robustness on

the value of the Supply Chain Customer.

Once again, as per the results from the four tables (Appendix II), it can be observed that,

reviewing correlation between variables (RB, AD) and SC, “Years of experience 2” has an

effect on the dependence structure between (“Agility”, “Robustness”) and “Supply Chain”.

Copyright UCT

31 | P a g e

9. FINDINGS & DISCUSSION

A. BASE CASE SCENARIO

Respondents have been asked to share the number of years they have experience in the SC

sector; data collected has been reviewed with regards to differences in responses based on the

years of experience in the field. 85.4% of all respondents had 3 years or more experience in

supply chain management or a directly related function, 61.8% had 6 years or more

experience, and 34.5% had 11 years or more experience. No significant outliers were

identified. Based on this, and based on the absence of outliers in response data given by

respondents with 0-2 years‟ experience, it can be assumed that the surveyed population is

sufficiently qualified to provide quality data.

Table 13: Work experience

How many years of work experience do you have in in supply chain management?

Answer Options Response Percentage Response Count

0-2 years 14.5% 8

3-5 years 23.6% 13

6-10 years 27.3% 15

11 years or more 34.5% 19

answered question 55

skipped question 0

Where the number of years of experience in the field can be deemed sufficient to assure

quality of responses, the survey further explored the number of years the respondents

gathered experience in the field whilst being with their current employer. The largest number

of responses were made in the range of 0-2 years (36.4 % of all responses) and 3-5 years

(34,5 years of experience). With the highest number of responses for general experience in

the field being 11 years or more (34,5 %), followed by 6-10 years (27.3%), it can be

concluded that most respondents have gained their experience with more than one employer.

This suggests a more balanced view on the questions asked than if respondents had only

worked with one employer.

Copyright UCT

32 | P a g e

B. EFFECT OF FIRM EXPERIENCE

Table 14: Work experience in current firm

How many years of work experience in supply chain management do you have in your

current firm?

Answer Options Response Percent Response Count

0-2 years 36.4% 20

3-5 years 34.5% 19

6-10 years 18.2% 10

11 years or more 10.9% 6

answered question 55

skipped question 0

Reviewing correlation coefficients of agility and robustness showed no statistical

significance. There is however an indication that the number of years of industry experience

have an effect on the strength and nature of the relationship of the constructs of the variables.

With an increasing number of years of overall industry experience, the believed relationship

of both agility and robustness with the value that a supply chain offers a customer,

strengthens significantly. This was further supported by analysing the second set of years of

experience measured, where respondents were surveyed on number of years in their

respective role at their current employer, which yielded similar results.

C. DISCUSSION

A number of hypotheses did not show statistical significance, there was however evidence

that indicates the support of some hypotheses, and in one instance, a negative effect instead

of the assumed positive effect of a variable. The results show several important conclusions.

Firstly, Communication appears not to have the same positive effect on Agility and

Robustness as it was observed in the guiding study to this research conducted in the South

African context. Secondly, a negative effect of integration on agility and robustness has been

identified. Thirdly, respondents of this study view the effects of agility and robustness on

value of the supply chain customer in the same positive light as respondents from the

Germanic markets surveyed in the guiding study of Wieland et al. (2013).

Copyright UCT

33 | P a g e

Figure 2: Structural equation modelling - supported and unsupported hypotheses

1. COMMUNICATION AND EFFECTS ON AGILITY AND

ROBUSTNESS

In contrast to the findings of Wieland and Wallenburg (2013), the findings of this study do

not show further support of the research of Barratt et al. (2007) who found that

communication improves quality of proactive and reactive measures to address challenges a

supply chain may face. Quality of communication has viewed as of high importance in the

observation of the aforementioned authors, which underlines the risk of lower quality

communication, where information may be withheld for the fear of reputational damage or

damage to overall harmony (Das & Kumar, 2009), which can result in inadequate assessment

of a disruption where partners up and down the supply chain fail to receive important

information (Hendricks et al., 2009). Also, early quality communication has been viewed

Copyright UCT

34 | P a g e

likely to mitigate this risk (Ritchie et al., 2007); again, no clear support of this claim could be

found in this research. When on the receiving side of information, higher volatility in

business and market risk due to dependence on mature economies and exchange rate

fluctuations (Weber, Siebenmorgen, & Weber, 2005) may have an effect in the trust in

communication provided due to insufficient or incomplete communication provided (Das et

al., 2009). The different views of the effect of communication on robustness between findings

of both this study and the pilot this study, provide a difference in perception of the

significance of communication, especially with increasing levels of supply chains that are

constantly optimised through lean work force and inventory levels, as stated by Jüttner et al.

(2011).

As mentioned above, psychological factors appear likely to have influenced results to an

extent. What may contribute to this different view on open communication of challenges and

problems is what has been observed by Pflug (2009), where subjective well-being of German

respondents was, amongst other points, strongly based on freedom and free expression. In

contrast, South African respondents strongly valued harmony over the aforementioned

freedom, which may indicate a tendency to withhold negative information and open

communication in order to maintain harmony (Das et al., 2009), subsequently giving

communication less weight. Therefore, a different type of psychological contract may be

present in South African business relationships (Dawkins & Ngunjiri, 2008), with different

levels of equity sensitivity (Restubog et al., 2007) that further impacted the outcome of this

study. Similar behaviour has been observed in the Japanese social culture (Lu & Gilmour,

2004).

2. COOPERATION, INTEGRATION AND EFFECTS ON

AGILITY AND ROBUSTNESS

External business partners up and down the supply chain are believed to provide higher levels

of commitment when involved in constant communication and relationship management

(Cousins et al., 2006), the first of which would not be supported as described above.

Furthermore, the hypothesis that assumes a positive effect of integration on agility is not

supported either. This, however, in return supports the findings of Matopoulos et al. (2007),

who pointed out the dangers of integration, where a stronger side of the business relationship

Copyright UCT

35 | P a g e

may exercise their power on their counterpart, and where integration may come with a

mismatch of governance levels. Integration of smaller entities has been viewed as a challenge

to introduce balanced corporate governance, which could affect robustness and agility

throughout the supply chain, not only through reduced visibility. Crona et al. (2010) provide

a different view with their claim that this may have a lesser impact on companies that already

have a strong integration culture. It could also be of positive nature, which was observed in

both Spain and the Netherlands with companies that have a highly complex supply chain set

up (Gimenez, Van Der Vaart, & Van Donk, 2012), where companies with a less complex

supply chain set up could not show measurable benefits of integration. The authors however

note that there is still a negative impact on cost performance, where structured

communication means are used to achieve supply chain integration.

Parker and Brey (2015) have also picked up on this trend in technology-based firms, where

formal contractual governance has a mitigating effect on to negative collaboration cost (Artz,

1999). Where collaboration cost is negatively affected, through factors such as lost time due

to higher levels of complexity in knowledge sharing, combined with exposure to opportunism

and knowledge loss, as observed by one of the authors of this study in one of his other works

(Wallenburg & Schäffler, 2014), a further negative impact on new product development

performance has been identified. Further impacts of formal governance in context of short-

term performance output have been observed, in the findings of Zhao et al. (2014), who

reviewed the performance of various high tech firms in context of black box supplier

integration.

To address and prepare for vulnerability factors as per the model of Pettit et al. (2010), it

appears to be of importance to incorporate factors of capability together with all stakeholders

in the supply chain network, in line with the findings of Chen et al. (2004), who further

suggest collaboration simultaneously with dispersion, which again underlines a potential

mitigation of the negative impact that integration may have.

Different levels of ethnic and cultural diversity are present in the work place in Southern

African countries when compared to European countries (Fearon, 2003), but those

differences have not been found to have an impact of positive or negative nature on

innovation (Østergaard, Timmermans, & Kristinsson, 2011). The authors however believe

Copyright UCT

36 | P a g e

that wider age gaps have a negative impact on innovation performance, where educational

and gender diversity show a positive impact. Based on low levels of average life expectancy

in South Africa (CIA, 2016) and high levels of youth unemployment, age gaps in the South

African work force are significantly lower than those in Germanic markets, which should

have a positive effect on innovation, which is in turn positively influenced by cooperation

(Østergaard et al., 2011).

Governance is of high relevance as a countermeasure to aforementioned risks (Crona et al.,

2010). Zhao et al. (2014) further suggest that a balance between relational governance and

formal governance needs to be established to mitigate the negative impacts and create an

environment where both levels of governance aid to complement each other to reduce the

overall cost of collaboration/cooperation. A positive balance can be established through

formal agreements that support joint actions agreed upon through relational governance.

3. EFFECTS ON VALUE OF THE SC CUSTOMER

The findings of this study further support the statement that agility, broken down into speed

and visibility (Braunscheidel et al., 2009), is believed to have a positive effect on the value

the supply chain customer receives (Barratt & Oke, 2007). Specifically looking at the group

of respondents with experience of 11 years or more, there appears to be an even stronger

relationship between robustness, agility and SC customer value. This is in line with the

statement of the authors that views market orientation and learning orientation of the firm as

having a significant impact on a supply chains‟ agility, amongst volume and mix flexibility,

especially during unforeseen circumstances (Kleindorfer & Saad, 2005). Where integration is

believed to have a negative effect on robustness and agility as per the findings of this study,

outsourcing and dispersion may mitigate the negative effects of these, but these would need

to be aligned to the overall strategy of the firm to avoid creating additional problems by

attempting to resolve the primary concern about integration (Kroes et al., 2010).

Furthermore, the stronger emphasis on cooperation in South African may mitigate some of

the negative effects of the lower level of perceived value of communication to sustain a

positive impact of both agility and robustness on the value of the SC customer.

Copyright UCT

37 | P a g e

10. LIMITATIONS

Responses of the survey are self-evaluated and therefore of subjective nature. Similar to this

research, Wieland et al. (2013) conducted their research up and down the supply chain,

allowing for statements regarding vertical relationships only. In light of emerging markets, it

may be interesting to additionally review robustness and agility in the context of demand side

challenges (Nagurney et al., 2005), where buying power and ability of the customer in a

young or emerging economy could be of higher volatility than in a mature economy

(Arellano, 2008). Although requesting feedback from the South African manufacturing

sector, cross-sectional drawbacks may exist, depending on demographics, size, location and

maturity of the business, as well as differences in experience and positions amongst the

respondents. Market size difference may also have an impact.

It also needs to be noted that a share of the questions in the questionnaire refer to the current

employer only, where responses may not reflect the full range of experiences a respondent

has had. Adjustment of the survey to distinguish between experiences in previous supply

chain management positions and the current position, may have given a different overview.

In contrast, this may have diluted the impact of the response where a survey question aims to

receive perspective about the current employer.

11. CONCLUSION

In contrast to the research results of Braunscheidel et al. (2009) and Paulraj et al. (2007), this

research could not find significant supporting evidence of a positive relation of integration to

robustness and agility but rather a negative relation, similar to what was experienced in the

guiding research to this paper (Wieland et al., 2013), in line with observations made in Spain

and the Netherlands. It can be concluded that cultural differences only have a limited effect

on this view.

On the other hand, the relevance of communication appears to be less valued by South

African respondents in comparison to Germanic respondents. This difference may be of

cultural nature, as observed in South African (Pflug, 2009) and Japanese (Lu et al., 2004),

Copyright UCT

38 | P a g e

where harmony may have more weight than free and open expression, potentially leading to

information withheld (Das et al., 2009)

As previously underlined by Wieland et al. (2012, 2013), there is evidence in research that

supply chains can only function effectively when adjusted in anticipation of problematic

environments (Christopher et al., 2011). Research conducted in this paper supports some of

the previous observations of Wieland et al. (2012) indicating that, amongst other factors, both

elements of resilience, namely robustness as preventative measure and agility as reactive

measure, have a positive impact on the value of the supply chain customer. With regards to

both measures, specifically cooperation is believed to have a positive effect on agility.

Research performed in the Netherlands and Spain has shown similar results, where a positive

effect of integration could only be found in context of highly complex supply chain structures

(Gimenez et al., 2012). This underlines that this is not likely an effect only measurable in the

African and Germanic context. This paper expanded the pilot study based on suggested future

research, where it surveyed and analysed disadvantages of integration. The results strengthen

the finding that integration is not only of low significance to the quality of agility; it is

perceived to have a negative effect on both robustness and agility. As mentioned, there may

however be an exception with firms that are built around a strong integration focus, as well as

those with a highly complex SC set up.

Hamel et al. (2003) believe that a firm, whilst striving towards operating as lean as

economically viable, also needs to make space for change to introduce new business models

before it becomes obvious that they have to change, may it even be for small experiments

only. This is of high importance for both robustness and agility of a supply chain. Again, a

shift in perspective is important to see that constant renewal may be just as important as

constant improvement. When looking at factors that could potentially influence this, I have

consulted various other studies. Different levels of ethnic and cultural diversity in South

Africa (Fearon, 2003), have not been found to have an impact of positive or negative nature

on innovation, only age gaps have been found to have an impact on innovation performance

(Østergaard et al., 2011). Low levels average life expectancy (CIA, 2016) paired with high

levels of youth unemployment, age gaps in the South African work force are significantly

lower than those in Germanic markets, positively impacting innovation, which is in turn

Copyright UCT

39 | P a g e

positively influenced by cooperation (Østergaard et al., 2011), that may mitigate some of the

negative effects of lower levels of communication.

A. CONTRIBUTIONS TO THE LITERATURE

The above research is the first of its kind in South Africa. It aims to add to the understanding

how relational competence affects the resilience of a supply chain in the South African

context, and attempts to better the understanding of the theory and has identified similarities

and differences between mature European markets and emerging African markets, with the

South African example as one of the stronger economies in sub-Saharan Africa. It attempts to

find an explanation of the difference in results, and finds supporting evidence that differences

may be influenced by cultural and psychological factors as well as the average age gap

difference between Germanic work forces and those in South Africa.

B. MANAGERIAL IMPLICATIONS

This study illustrates to supply chain managers in South Africa that relational competence has

an effect on supply chain resilience as in mature Germanic market, but not necessarily with

equal weighting. There is a lower trust into the importance of communication than what was

observed in the guiding study, where impacts of integration were viewed equally negative.

This is not only useful in itself, it may also add to the understanding of relationship qualities

along an international supply chain that links the South African market with these mature

Germanic markets, especially with regards to the differences in value of communication

towards the robustness and agility of a supply chain. The study further offers insight that

lower age gaps in the work force have a positive effect on innovation.

Copyright UCT

40 | P a g e

12. FUTURE RESEARCH

Future research could apply the methodology of this research to other African countries to see

if there are patterns that differentiate the findings of African countries from those of the

Germanic market, or to expand the research to non-Germanic developed markets within

Europe to further compare these to the African context, as was done by Gimenez et al. (2012)

for Spain and the Netherlands for disadvantages of integration already. Furthermore, a

comparative study to identify differences between industries in comparison to the

manufacturing sector, may give further insights into applicability of the findings, where some

industries may have higher exposure to external factors, especially with internationally linked

supply chains (Manuj et al., 2008). Specific to this research, it may be of interest to explore

the disadvantages of integration in the manufacturing sector in line with the research

performed by Parker et al. (2015), who have already observed power asymmetry impacts in

the information technology sector in South Africa.

Copyright UCT

41 | P a g e

BIBLIOGRAPHY

Abor, J., & Quartey, P. (2010). Issues in SME development in Ghana and South Africa.

International Research Journal of Finance and Economics, 39(39), 218–228.

Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firms‟ resilience to supply chain

disruptions: Scale development and empirical examination. Journal of Operations

Management, 33-34, 111–122.

Arellano, C. (2008). Default risk and income fluctuations in emerging economies. American

Economic Review, 98(3), 690–712.

Artz, K. W. (1999). Buyer-supplier performance: The role of asset specificity, reciprocal

investments and relational exchange. British Journal of Management, 10(2), 113–126.

Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of

the Academy of Marketing Science, 16(1), 74–94.

Barratt, M., & Oke, A. (2007). Antecedents of supply chain visibility in retail supply chains:

A resource-based theory perspective. Journal of Operations Management, 25(6), 1217–

1233.

Blumenfeld, D. E., & Inman, R. R. (2009). Impact of absenteeism on assembly line quality

and throughput. Production and Operations Management, 18(3), 333–343.

Braunscheidel, M. J., & Suresh, N. C. (2009). The organizational antecedents of a firm‟s

supply chain agility for risk mitigation and response. Journal of Operations

Management, 27(2), 119–140.

Bruce, M., Daly, L., & Towers, N. (2004). Lean or agile: A solution for supply chain

management in the textiles and clothing industry? International Journal of Operatons &

Production Management, 24(2), 151–170.

Carter, L., & Gray, D. (2007). Relational competence, internal market orientation and

employee performance. The Marketing Review, 7(4), 385–400.

Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The

constructs and measurements. Journal of Operations Management, 22(2), 119–150.

Christopher, M., & Holweg, M. (2011). “Supply Chain 2.0”: Managing supply chains in the

era of turbulence. International Journal of Physical Distribution & Logistics

Management, 41(1), 63–68.

Christopher, M., & Peck, H. (2004). Building the resilient supply chain. International

Journal of Logistics Management, 15(2), 1–13.

Copyright UCT

42 | P a g e

CIA. (2016). Country comparison: Median age. In The World Factbook. Retrieved from

https://www.cia.gov/library/publications/the-world-factbook/fields/2177.html

Cousins, P. D., & Menguc, B. (2006). The implications of socialization and integration in

supply chain management. Journal of Operations Management, 24(5), 604–620.

Crona, B., & Bodin, Ö. (2010). Power asymmetries in small-scale fisheries: A barrier to

governance transformability? Ecology and Society, 15(4).

Das, T. K., & Kumar, R. (2009). Interpartner harmony in strategic alliances: Managing

commitment and forbearance. International Journal of Strategic Business Alliances,

1(1), 24–52.

Dawkins, C., & Ngunjiri, F. W. (2008). Corporate social responsibility reporting in South

Africa. Journal of Business Communication, 45(3), 286–307.

Ergun, Ö., Heier Stamm, J. L., Keskinocak, P., & Swann, J. L. (2010). Waffle House

Restaurants hurricane response: A case study. International Journal of Production

Economics, 126(1), 111–120.

Fabbe-Costes, N., & Jahre, M. (2007). Supply chain integration improves performance: The

emperor‟s new suit? International Journal of Physical Distribution & Logistics

Management, 37(10), 835–855.

Fearon, J. D. (2003). Ethnic structure and cultural diversity by country. Journal of Economic

Growth, 8(2), 195–222.

Flynn, B. B., Huo, B., & Zhao, X. (2010). The impact of supply chain integration on

performance: A contingency and configuration approach. Journal of Operations


Fornell, C., & Larcker, D. (1981). Evaluating structural equation models with unobservable

variables and measurement error. Journal of Marketing Research, 18(3), 39–50.

Frese, M., Krauss, S. I., Keith, N., Escher, S., Grabarkiewicz, R., Luneng, S. T., … Friedrich,

C. (2007). Business owners‟ action planning and its relationship to business success in

three African countries. Journal of Applied Psychology, 92(6), 1481–98.

Frohlich, M. T., & Westbrook, R. (2001). Arcs of integration: An international study of

supply chain strategies. Journal of Operations Management, 19(2), 185–200.

Gimenez, C., Van Der Vaart, T., & Van Donk, D. P. (2012). Supply chain integration and

performance: The moderating effect of supply complexity. International Journal of

Operations & Production Management, 32(5), 583–610.

Hamel, G., & Välikangas, L. (2003). The quest for resilience. Harvard Business Review,

Copyright UCT

43 | P a g e

81(9).

Hendricks, K. B., Singhal, V. R., & Zhang, R. (2009). The effect of operational slack,

diversification, and vertical relatedness on the stock market reaction to supply chain

disruptions. Journal of Operations Management, 27(3), 233–246.

Husdal, J. (2010). Managing risk in virtual enterprise networks: Implementing supply chain

principles. Hershey, PA: Business Science Reference.

John, T. M., Daniel, J. F., Mentzer, J. T., & Flint, D. J. (1997). Validity in logistics research.

Journal of Business Logistics, 18(1), 199.

Jüttner, U., & Maklan, S. (2011). Supply chain resilience in the global financial crisis: An

empirical study. Supply Chain Management: An International Journal, 16(4), 246–259.

Kleindorfer, P. R., & Saad, G. H. (2005). Managing risks in supply chains. Production and

Operations Management, 14(1), 53–68.

Knemeyer, A. M., Zinn, W., & Eroglu, C. (2009). Proactive planning for catastrophic events

in supply chains. Journal of Operations Management, 27(2), 141–153.

Kroes, J. R., & Ghosh, S. (2010). Outsourcing congruence with competitive priorities: Impact

on supply chain and firm performance. Journal of Operations Management, 28(2), 124–

143.

L‟Abate, L., Cusinato, M., Maino, E., Colesso, W., & Scilletta, C. (2010). Relational

competence theory: Research and mental health applications. Dordrecht, The

Netherlands: Springer.

Lu, L., & Gilmour, R. (2004). Culture and conceptions of happiness: Individual oriented and

social oriented SWB. Journal of Happiness Studies, 5(3), 269–291.

Manuj, I., & Mentzer, J. T. (2008). Global supply chain risk management strategies.

International Journal of Physical Distribution & Logistics Management, 38(3), 192–

223.

Matopoulos, A., Vlachopoulou, M., Manthou, V., & Manos, B. (2007). A conceptual

framework for supply chain collaboration: Empirical evidence from the agri-food

industry. Supply Chain Management: An International Journal, 12(3), 177–186.

Modi, S. B., & Mabert, V. A. (2007). Supplier development: Improving supplier performance

through knowledge transfer. Journal of Operations Management, 25(1), 42–64.

Morris, M., & Carter, C. R. (2005). Relationship marketing and supplier logistics

performance: An extension of the key mediating variables model. Journal of Supply

Chain Management.

Copyright UCT

44 | P a g e

Nagurney, A., Cruz, J., Dong, J., & Zhang, D. (2005). Supply chain networks, electronic

commerce, and supply side and demand side risk. European Journal of Operational

Research, 164(1), 120–142.

Norrman, A., & Jansson, U. (2004). Ericsson‟s proactive supply chain risk management

approach after a serious sub-supplier accident. International Journal of Physical

Distribution & Logistics Management, 34(5), 434–456.

Nunnally, J. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.

Olawale, F., & Garwe, D. (2010). Obstacles to the growth of new SMEs in South Africa: A

principal component analysis approach. African Journal of Business Management, 4(5),

729–738.

Omar, A., Davis-Sramek, B., Myers, M. B., & Mentzer, J. T. (2012). A global analysis of

orientation, coordination, and flexibility in supply chains. Journal of Business Logistics,

33(2), 128–144.

Østergaard, C. R., Timmermans, B., & Kristinsson, K. (2011). Does a different view create

something new? The effect of employee diversity on innovation. Research Policy, 40(3),

500–509.

Parker, H., & Brey, Z. (2015). Collaboration costs and new product development

performance. Journal of Business Research, (68(7)), 1653–1656.

Paulraj, A., & Chen, I. J. (2007). Strategic buyer–supplier relationships, information

technology and external logistics integration. Journal of Supply Chain Management,

(43(2)), 2–14.

Paulraj, A., Chen, I. J., & Lado, A. A. (2012). An empirical taxonomy of supply chain

management practices. Journal of Business Logistics, 33(3), 227–244.

Paulraj, A., Lado, A. A., & Chen, I. J. (2008). Inter-organizational communication as a

relational competency: Antecedents and performance outcomes in collaborative buyer-

supplier relationships. Journal of Operations Management, 26(1), 45–64.

Pecillo, M. (2016). The concept of resilience in OSH management: A review of approaches.

International Journal of Occupational Safety and Ergonomics, 22(2), 291–300.

Pesqueux, Y. (2012). Social contract and psychological contract: A comparison. Society and

Business Review, 7(1), 14–33.

Pettit, T. J., Fiksel, J., & Croxton, K. L. (2010). Ensuring supply chain resilience:

Development of a conceptual framework. Journal of Business Logistics, 31(1), 1–21.

Pflug, J. (2009). Folk theories of happiness: A cross-cultural comparison of conceptions of

Copyright UCT

45 | P a g e

happiness in Germany and South Africa. Social Indicators Research, 92(3), 551–563.

Ponomarov, S. Y., & Holcomb, M. C. (2009). Understanding the concept of supply chain

resilience. The International Journal of Logistics Management, 20(1), 124–143.

Prater, E., Biehl, M., & Smith, M. A. (2001). International supply chain agility - Tradeoffs

between flexibility and uncertainty. International Journal of Operations & Production

Management, 21(5/6), 823–839.

Restubog, S. L. D., Bordia, P., & Tang, R. L. (2007). Behavioural outcomes of psychological

contract breach in a Non-Western culture: The moderating role of equity sensitivity.

British Journal of Management, 18(4), 376–386.

Rindfleisch, A., Malter, A. J., Ganesan, S., & Moorman, C. (2008). Cross-sectional versus

longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing

Research, 45(3), 261–279.

Ritchie, B., & Brindley, C. (2007). Supply chain risk management and performance: A

guiding framework for future development. International Journal of Operations &

Production Management, 27, 303–322.

Rossiter, J. R. (2008). Content validity of measures of abstract constructs in management and

organizational research. British Journal of Management, 19(4), 380–388.

Sambasivan, M., Siew-Phaik, L., Abidin Mohamed, Z., & Leong, Y. C. (2013). Factors

influencing strategic alliance outcomes in a manufacturing supply chain: Role of

alliance motives, interdependence, asset specificity and relational capital. International

Journal of Production Economics, 141(1), 339–351.

Shah, R., & Goldstein, S. (2006). Use of structural equation modeling in operations

management research: Looking back and forward. Journal of Operations Management,

24(2), 148–169.

Skarmeas, D., Katsikeas, C. S., Spyropoulou, S., & Salehi-Sangari, E. (2008). Market and

supplier characteristics driving distributor relationship quality in international marketing

channels of industrial products. Industrial Marketing Management, 37(1), 23–36.

Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of

a firm: Scale development and model testing. Journal of Operations Management.

Swink, M., Narasimhan, R., & Wang, C. (2007). Managing beyond the factory walls: Effects

of four types of strategic integration on manufacturing plant performance. Journal of

Operations Management, 25(1), 148–164.

Thomas, D. C., Au, K., & Ravlin, E. C. (2003). Cultural variation and the psychological

Copyright UCT

46 | P a g e

contract. Journal of Organizational Behavior, 24, 451–471.

Unger, J. M., Keith, N., Hilling, C., Gielnik, M. M., & Frese, M. (2009). Deliberate practice

among South African small business owners: Relationships with education, cognitive

ability, knowledge, and success. Journal of Occupational and Organizational

Psychology, 82(1), 21–44.

Wallace, S. W., & Choi, T. M. (2011). Flexibility, information structure, options, and market

power in robust supply chains. International Journal of Production Economics, 134(2),

284–288.

Wallenburg, C. M., & Schäffler, T. (2014). The interplay of relational governance and formal

control in horizontal alliances: A social contract perspective. Journal of Supply Chain


Weber, E. U., Siebenmorgen, N., & Weber, M. (2005). Communicating asset risk: How name

recognition and the format of historic volatility information affect risk perception and

investment decisions. Risk Analysis, 25(3), 597–609.

Wei, H.-L., & Wang, E. T. G. (2010). The strategic value of supply chain visibility:

Increasing the ability to reconfigure. European Journal of Information Systems, 19(2),

238–249.

Wieland, A., & Wallenburg, C. M. (2012). Dealing with supply chain risks: Linking risk

management practices and strategies to performance. International Journal of Physical

Distribution & Logistics Management, 42(10), 887–905.

Wieland, A., & Wallenburg, C. M. (2013). The influence of relational competencies on

supply chain resilience: A relational view. International Journal of Physical

Distribution & Logistics Management, 43(c), 300–320.

Williams, L. J., Hartman, N., & Cavazotte, F. (2010). Method variance and marker variables:

A review and comprehensive CFA marker technique. Organizational Research

Methods, 13(3), 477–514.

Yang, Z., Aydın, G., Babich, V., & Beil, D. R. (2009). Supply disruptions, asymmetric

information, and a backup production option. Management Science, 55(2), 192–209.

Zhao, Y., Cavusgil, E., & Cavusgil, S. T. (2014). An investigation of the black-box supplier

integration in new product development. Journal of Business Research, 67(6), 1058–

1064.

Copyright UCT

47 | P a g e

APPENDIX I – BOX AND WHISKER PLOTS

Box & Whisker Plot

Communication

Median

25%-75%

Min-Max CM1 CM2 CM3 CM40

1

2

3

4

5

6

7

8

Figure 3: Box & whisker plot – Communication

Box & Whisker Plot

Cooperation

Median

25%-75%

Min-Max CP1 CP2 CP3 CP40

1

2

3

4

5

6

7

8

Figure 4: Box & whisker plot – Cooperation

Copyright UCT

48 | P a g e

Box & Whisker Plot

Integration

Median

25%-75%

Min-Max IT1 IT2 IT3 IT40

1

2

3

4

5

6

7

8

Figure 5: Box & whisker plot - Integration

Box & Whisker Plot

Agility

Median

25%-75%

Min-Max AD1 AD2 AD3 AD40

1

2

3

4

5

6

7

8

Figure 6: Box & whisker plot - Agility

Copyright UCT

49 | P a g e

Box & Whisker Plot

Robustness

Median

25%-75%

Min-Max RB1 RB2 RB3 RB40

1

2

3

4

5

6

7

8

Figure 7: Box & whisker plot - Robustness

Box & Whisker Plot

Supply Chain

Median

25%-75%

Min-Max SC1 SC2 SC3 SC40

1

2

3

4

5

6

7

8

Figure 8: Box & whisker plot - Supply chain value of a customer

Copyright UCT

50 | P a g e

Normal Probability Plot

Normalized Residuals

-3 -2 -1 0 1 2 3 4

Value

-4

-3

-2

-1

0

1

2

3

4

Exp

ecte

d N

orm

al V

alu

e

Figure 9: Normal probability plot - normalised residuals

Copyright UCT

51 | P a g e

APPENDIX II – CORRELATION EXPERIENCE (YEARS)

Table 15: Correlations (CM/CP/IT) and (AD/RB) - Years of experience 1

Variable

Correlations

Variable

Correlations

Include condition:

YEXP1=”0-2 years”

Include condition:


p = AD RB

p = AD RB

CM -0.180969 -0.438363

CM 0.183907 -0.153141

CP 0.040887 -0.17449

CP 0.223327 -0.080204

IT -0.25853 -0.135532

IT -0.035413 -0.454244

Variable

Correlations

Variable

Correlations

Include condition:


Include condition:

YEXP1=”11 years or more”

p = AD RB

p = AD RB

CM -0.116084 0.047967

CM 0.201366 0.084977

CP 0.446872 0.340022

CP 0.080694 -0.205875

IT 0.206196 0.116844

IT 0.425748 -0.058329

Table 16: Correlations (RB/AD) and SC - Years of experience 1

Variable

Correlations

Variable

Correlations

Marked correlations are

significant at p < .05000 Marked correlations are

significant at p < .05000

N=7 (Case wise deletion of

missing data) N=13 (Case wise deletion of

missing data)

Include condition: YEXP1=”0-

2 years” Include condition: YEXP1=”3-

5 years”

SC

SC

AD -0.199072

AD 0.02379

RB 0.226245

RB 0.392174

Copyright UCT

52 | P a g e

Variable

Correlations

Variable

Correlations






missing data)

Include condition: YEXP1=”6-

10 years” Include condition:


SC

SC

AD 0.512909

AD 0.512909

RB 0.486368

RB 0.486368

Table 17: Correlations (CM/CP/IT) and (AD/RB) - Years of experience 2

Variable

Correlations

Variable

Correlations






missing data)

Include condition:

YEXP1=”0-2 years” Include condition:


AD RB

AD RB

CM 0.316596 -0.099316

CM -0.051484 0.143026

CP 0.260207 -0.020908

CP 0.651586 -0.030277

IT -0.112154 0.112206

IT 0.406603 -0.085478

Variable

Correlations

Variable

Correlations






missing data)

Include condition:



AD RB

AD RB

CM -0.076618 -0.295129

CM 0.152316 -0.453491

CP -0.501469 -0.06378

CP -0.544007 -0.230273

IT -0.248506 -0.303122

IT 0.622581 -0.591841

Copyright UCT

53 | P a g e

Table 18: Correlations (RB/AD) and SC - Years of experience 1

Variable

Correlations

Variable

Correlations






missing data)

Include condition:



SC

SC

AD 0.317849

AD 0.349327

RB 0.361872

RB 0.532085

Variable

Correlations

Variable

Correlations






missing data)

Include condition:



SC

SC

AD 0.570826

AD 0.681407

RB 0.789307

RB 0.336051

Copyright UCT

54 | P a g e

APPENDIX III: SEM ANALYSIS

Table 19: Structural equation model estimates

Model Estimates

Parameter

Estimate

Standard

Error

T

Statistic

Prob.

Level

(CM)-1->[CM1] 0.622 0.121 5.152 0.000

(CM)-2->[CM2] 0.817 0.117 7.004 0.000

(CM)-3->[CM3] 0.618 0.121 5.112 0.000

(CM)-4->[CM4] 0.308 0.149 2.065 0.039

(CP)-5->[CP1] 0.701 0.158 4.426 0.000

(CP)-6->[CP2] 0.547 0.153 3.563 0.000

(CP)-7->[CP3] 0.462 0.155 2.973 0.003

(CP)-8->[CP4] 0.154 0.172 0.893 0.372

(IT)-9->[IT1] 0.856 0.111 7.726 0.000

(IT)-10->[IT2] 0.418 0.136 3.063 0.002

(IT)-11->[IT3] 0.661 0.115 5.734 0.000

(IT)-12->[IT4] 0.479 0.130 3.678 0.000

(SC)-25->[SC1] 0.678 0.095 7.156 0.000

(SC)-26->[SC2] 0.709 0.090 7.902 0.000

(SC)-27->[SC3] 0.774 0.080 9.697 0.000

(SC)-28->[SC4] 0.714 0.089 8.024 0.000

(AD)-29->[AD1] 0.525 0.120 4.386 0.000

(AD)-30->[AD2] 0.731 0.089 8.214 0.000

(AD)-31->[AD3] 0.761 0.085 8.946 0.000

(AD)-32->[AD4] 0.748 0.087 8.609 0.000

(RB)-33->[RB1] 0.610 0.112 5.448 0.000

(RB)-34->[RB2] 0.640 0.108 5.946 0.000

(RB)-35->[RB3] 0.754 0.093 8.067 0.000

(RB)-36->[RB4] 0.648 0.107 6.074 0.000

(EPSILON1)-37-(EPSILON1) 0.541 0.128 4.211 0.000

(EPSILON2)-38-(EPSILON2) 0.497 0.127 3.911 0.000

(EPSILON3)-39-(EPSILON3) 0.401 0.124 3.248 0.001

(EPSILON4)-40-(EPSILON4) 0.491 0.127 3.863 0.000

(EPSILON5)-41-(EPSILON5) 0.725 0.125 5.776 0.000

(EPSILON6)-42-(EPSILON6) 0.465 0.130 3.572 0.000

(EPSILON7)-43-(EPSILON7) 0.421 0.130 3.249 0.001

(EPSILON8)-44-(EPSILON8) 0.441 0.130 3.397 0.001

(EPSILON9)-45-(EPSILON9) 0.628 0.136 4.603 0.000

(EPSILON10)-46-(EPSILON10) 0.590 0.138 4.278 0.000

(EPSILON11)-47-(EPSILON11) 0.432 0.141 3.070 0.002

(EPSILON12)-48-(EPSILON12) 0.580 0.138 4.199 0.000

(ZETA1)-49-(ZETA1) 0.624 0.153 4.086 0.000

(ZETA2)-50-(ZETA2) 0.764 0.161 4.732 0.000

(ZETA3)-51-(ZETA3) 0.935 0.088 10.612 0.000

(CM)-52->(AD) -0.024 0.167 -0.142 0.887

Copyright UCT

55 | P a g e

Model Estimates

Parameter

Estimate

Standard

Error

T

Statistic

Prob.

Level

(CP)-53->(AD) 0.448 0.173 2.592 0.010

(IT)-54->(AD) -0.187 0.162 -1.155 0.248

(CM)-55->(RB) -0.046 0.178 -0.256 0.798

(CP)-56->(RB) 0.093 0.194 0.480 0.632

(IT)-57->(RB) -0.234 0.171 -1.369 0.171

(AD)-58->(SC) 0.278 0.148 1.876 0.061

(RB)-59->(SC) 0.523 0.138 3.799 0.000

Table 20: Noncentrality fit indices

Noncentrality Fit Indices

Lower 90%

Conf. Bound

Point

Estimate

Upper 90%

Conf. Bound

Steiger-Lind RMSEA Index 0.049 0.076 0.099

Population Gamma Index 0.833 0.895 0.954

Adjusted Population Gamma Index 0.795 0.871 0.944

Table 21: Single sample fit indices

Single Sample Fit Indices

Value

Joreskog GFI 0.656

Independence Model Chi-Square 644.033

Independence Model df 276.000

Copyright UCT

Date post:	07-Jun-2018
Category:	Documents
Upload:	hoangkiet
View:	215 times
Download:	0 times

The impact of relational competence on South African...

Documents