Sharing Sales and Service Networks with Competitors: A Multiple-case Study in the Heavy Truck Industry

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IN THE FIELD OF TECHNOLOGY DEGREE PROJECT

INDUSTRIAL ENGINEERING AND MANAGEMENT AND THE MAIN FIELD OF STUDY

INDUSTRIAL MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM SWEDEN 2018,

Sharing Sales and Service Networks with Competitors

A Multiple-case Study in the Heavy Truck Industry

ALEXANDER ENGBLOM EMMA LUNDQUIST

KTH ROYAL INSTITUTE OF TECHNOLOGY

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Sharing Sales and Service Networks with Competitors

by

Alexander Engblom Emma Lundquist

Master of Science Thesis TRITA-ITM-EX 2018:185 KTH Industrial Engineering and Management

Industrial Management

SE-100 44 STOCKHOLM

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Att dela sälj- och servicenätverk med konkurrenter

av

Alexander Engblom Emma Lundquist

Examensarbete TRITA-ITM-EX 2018:185 KTH Industriell teknik och management

Industriell ekonomi och organisation

SE-100 44 STOCKHOLM

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Master of Science Thesis TRITA-ITM-EX 2018:185

Sharing Sales and Service Networks with Competitors

Alexander Engblom Emma Lundquist

Approved

2018-06-11

Examiner

Lars Uppvall

Supervisor

Bo Karlsson

Commissioner

Scania

Contact person

Jens Tullberg Oscar Wyckman

Abstract

In the heavy truck industry, sales and service networks play an important role.

Investments in network infrastructure are costly, and to be profitable, there is a critical service volume to fulfil. This is especially challenging in new markets, where service demand is low and uncertain. A possible solution is to share sales and service networks with competitors. Simultaneous cooperation and competition, co-opetition, is a complex and contradictory phenomenon that has been previously researched, but with focus on cooperation in input activities like R&D. This thesis investigates co-opetition in output activities, in sales and services, by analysing how heavy truck companies could form competitor partnerships in sales and service networks. This is done by a literature review and a multiple case study.

From the analysis, a framework for assessing and designing potential competitor partnerships is presented. The framework consists of seven factors that are significant for competitor partnership success in sales and service networks; cultural fit, non- competing products, compatible goals, excess network capacity, dedicated

salespersons, high commitment, and patient implementation. This thesis contributes to science by research on co-opetition research in output activities, and by a discussion on the meaning of competition, success and partnerships.

Key-words: Sales and service networks; competitor partnerships; competition;

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Examensarbete TRITA-ITM-EX 2018:185

Att dela sälj- och servicenätverk med konkurrenter

Alexander Engblom Emma Lundquist

Godkänt

2018-06-11

Examinator

Lars Uppvall

Handledare

Bo Karlsson

Uppdragsgivare

Scania

Kontaktperson

Jens Tullberg Oscar Wyckman

Sammanfattning

I tunga lastbilsbranschen spelar sälj- och servicenätverk en viktig roll. Investeringar i nätverken är dyra, och för att vara lönsamma krävs en viss servicevolym. Det är särskilt utmanande på nya marknader, där efterfrågan är låg och obestämd. En möjlig lösning är att dela sälj- och servicenätverk med konkurrenter. Simultant samarbete och

konkurrens, så kallad co-opetition, är ett komplext och motsägande fenomen, som tidigare forskats på med fokus på samarbete inom utveckling. Denna uppsats

undersöker co-opetition inom sälj, genom att titta på hur tunga lastbilsföretag kan ingå partnerskap inom sälj- och servicenätverk tillsammans med konkurrenter. Detta görs genom en litteraturgenomgång samt en flerfallstudie.

Utifrån vår analys presenteras ett ramverk för att bedöma och utforma potentiella partnerskap med konkurrenter. Ramverket består av sju viktiga faktorer för framgångsrika partnerskap inom sälj- och servicenätverk tillsammans med

konkurrenter; kulturell passform (cultural fit), icke-konkurrerande produkter, kompatibla mål, överbliven kapacitet i nätverket, dedikerade säljare, högt engagemang och

tålmodig implementering. Uppsatsen bidrar till forskning inom co-opetition inom sälj och med diskussion kring begreppen konkurrens, framgång och partnerskap.

Nyckelord: Sälj- och servicenätverk; partnerskap med konkurrenter; konkurrens;

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List of Figures

3.1 Illustration of research process . . . 23 7.1 Illustration of assessment tool . . . 62

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List of Tables

2.1 Resource types and firms’ structural preferences (Das and Teng,

2000) . . . 10

2.2 A typology of inter-partner resource alignments (Das and Teng 2000) . . . 11

4.1 Overview of global truck groups’ brand positioning . . . 31

5.1 Factors from literature review . . . 34

5.2 Factors from pre-study . . . 36

5.3 Analytical framework . . . 37

6.1 Short facts, Hino and Scania . . . 39

6.2 Summary of Hino and Scania’s joint marketing in Japan . . . 41

6.3 Short facts, Scania and Hino . . . 42

6.4 Summary of Scania’s distribution of Hino in South Korea . . . 44

6.5 Short facts, Scania and Volkswagen Commercial Vehicles . . . 45

6.6 Summary of Scania’s distribution of Volkswagen commercial vehicles in Switzerland . . . 46

6.7 Short facts, Volvo and Renault . . . 48

6.8 Summary of Volvo and Renault’s consolidation of networks in EMEA . . . 50

6.9 Short facts, Valtra Fendt . . . 52

6.10 Summary of Valtra and Fendt’s shared dealer network in Sweden 54 7.1 Summary of factor assessment . . . 57

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Acknowledgements

This master thesis has been conducted at KTH Royal Institute of Tech- nology’s School of Industrial Engineering and Management in Stockholm, Sweden, in cooperation with Scania CV AB in S¨odert¨alje, Sweden.

We would like to express our sincere gratitude towards Scania for giving us the opportunity to conduct our study in cooperation with them, with special thanks to our supervisors at Scania’s New and Strategic Project Markets group, Jens Tullberg and Oscar Wyckman. We would also like to thank Bo Karlsson and Lars Uppvall at KTH for guiding us through this process.

Further appreciation is warranted for those at Scania and elsewhere who have agreed to be interviewed or helped in other ways to make this thesis possible.

Lastly, we would like to thank all who have been opposing and critiquing our work throughout the process.

Alexander Engblom & Emma Lundquist June 2018

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Chapter 1 Introduction

This chapter introduces the research study, including background, problem statement, purpose and research questions. Furthermore, the delimitations of the study are described and the positioning of the research presented. The chapter ends with a discussion of the central concepts of competitor partnership and partnership performance.

1.1 Background

The heavy truck industry is a commercial goods industry, characterised by competition based on total cost of ownership¹(TCO), uptime² and other fea- tures creating tangible customer value (Mohr et al., 2016). Most customers choose transportation investments based on criteria linked to value for money, and customer loyalty is fostered mainly by reliability and service (Gnamm

& Lundgren, 2016). Intangible values such as brand are considered less relevant than in consumer goods markets, where brands are more associated with emotions and image (Brown et al, 2011). Still, the heavy truck industry involves strong brands, associated with tangible characteristics and experiences.

Increased possibilities to leverage scale within the heavy truck industry has resulted in an overall international consolidation, with a significant number of acquisitions and partnerships over the last few years (Schiller et al., 2016).

Today, a small number of consolidated groups are controlling the global heavy

1Financial estimate of all direct and indirect costs related to a product.

2The time during which a product is operational.

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truck industry, with regional di↵erences among the top players. Further mergers and acquisitions (M&A) and partnerships among competitors in the industry are anticipated, driven by market positioning and economies of scale, in particular within research and development (R&D), manufacturing and purchasing. (Schiller et al., 2016)

For many premium heavy truck companies, the product o↵er includes an extensive service o↵ering in addition to the vehicles. Such services include maintenance, repair, financing, fleet management systems and other connected services. Most of these services improve the customers’ TCO and uptime, which motivates higher prices for products with a better service of- fering. The lack of a well-established and reliable network that immediately handles customer emergencies can result in lost customers for truck manufac- turers. The lack of reliability and service is a deterrent for customer loyalty . Thus, the sales and service network plays an important role in establish- ing long-term, profitable relationships to customers. (Gnamm & Lundgren, 2016)

Due to this reason, heavy truck companies strive to establish extensive networks of sales and service locations in each geographic market for customers to fully realise their o↵ering. As these investments are substantial, there exists a critical service volume to fulfil in order to exist in a particular market.

That is, the minimum service volume required to repay the initial investment and stay profitable in the considered market. If the critical volume is not reached, the potential income is o↵set by the investment- and operational costs.

Historically, cooperation among rivals in the heavy truck industry has been concentrated to R&D, manufacturing and purchasing, where large investments have been shared among companies. As these functions are not directly connected to the customer interface, no brand recognition, service o↵ering or other factors related to customers’ perception or experience have been harmed. Large companies have been able to secure profits of large scale operations without interfering with the customer relations. With the di↵er- entiated nature of each brand’s o↵ering, competition has still occurred in the marketplace.

To form partnerships within sales and services might appear less attractive, but is a possible next step to increase service volume in new markets. The cost-saving benefits of such consolidation are quite obvious, but the risks involved can be difficult to assess. For instance, sharing workshop locations among competitors would clearly imply reduced investment costs for each company, but the e↵ect on the customer perception is less explored. Even a

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single bad experience can lead to substantial harm on the brand or customer relations and therefore it can be advantageous for heavy truck companies to control their own network. The brand and customer relations need to be protected and maintained carefully, since customers associate brands with certain characteristics, related to TCO and uptime. Another potential risk of cooperating in this sphere with competitors is the erosion of the competitive advantage of a superior network, by either weakening the network or improving a competitor’s o↵ering.

The closer to the customer you get in the value chain, the more likely companies are to compete rather than cooperate (Lindstr¨om and Polsa, 2016).

Since sales and services are closer to the customer in the value chain than for instance R&D, this could explain why companies are usually reluctant to form such competitor partnerships, but market circumstances could force these forms of cooperation. It might seem counter-intuitive since it involves two fundamentally opposite behaviours, but clearly there exists potential benefits as well, in terms of cost savings.

To share sales and service networks could also be of interest in the future due to the likely decrease of service need. Improvements in truck design imply enhanced service efficiency, advance of connected and autonomous trucks enables predictive maintenance and the expected shift to electrified power- trains reduces the number of moving parts in need of service. Meanwhile, the customers’ expectation on service presence has increased on a global level, following an expanding market and cross-border transportation. Con- sequently, heavy truck companies are facing increased challenges to maintain critical service volumes in local markets, which potentially could be solved by shared networks in the future.

1.2 Problem Statement

In markets with relatively low sales volumes but with a significant need for service presence, heavy truck companies face challenges in reaching a critical service volume to pay back initial investments and operating service costs.

To establish an extensive service network is particularly crucial for premium heavy truck brands with their after-market o↵ering, which can make new markets out of reach.

A solution to this is to share sales and service networks with competitors, so that investments and operating costs are reduced, while still ensuring local

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presence. Cooperation in output activities such as sales and services can be problematic though, as it is within these activities competition also takes place. Not least for premium brands is it a risk of losing brand image and customers’ trust if there is confusion by mixed brands. With the after-market services as a main selling argument, sharing these networks with a competitor might erode the competitive advantage obtained. Even cultural aspects and company loyalty can further increase the difficulties of such cooperation.

Albeit many potential conflicts, it is of interest to investigate this possibility, as it could be a solution to reach new markets and increase sales.

1.3 Purpose

The purpose of this report is to investigate success factors for competitor partnership in sales and service networks and describe how to assess and design a potential partnership in sales and service networks among heavy truck companies.

1.4 Research Question

How could heavy truck companies form successful competitor partnerships in sales and service networks?

To answer our research question, we need to investigate the following three sub-research questions:

1. What former and existing competitor partnerships in sales and service networks can be used for analysis?

2. What success factors can be identified for competitor partnerships in sales and service?

3. How could potential partnerships be assessed and designed, using identified success factors?

1.5 Delimitations

The recommendations and conclusions of this report are geared towards the heavy truck industry, and analysis is done with the heavy truck industry in

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mind. The partnerships considered are delimited to competitor partnerships within sales and service networks, hence no other partnership constellations are investigated. Other solutions to the problem of reaching a critical service volume, apart from forming competitor partnerships, are not investigated.

The focus of this report is on existing markets and current circumstances, why future implications of new technology, such as electrified power trains, and their e↵ects on sales and service networks, are not considered.

1.6 Positioning

This thesis contributes with insights on competitor partnerships in sales and services and related success factors for such cooperation. The research provides an understanding of how to form successful competitor partnerships in sales and service networks in the heavy truck industry, with focus on the mechanisms and implications present in shared sales and service networks with competing brands. We also contribute to research with a review of the dynamics of competition, cooperation and co-opetition and their implications on partnerships.

Although we focus on the heavy truck industry, the findings of this report could be applicable on other commercial goods industries that share characteristics with the heavy truck industry. While this thesis focuses on non- mature and complex markets in today’s business environment, the rationales for forming competitor partnerships could remain the same if expected technology shifts change the need for service.

1.7 Terminology Discussion

1.7.1 Competitor Partnership

When investigating the possibility of sharing sales and service networks with rivals, the area of analysis is the cooperation between the competing firms.

We define a competitor partnership as any form of mutual agreement between two competitors to achieve a set of objectives and benefits while re- maining autonomous organisations. This is similar to the alliance definition by Gomes-Casseres (2003), which is “any governance structure to manage an incomplete contract between separate firms and in which each partner has limited control”. Here the incompleteness of contracts determines the level of

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formality. As the contracts themselves are not required for our field of analysis, we include all forms of agreements; from handshakes to legal contracts, and the alliance concept is thereby stretched to that of partnerships. Despite this partnership term being somewhat wider than that of an alliance, prior research on alliance management is used as alliances have had a larger focus in the literature. However, the definitions of an alliance range from strict formal contracts to less formal agreements, why we consider the findings to be applicable for all forms of competitor partnerships.

We also let the equity component of a partnership be of any form, either the firms are totally independent or equity-related. Here, the organisations can have stakes in each other or be subsidiaries of the same parent company, but the formation of a joint venture or the acquisition of one another are excluded from analysis as we are only examining the cooperation between existing firms with limited control. Reuer and Ari˜no (2007) found that con- tractual complexity of alliances has a large variation that is not captured by the equity/non-equity dichotomy and hence we do not distinguish between di↵erent equity set-ups of cooperation. An equally important factor for the governance structure of a cooperation is the existence of previous cooperative relationships (Gulati, 1995). A competitor could be any rival with similar products in the same industry. Even though our analysis is conducted in one industry, we consider competitors that are o↵ering similar products on other geographical markets as potential partners.

1.7.2 Partnership Performance

Partnership performance can be difficult to measure, due to complexity and incomparable goals of the partners. According to Parkhe (1993), one of the most common ways to measure alliance performance is by partner satisfaction, but more recent studies tend to focus on financial measures, like profitability, revenue growth or return on investments (Jennings et al., 2000;

Luo, 2002). Since measuring alliance performance is such a complex task, new measures have been introduced. One such is by Pansiri (2008), who proposed a framework with three measures of alliance performance, including: total alliance performance, operational performance and market share profitability.

We use the definition by Parkhe (1993), meaning that we assess partnership performance by partner satisfaction. Even though it involves subjectivity it is a reasonable measure for our field of analysis, where financial performance

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might not be representative for the outcome of the partnership. Thus, in a successful competitor partnership both partners need to be satisfied.

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Chapter 2 Literature Review

In this chapter, the theoretical references for our study are described. Resource- based theory is presented as an approach to view and analyse alliances, factors for alliance success are outlined and the contradictory relationship of co-opetition is discussed. The chapter ends with a review of sales and service networks.

2.1 Alliance Perspectives

When explaining alliance success, di↵erent models can be grouped into four main perspectives; resource-based view, competence-based view, relational factors view and competitive advantage view (Hunt et al., 2002). Of these, the resource-based view o↵ers the best explanation for alliance success, according to Ireland et al. (2002). This view of a company emerged during the 1980’s as an alternative approach to the classical transactional view that emphasises external environment and assumes firm homogeneity (Das and Teng, 2000). In Resource-based Theory (RBT), on the contrary, a firm’s profit potential is determined by its internal factors and specific resources, meaning that RBT assumes resource heterogeneity (Kozlenkova et al., 2014).

There are numerous definitions of resources, but one commonly used is “those (tangible and intangible) assets which are tied semi-permanently to the firm”

by Wernerfelt (1984). Another, by Tsang (1998), reads “a firm’s resources consist of all its assets, knowledge, organisational structure, procedures and so forth that are controlled by the firm”. Furthermore, resources are often categorised as either property-based or knowledge-based.

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A firm achieves competitive advantage when it generates more economic value than a break-even competitor in the product market (Peteraf and Barney, 2003) and sustained competitive advantage is when this advantage cannot be duplicated by competitors (Barney and Clark, 2007). In RBT, there exist two central assumptions of how firms obtain sustained competitive advantage (SCA); that each company has a di↵erent set of resources even within the same industry, and that resources are difficult to trade (Kozlenkova et al., 2014). These two assumptions, resource heterogeneity and resource immobility, can be further extended into three assumed characteristics of resources (Das and Teng, 2000):

1. Imperfect mobility

• The difficulty and the costs related to moving resources from one firm to another

• Examples: Human resources, organisational resources, corporate culture

2. Imperfect imitability

• The difficulty in imitating a resource

• Examples: Patents, contracts, copyrights, trademarks, registered designs, technological resources, managerial resources

3. Imperfect substitutability

• The difficulty in finding a substitute for a particular resource

• Examples: Physical resources, technological resources, managerial resources

Resources with these characteristics imply barriers for companies trying to acquire, imitate or substitute such resources, which consequently provides opportunities for companies to form alliances in order to gain access to otherwise unavailable resources (Barney, 1991). When resources are mobile, imitable or substitutable, on the other hand, firms will not seek a strategic alliance, since they can easier obtain the resources themselves through trans- actions, without the costs associated with alliances (Chi, 1994). Thus, RBT can be applied to explain strategic alliances, their rationale and success. In the traditional transaction cost rationale, the ultimate goal in all strategic alliances and other forms of cooperation has been to minimise production and transaction costs (Coase, 1937). In RBT, instead, the goal is to max- imise value by gaining access to other firms’ resources (Barney and Clark, 2007).

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Partner firm (B)

—————————————————————–

Firm (A) Property-Based Resources Knowledge-Based Re- sources

Property-Based Resources

Unilateral Contract-Based Alliances

Equity Joint Ventures

Knowledge- Based Resources

Minority Equity Alliances Bilateral Contract- Based Alliances

Table 2.1: Resource types and firms’ structural preferences (Das and Teng, 2000)

The most appropriate alliance structure depends on the interdependence between the resources of the two firms. If a firm’s required resources can be separated from redundant resources, an acquisition of this company is preferred, but if not, a joint venture (JV) or other forms of cooperation is preferred, as a JV would only comprise the relevant resources from the two firms (Hennart and Reddy, 1997). If resources can be efficiently obtained through transac- tions of any kind, this instead is the most appropriate method (Chi, 1994).

The structural preferences for a firm entering a partnership is explained in table 2.1. Depending on the combination of resources that firm A and its partner firm B holds, di↵erent structures will be preferred by firm A (Das and Teng, 2000).

Firms are aiming at finding the best resource combination for their and the partner firms’ resources and hence cooperation among firms is essentially resource integration that is beneficial for all participants. As resources are not a zero-sum game, new combinations provide value not realised by the resources independently. Depending on the type of resources that a firm owns or wants to achieve, di↵erent alliance structures are preferred, as explained in table 2.1. (Das and Teng, 2000)

Two critical factors to look at when evaluating alliances are collective strengths, defined as the amount of relevant resources, and inter-firm conflicts (Das and Teng, 2000). According to RBT, the goal of an alliance is to gain access to more resources than currently held, and the amount of total resources in the alliance is the collective strength (Das and Teng, 2000). Alliance success can be directly linked to the level of collective strength (Beamish, 1987), while reasons for alliance failure are closely linked to inter-firm conflicts, like

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Resource Utilization

—————————————————————–

Resource Similarity Performing Resources Nonperforming Re- sources

Similar Resources Supplementary Surplus Dissimilar Resources Complementary Wasteful

Table 2.2: A typology of inter-partner resource alignments (Das and Teng 2000)

competing interests, incompatible goals, opportunistic behaviour or disagree- ments regarding resource allocation (Hardy and Phillips, 1998).

Alliance success is also determined by the alignment of resources of the participating firms, as it implies either collective strength or inter-firm conflicts.

In the most extensive definition, resource alignment comprises four di↵erent patterns of resource integration: complementary, supplementary, surplus and waste (Das and Teng, 2000). To explain each pattern one needs to understand resource similarity and resource utilisation, and performing and nonperforming resources. Resource similarity is the degree to which resources from each participant is comparable in terms of type and amount (Chen, 1996) while resource utilisation is the degree to which the resources are utilised to meet the goals of the alliance. Performing resources are used to fulfil the goals of the alliance, and nonperforming are idle resources. In table 2.2, each pattern is characterised according to similarity and utilisation. (Das and Teng, 2000)

In prior research, all patterns except the wasteful one has been shown to contribute positively to alliance performance. Supplemental resources create value by being of the performing kind, thus being used to pursue a value- driven strategy (Das and Teng, 2000). In a complementary setting, the resources contributed are unique and non-redundant, resulting in a larger resource base for the alliance (Deeds and Hill, 1996). The surplus pattern contributes to alliance success by lowering the amount of conflicts. When the resources are not constrained, the fight over the allocation is less fierce (Singh, 1987). As mentioned, the only resource pattern that contributes negatively to alliance success is the wasteful pattern, as this implies the two firms are not compatible, and leads to inter-firm conflicts (Park and Ungson, 1997).

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In sum, according to RBT, a firm’s success is based on the resources it holds, because resource heterogeneity and immobility prevents the market from obtaining a duplicate of a firm’s resources. The same holds for alliances, where the success of an alliance can be determined by the resources it holds.

The amount of collective strength that the pooled resources provide is directly linked to how well an alliance performs. Furthermore, inter-firm conflicts provide negative contribution to an alliance. When selecting a partner and structure of an alliance, the resource alignment is critical as it contributes to either positive or negative results by either enhancing collective strength or inter-firm conflicts. All types of alignment patterns can contribute to positive alliance performance, except the so called wasteful pattern, characterised by dissimilar, nonperforming resources.

2.2 Alliance Management

Companies form strategic alliances for a variety of reasons, examples ranging from shrinking product life cycles to increasing competitiveness through organisational learning (Spekman et al., 1998). In essence, a strategic alliance is a cooperative agreement between two or more firms to improve their competitive position and performance by sharing resources (Ireland et al., 2002).

A definition o↵ered by Hunt et al. (2002) is “collaborative e↵orts between two or more firms that pool their resources in an e↵ort to achieve mutually compatible goals that they could not achieve easily alone”.

Despite the rapidly increasing number of formed alliances, many of them fail to achieve their targets. Bamford and Gomes-Casseres (2003) estimated that 30 to 70% of all strategic alliances fail but meanwhile, alliances account for 6 to 15% of median company value (Hunt et al., 2002). Kaplan et al (2010) found that only half of all strategic alliances manage to yield returns higher than the cost of capital, which indicates that a large number of alliances fail.

2.2.1 Success Factors

Research on alliance success has identified a vast number of factors that contribute to an alliance success or failure. Whipple and Frankel (2000), after investigating 41 firms, found five factors among a set of 18 that were most vital to strategic alliance success, comprising:

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1. Trust

2. Senior management support

3. Ability to meet performance expectation 4. Clear goals

5. Partner compatibility

Trust involves both character-based and competence-based trust, i.e. trust in characteristics such as high moral, integrity and honesty and in compe- tency and solutions-orientation, respectively (Gabarro 1987). Lack of trust is one of the most common causes of failed alliances, as partners that do not trust each other do not work well together. Trust can only be built between individuals, so alliances need to be formed in a manner that enhances trust among individual persons in the alliance (Elmuti and Kathawala 2001).

Senior management support is crucial to provide focus and resources, on both a strategic and operational level (Whipple and Frankel, 2000). 50%

of alliance failures are due to poor management and hence having a clear structure regarding the objectives and management of the alliance is vital.

Lack of coordination among management teams is another factor that leads to alliance failure. (Elmuti and Kathawala, 2001)

Since a main rationale behind the formation of a strategic alliance is to improve both partners’ competitive advantage, the success of the alliance hinges on the ability to meet the agreed performance expectations. When firms in an alliance operate at di↵erent performance levels this can lead to conflicts and mistrust, further undermining the alliance. (Whipple and Frankel, 2000)

For a successful alliance, the goals need to be agreed upon, written down and regularly reviewed. Without clear goals, the performance cannot be measured and improved. When analysing the partner compatibility, the partners’ will- ingness to work as a team was found to improve the alliance, rather than operational philosophy or management styles (Whipple and Frankel 2000).

Thus, to be compatible, it is more important to be able to cooperate than to have similar structure in the participating firms.

Technology Associates and Alliances (1999) conducted a survey among 455 CEO:s involved in strategic alliances, and let them rank the most important factors for success. The five most important factors turned out to be:

1. Partner selection

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2. Senior management commitment 3. Clearly understood roles

4. Communication between partners 5. Clearly defined objectives

When comparing the result of this survey with the success factors of Whip- ple and Frankel (2000), many factors are overlapping and the two studies confirm each other. Senior management commitment overlaps with senior management support, partner selection partly covers partner compatibility and clearly defined objectives is another wording of clear goals.

The results of Whipple and Frankel (2000) also covers trust and ability to meet performance expectation while the results from the survey are more concentrated to the organisation of the alliance with clearly understood roles and communication between partners as central factors.

Elmuti and Kathawala (2001) have also investigated success factors of strategic alliances, where partner selection is claimed to be the most important factor for alliance success. Also, it is important that the strategic reasons for forming an alliance are clear. In some cases, an alliance is not the best solution and instead an acquisition or a merger would be a better option.

Other research underlines the importance of partner similarity for successful strategic alliances. Here, similarity refers to culture and strategy similarity (Rothaermel and Boeker, 2008; Shah and Swaminathan, 2008). Gao et al.

(2017), found in a study of the logistics industry in China, that similarity in competence and culture, but di↵erence in geographic cover, most positively leads to strategic alliance success. The importance of culture similarity confirms earlier studies, which show positive correlation between culture similarity and alliance success (Lin and Germain 1998).

Partner compatibility enhances knowledge transfer and operations among participating firms and facilitates coordination and execution of alliance activities and strategies (Elvi, 2014; Pansiri, 2008). Compatibility demonstra- tively correlates with shared norms and values as well as a higher level of trust, especially for compatible cultures (Schreiner et al., 2009). Due to this, compatible cultures are associated with less opportunistic behaviours in the alliance, leading to better alliance stability (Tubin and Rozalis, 2008). Raue and Wallenburg (2013) have shown that alliance stability can lead to alliance compatibility, meaning similarity and compatibility are two intertwined concepts in the sphere of strategic alliances.

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Overall, similar companies, especially with similar cultures, is an important factor in alliance success. The similarity of culture, and more importantly the underlying compatibility that it often implies, is seen as a driver for other identified success factors, such as trust and partner compatibility. (Raue and Wallenburg, 2013)

The main reason for culture similarity being a crucial factor in alliance constellations is that firms with similar culture have fewer conflicts. Less conflicts lead to improved alliance stability which has been shown to be positively tied to alliance performance (Raue and Wallenburg, 2013). Jiang et al (2008) lists alliance stability as a determinant of alliance performance, and it is closely tied to both short- and long-term success. Relational stability is tied to goal- interdependence and positive social exchange (Yang et al. 2008), as well as providing opportunities for sharing knowledge and innovation (Von Krogh et al. 2001).

One of the most common reasons for failure in strategic alliances is partner incompatibility and clash of cultures (Brekalo and Albers, 2016; Elmuti and Kathawala, 2001). Clash of cultures can occur due to of di↵erences in lan- guage and operations. For example, US companies tend to focus more on profits whereas Japanese companies concentrate on improving their strategic position, which can cause conflicts if they work together (Daniels and Radebaugh 2001).

When discussing corporate culture, it is common to divide the discussion into national and organisational culture (Lok and Crawford, 2004). Some earlier researchers, like Lane and Beamish (1990) has pointed to national culture as a main source of conflicts in alliances, as they can disrupt cooperation and learning. Newer research by Simon and Lane (2004) point to organisational culture being more important than national culture, while also claiming that the closer a social group is to value-adding activities, the more disruptive the culture di↵erences will be. The claim that organisational culture is more important than national culture is supported by findings by Vara et al. (2012).

Culture, and therefore also cultural fit, is a word that has numerous conno- tations and it has been argued that the word itself is conceptually difficult to define. It has been defined in several di↵erent ways but there is no consensus among researchers. One common definition is ”the set of important assumptions (often unstated) that members of a community share in common” (Weber and Menipaz, 2003). Most existing definitions describe culture in terms of the values, beliefs and behaviours that form an organisation’s core identity (Rashid et al. 2003). Van der Post et al. (1998) described culture

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as a hidden but unifying force that provides meaning and direction. Still, despite disagreement over its definition, researchers agree on the importance of culture (O’Reilly III et al. 1991).

Zeng and Schoenecker (2015) found that strategic similarity is important in the strategic part of alliances, like products, technology, markets and customers. Strategic similarity in this context is defined as the degree to which the partner firm’s strategy enhances your own strategy, i.e. a strategic fit (Jemison and Sitkin 1986).

2.3 Co-opetition

Competition is central to classical economic theory. Adam Smith (1776) used competition to explain di↵erences in prices, more competition lead to lower prices and vice versa. He had five criteria for perfect competition: rivals act independently, the number of rivals is sufficient to eliminate extraordinary gains, the customers must possess tolerable knowledge of the market, they must have freedom to act on this knowledge and sufficient time must be allowed for resources to flow in the direction wished by its owners. (Smith, 1776)

A more recent definition of perfect competition is a market absent of monopoly powers, and in extension a market without barriers to entry or exit (Stigler, 1957). Bork (1993) has given a number of factors for perfect competition, including no entry or exit barriers, profit maximisation of sellers, and a large number of buyers and sellers. Furthermore, in perfect competition there is non-increasing returns to scale, perfect information, and every participant is a price taker, meaning lacking the power to influence power (Bork, 1993). In reality, no market will have perfect competition. Cooperation is the opposite of competition. Instead of working independently, firms work together to help each other. One definition by Doran (1995), is a situation where two firms either work together towards the same goal, or work together to achieve each other’s di↵erent goals.

The term co-opetition refers to the phenomenon of two rival firms simultaneously competing and cooperating, that is, a complex and contradictory relationship, which by Bengtsson and Kock (2000) is argued to be one of the most advantageous connections if the two parts are managed separately. Co- opetition involves a fundamental duality, with value creation as an inherently cooperative process and value capturing as an ultimately competitive process

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(Luo, 2007). The complexity of co-opetitive relationships occurs from these contradictory interests that are both conflicting and common, implying both hostility and friendliness at the same time.

Nalebu↵ and Brandenburger (1996) have also observed simultaneous competition and cooperation, i.e. co-opetition, but using a wider definition of competitors as any player whose product makes your product less worth.

With the use of a game theory framework, they developed strategy guidelines from analyses of every relationship with customers, suppliers, competitors and complementors, meaning any player whose product makes your product worth more, and from this, strategic positions and alliances were outlined.

The best solution to such business games often involves co-opetition and dif- fers from the Nash equilibrium¹as other players’ decision are considered, with value creation and value appropriation as central factors (Brandenburger and Nalebu↵, 1996). Another beneficial solution could be a change of the whole playing field.

According to Luo (2007), there are several areas in terms of function, product or geography that inspire cooperation between rivals. Functional areas include primary and supporting value chain activities such as outsourcing, co-production, co-marketing and R&D, product areas comprise new products not tested by the market, complementary products and learning opportunities and geographical areas involve volatile markets, high entry-barriers and markets superior in location-specific resources.

Research about co-opetition is a fairly new field, most older related research has been focused on either competition or cooperation instead of the simultaneous process. It is a frequently occurring situation in strategic groups or alliances, why further research is anticipated. The linkage between competitors has been discussed in older research as a consequence of relationships with the same buyer, but not as a factor of success as both Granovetter (1985) and Bengtsson and Kock (2000) suggest. Most research conducted in the field is concentrated to the general dynamics of co-opetitive relationships by investigating whole firms and not separate business units.

According to Luo (2007), co-opetition is e↵ectively practised if corporate culture and managerial cognition embrace a simultaneous consideration of both cooperation and competition, that is, a yin and yang philosophy. Global co- opetitive players should aim for a co-existence of strategic goal convergence

1Nash equilibrium is the steady-state solution to a game, involving two or more players, where each player is making the best possible decision, assuming all other players’ decisions remain unchanged (Nash, 1950).

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and competitive goal divergence, meaning that the players’ long-term visions and direction of the whole organisation should be aligned while specific objectives and short-term target goals for particular sub units or sub markets should be separated (Luo, 2007). Furthermore, the definition of boundaries for cooperation and competition that allows for strategic balance of these actions is crucial and a distinct separation is required (Luo, 2007; Bengts- son and Kock, 2000). Bengtsson and Kock (2000) found that the divide between competition and cooperation could depend on either the proximity to the customers or the access to specific resources. This is based on the fact that employees can only act in accordance with one logic of interaction at a time. Bengtsson and Kock (2000) also showed from empirical studies that the division was either related to the value chain, that is, functionality or activity based, or to the magnitude of business units, i.e. based on product areas. They identified companies cooperating within R&D and logistics but also companies cooperating within sales and marketing of complementary products.

Most research on co-opetition is focused on cooperation among input activities, i.e. product development, purchasing, manufacturing etc., but there are a few investigations of co-opetition in output activities as well, i.e. in sales, marketing and service activities. Their results show that benefits of simultaneous competition and cooperation could also be attained in areas closer to the customer. Lindstr¨om and Polsa (2016) identified five factors for successful cooperation in output activities in their case study of small business networks; activeness, commitment, strategic fit, geographical distance and personnel resources (Lindstr¨om and Polsa, 2016). The importance of strategic fit and commitment was also found for general strategic alliances (Rothaermel and Boeker, 2008; Shah and Swaminathan, 2008) and the geographical distance factor confirms the study of Gao et al. (2017) who found that di↵erence in geographical cover could lead to alliance success. Lack of commitment has also been found to be one of the most common reasons behind alliance failures (Elmuti and Kathawala, 2001), further showing its importance for alliance outcomes.

Another phenomenon related to co-opetition is co-marketing, meaning con- tractual relationships between firms at the same level in the value-added chain with complementary products and whose individual successes are interdependent (Bucklin and Sengupta, 1993). Through the linkage of multiple firms’ resources, it has been shown that new market positions or systems can be achieved and potential innovations unlocked, but managerial implications can be challenging, as competition is frequently present at this stage in the value chain. Bucklin and Sengupta (1993) found in a study of co-marketing

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alliances in the computer and semi-conductor industries that imbalances in power and in the individual managerial resources are significant drawbacks of a co-marketing alliance and it can limit alliance success. On the contrary, successful alliances are likely when projects are well-organised, have high pay-outs in relation to costs and have high partner compatibility in terms of culture, objectives and operations. The finding of partner compatibility is in line with the success factors defined for more general strategic alliances while the factors organisation and high pay-outs have not been covered earlier. Buckling and Sengupta (1993) also found alliances to be more successful in turbulent environments due to access to new complementary products or technologies without the risks of internal development.

2.4 Sales and Service Networks

Industrial companies have recently been moving towards solution providers instead of only product providers, where customer demand, competitive pres- sure and the pursuit of more stable revenues have worked as catalysts (Ko- htam¨aki et al., 2013). Disney et al. (1999) have found that in the automotive industry, the number of dealer sites have decreased but service presence has remained, indicating an identified need for services from automotive companies.

Having an elaborate sales and service network is most crucial in terms of being able to o↵er after-sales services, since these often make up the majority of profits in product companies (Gaiardelli et al., 2007). In the automotive industry, for instance, 45 % of gross profit stems from after-market despite only accounting for 24 % of revenues (Cohen et al., 2006). Services also act as a main di↵erentiator for product companies (Wise and Baumgartner, 1999). Gallagher et al. (2005) have shown that services o↵er a constant connection with customers and has influence on customer satisfaction and loyalty. O↵ering services is also positively tied to sales performance, making service capabilities an important component in the total o↵ering of product companies (Kohtam¨aki et al., 2013). Additional benefits of o↵ering after- market services is an extended time period for earning revenue and greater knowledge about customers (Cohen et al., 2006)

The supply chain has a central role in ensuring an efficient network, as the number of items in stock can be 20 times as high for after-market services than for the manufacturing of products (Cohen et al., 2006). Johnson and Mena (2008) investigated which processes were most vital in the supply chain

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of service products. The key di↵erence between product and service supply chains is that the service one needs to be more responsive, as demand is harder to predict. What Johnson and Mena found was that processes that facilitate responsiveness improve the network capabilities and they highlight information flow processes as a key process.

Johnstone et al. (2008) points out that the process of developing an after- market network is highly complex, and current literature tend to oversimplify this reality, which is in a state of perpetual change. They further acclaim there are several paths to take in the forming of networks, with no consensus of a best practice for how to approach services for product companies.

Cohen et al. (2006) have developed a framework to design a sales and service network. There are two hierarchies to have in mind when decided on the network; product hierarchy and geographical hierarchy. Product hierarchy refers to the service level to which the company will commit. A high product hierarchy refers to the end product, meaning being able to o↵er a spare product if necessary, while a low hierarchy refers to piece parts. Geographical hierarchy refers to how close to the customer the after-sales locations are. A high geographical hierarchy means being present on customer sites, while low geographical hierarchy refers to the use of central facilitates. Being high in the product hierarchy is tied to higher prices, while being high in the geographical hierarchy is tied to faster response time. The preferences of a company will determine how they design their network. To have success in the after-market, Gaiardelli et al. (2007) have determined that companies need to introduce KPIs in their network and measure performance. (Cohen et al., 2006)

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Chapter 3 Methodology

This chapter describes the research methodology, including its strategy and process. Each method for data collection and data analysis is further explained. The chapter ends with a reflection on the reliability and validity of the research design.

3.1 Research Strategy

In order to analyse how to form successful competitor partnerships within sales and service networks in the heavy truck industry, a multiple-case study approach was chosen as research strategy. As the main research question is of explanatory nature with its “how”-formulation, a case study approach is suitable to focus on dynamics present in specific settings (Yin, 2003; Eisen- hardt, 1989). With the aim of contributing to research in an unexplored field, the choice of an iterative method of case studies is motivated by its likelihood of generating new theory (Eisenhardt, 1989). Furthermore, a multiple-case design was chosen over a single-case design as the overall study is considered more robust and its evidence more compelling (Yin, 1994). This choice of five cases represents a well-balanced use of available resources.

Four out of five case studies were from the truck industry, of which three cases were former attempts to share sales and service functions while one was still in progress. The fifth case was an ongoing example from the agricultural machinery industry, that was identified to have similar characteristics as the heavy truck industry. With the aim of generating theory based on case study evidence, the cases were identified as applicable examples in real-world set-

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tings, with diversified perspectives on the proposed problem. As two out of five identified cases are from other companies than Scania, and one case is from another, but similar, industry, they form a broad base with several perspectives that fit well to the research question. Thus, research was conducted on the same issue, but in a variety of contexts, which reduces biases to specific contexts. The combination of retrospective and real-time cases is according to Leonard-Barton (1990) a useful approach to mitigate bias.

The data collection of real-time cases enhances the internal validity of our research as cause and e↵ect relationships can be further investigated, while the use of retrospective cases instead increased external validity and enabled identification of cross-case patterns (Leonard-Barton, 1990). However, for retrospective cases, the memory of important events can di↵er among interviewees and the exact chronological order can be mixed up.

The multiple case study approach o↵ers the opportunity to conduct multiple levels of analysis within each study (Yin, 1984), which is of interest here as the di↵erent cases represent cooperation at di↵erent levels, e.g. shared networks, shared distributor or consolidated sales functions. An alternative research design could have been chosen with both fewer and more cases for the same research questions. Fewer to get even more detailed results as those cases could be deeper analysed, more to obtain di↵erent set-ups and solutions and to achieve a higher level of generalisability. However, the cases in these types of settings all have specific characteristics not present in other cases and examining fewer cases could result in dependence on specific settings of the particular case. More cases with the desired characteristics was hard to find due to the nature of the heavy truck industry with a limited set of large players. Hence, adding cases that are further away from the main research question would a↵ect the applicability of our findings negatively. By investigating a few cases from the truck industry and comparing them with an industry of similar characteristics, the findings are general enough to be applicable in new cases, while not being too general.

To use surveys is another research method that could have been used to quantify our research approach. It could have been useful to strengthen our qualitative results on important success factors with statistical evidence, but it also presumes a larger set of data. Adding more situations to the data sample would probably make the findings less applicable to the specific conditions we are investigating. The research questions eliminate many cases that are not applicable in this case, and including them in a survey would decrease the validity of the findings. In contrast to earlier research using surveys that tend to be more of big picture analyses, the research we intend to perform is subject to particular settings, with a limited number of relevant

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examples. Furthermore, qualitative research seems more appropriate to our explanatory research question as directly causal relationships are hard to determine when many interdependent factors are present.

This master thesis was conducted during a total time of twenty weeks, with ten introductory days during November and December 2017 and 18 coherent full-time weeks from January to May 2018. In brief, the process consisted of problem formulation, literature review, empirical data gathering through a pre-study and a multiple case studies with interviews and public material, analysis and conclusions. The di↵erent steps are visualised below.

Figure 3.1: Illustration of research process

3.2 Data Collection

The research approach was an iterative process where problem formulation, research questions, literature review and analysis were continuously revised.

Also, the literature review, the empirical data collection and the data analysis were frequently overlapping, to iterate the process and let di↵erent data sources infer with each other. This implied a flexible data collection, allowing for adjustments during the process, that is, a controlled opportunism to take advantage of specific cases in order to reach an in-depth understanding and generate new theory (Eisenhardt, 1989). For example, the chosen set of case studies was revised to match the key considerations found in previous research.

3.2.1 Pre-study

To formulate the problem and orient ourselves in the industry we first conducted a pres-study through a series of semi-structured interviews. The

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interviewees were all employees at Scania or Volkswagen Truck & Bus, with di↵erent experiences relevant to our research. All pre-study interviews were made in person and lasted approximately 45-60 minutes. In appendix B all pre-study interviews are listed and further details presented. From these interviews, a wider understanding of the heavy truck industry and sales process was obtained to refine the problem formulation. Also, prior examples of strategic alliances in sales and service networks were discussed as potential case studies. The interviewees were chosen together with our supervisors at Scania, based on the respondents’ experience and position, to obtain dif- ferent perspectives and expertise. This sample selection strategy is subject to some bias in terms of non-random sampling as all pre-study interviewees were recommended by our Scania supervisors, but with the purpose of understanding the heavy truck industry and its business, and to formulate the research problem, this is not considered a concern here.

Simultaneously, a pre-study of the literature field was conducted to identify the status quo of prior research and potential theories to rely our analysis on. Also, internal and public documents were reviewed to add to our understanding of the business and the industry. From this we could position our research and apply it to the truck industry. The research question was discussed with our Scania supervisors to both cover the practical issues at Scania, but also to contribute to research in an untapped field and from a new perspective. The problem and its research question was formulated as it was perceived at the time, but throughout the process the problem was revised and narrowed down, as new insights and information were obtained.

Even in the end of the thesis process the problem statement was revised to be consistent with the analysis and the conclusions.

3.2.2 Literature Review

A literature review was conducted, including both relevant theory and earlier research on the topic of competition and cooperation among companies.

Secondary data was collected from books and articles and for literature references, KTHB Primo (search engine from the Royal Institute of Technology) and Google Scholar was used. Even though these data bases mostly comprise reliable journals from published authors, journals were chosen with respect to number of citations, authors’ other works and publication to avoid unreli- able works. The literature review was systematically accomplished, starting broad to screen the research field and then narrowing down to our specific context of simultaneous competition and cooperation. Resource-based theory

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(RBT), alliance management and co-opetition were identified as appropriate research fields and studied in more detail. Theoretical models were complemented by prior, similar research examples, to refer and compare both findings and research strategy with.

During the pre-study interviews and the literature study, the initial structure for the case analyses was created. Findings from the literature were continuously incorporated into the semi-structured interview questions and iterated with our Scania supervisors. The interview structure was based on key factors in prior research of alliances, modified to fit our research questions.

3.2.3 Case Studies

The empirical data gathering for the thesis was conducted through five case studies. The cases were selected based on the pre-study interviews, where attempts to share sales and service networks were discussed. Examples that were recurrent in interviews and that fitted well to our research question were chosen, resulting in four cases from the truck industry. To provide insights from another industry an additional case was added. This strengthened our research as comparison was made outside the industry of interest and more general conclusions could be achieved. The agricultural equipment industry was identified to share many characteristics with the truck industry and the case where two competing brands having the same dealer in Sweden was selected. As appropriate examples of shared sales and service networks among competitors were rare, the case selection process was not chosen to be systematic and unbiased, but rather exploratory and open-minded.

All cases involved some kind of competitor partnership within sales and services, but operated under di↵erent circumstances. Also, the cases represent di↵erent forms of partnerships in terms of ownership structures. Initially, the cases were discussed during the pre-study interviews to understand the general settings of the shared sales and service networks. Based on both literature and the pre-study interviews, our analytical framework was formed to test models and parameters on the cases in-depth. This framework was the result of the literature review and pre-study and was used as a tool for the case studies. When available, public materials such as press releases or news articles were also used to compare with interview responses. Triangu- lation was obtained through multiple means of data collection such as prior research, interviews, observations and public documents.

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Application #interviews #public material

Pre-study 12 5

Scania - Hino 6 9

Scania - Volkswagen 5 0

Volvo - Renault 3 6

Valtra - Fendt 4 5

Total 30 23

After selecting the particular cases, persons involved were selected for interviews. Interviewees were chosen from recommendations, initially from our Scania supervisors, but also from the interviewees if they knew other persons involved that we could interview. This sample selection strategy, also called snowball- or chain referral sampling, is subject to bias and non-randomness.

According to Biernacki and Waldorf (1981) such data has a lower degree of generalisability as the selected sample is dependent on social networks, which could impact the general validity of our results. Interview persons were selected from di↵erent business areas and with di↵erent responsibilities in the partnership though, to obtain diverse perspectives which according to Eisenhardt and Graebner (2007) limits bias.

All case study interviews were 30-60 minutes and all except two interviews were held with both interviewers present. This allowed for taking extensive notes but also reduced misinterpretations. When possible, the interviews were recorded. Furthermore, the interviews were held face-to-face if possible and otherwise by phone or Skype. A detailed table of all interviews is found in appendix B. The interviews were of semi-structured format, with predefined questions and discussion topics but open-ended to minimise bias.

An interview guide is found in appendix A. The interview questions were based on the analytical framework, which were continuously revised as new findings were discovered. When available, the interviews were complemented by public material to include an outside view of the situation.

3.3 Data Analysis

The empirical data obtained during interviews was reduced and structured in a table for each case, so that cross-case patterns could be identified. The cate- gorisation of potentially important factors was based on the analytical framework obtained from both the theoretical framework and industry-specific

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findings from interviews. In order to find patterns that lead to successful competitor partnerships, the cases defined as successful were compared to the ones defined as unsuccessful, for each potential factor. The empirical data was also structured in terms of timing aspects of a partnership, i.e.

factors were either categorised to partner assessment or network design. To structure the data in a variety of patterns is a useful technique to find order in chaos (Stuart et al., 2002).

By reducing the data into tables of the same structure, the cases became easier to compare. However, the simplification of each case that was required can be criticised according to Stuart et al. (2002) as it implies a difficulty to convince the reader that each item in a table represents the raw data.

The goal of the data analysis was to develop an assessment tool for forming successful competitor partnerships, based on a limited set of decisive factors and their interdependence. Here, one should keep in mind the complexity and subjectivity involved in partnerships’ success or failure, which is a challenge to capture in a simplification of patterns found in the empirical data.

3.4 Validity and Reliability

The reliability of research is defined by Collis and Hussey (2014) as the absence of di↵erences in the results if the research was repeated. High reliability means that if the study is repeated, the findings should not deviate from the original study too much. Thus, both a precise process description and absence of random errors are desirable (Gibbert et al. 2008).

The reliability of our thesis is relatively low even though the process is well documented and chronologically structured, random errors are not eliminated due to the semi-structured interviews and exploratory case selection process.

If the research was repeated it is likely that other cases could have been found and other dynamics and success factors explored. Also, the nature of qualitative data and its interpretation of data inevitably implies some bias.

To remedy this, triangulation of the data collection was used to enhance the reliability of the study and lower the risk of biased sources (Eisenhardt, 1989), so that our findings and conclusions were inferred from literature, semi-structured interviews and public documents.

Validity can be separated into three di↵erent types: external, internal and construct, where external validity is also referred to as generalisability, i.e.

how well the findings can be repeated in other settings (Gibbert. et al 2008).

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By conducting several case studies from the truck industry and one from a similar but di↵erent industry, the generalisability, is increased, and therefore also the validity (Eisenhardt 1989). So, by conducting a multiple case study, analytical generalisation, as defined by Yin (1994), is obtained from replication and the results are therefore less dependent on specific settings.

We also conducted two case studies at the same company, a so called nested approach. This is also argued to increase generalisability (Yin 1994).

Internal validity refers to the researchers’ argumentation for their findings.

High internal validity is obtained by logical reasoning and through demon- stration of work (Gibbert et al. 2008). Most of our reasoning is based on existing literature but influenced by cases and adapted to the specific settings of our research question. On the contrary, the analyses of the case studies may be biased and less logically coherent.

Construct validity is the degree to which the research is studying the things it is stated to investigate. A high construct validity is obtained by picking the right literature and asking the right questions (Gibbert et al., 2008).

We achieved a high construct validity by constantly reviewing the chosen literature to make sure it was still relevant, and to update our interview questions and framework continuously as we came over new findings.

3.5 Research Ethics

This study is carried out in conjunction with Scania CV AB. Confidential- ity and anonymity have therefore been respected throughout the research process. Sensitive information has been carefully handled and either excluded from the report or modified to represent the same dynamics. Any uncertainties regarding confidentiality of the information obtained have been discussed with our Scania supervisors to make sure no sensitive information is published. Also, Scania’s general rules of conduct have been followed and a non-disclosure agreement was signed in the beginning of the project.

All interviewees were informed of the purpose of the study, and anonymity was maintained. Following this, interview notes are excluded from the appendix. Even for secondary data gathering, research ethics was respected and sources criticised. The general guidelines from KTH of research ethics have also been followed with no tolerance of plagiarism.

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Chapter 4 The Heavy Truck Industry

This chapter presents an overview of the heavy truck industry and its characteristics. It starts with a description of the market, including its development, customer segments and the top three global groups. Thereafter, the sales process is explained. The chapter ends with a description of potential factors for successful partnerships in sales and service networks. The chapter is based on empirical findings from interviews and public documents from our pre-study.

4.1 Industry Description

The heavy truck industry comprises trucks weighing more than 16 tons.

The applications range from long-haulage transportation to other demand- ing functions like mining and fire-trucks. This di↵ers from light and medium trucks, which are mainly used for distribution. The main customer segment for heavy trucks remains long-haulage customers, e.g. logistics providers.

Since entry barriers for carriers are quite low, fierce competition is present.

There are many small haulage contractors in today’s market, with only one or a few truckers, but following the increasing concentration in the freight forwarding and logistics industry, the trend goes towards larger fleets (Shiller et al. 2016).

Due to constant competition among logistics providers, margins are low and high productivity is required. This implies a demand on reliable trucks in terms of availability, as a missed delivery reduces the productivity and can lead to further lost contracts. When calculating operational costs, the aim is

Sharing Sales and Service Networks with Competitors: A Multiple-case Study in the Heavy Truck Industry

Sharing Sales and Service Networks with Competitors

A Multiple-case Study in the Heavy Truck Industry

ALEXANDER ENGBLOM EMMA LUNDQUIST

Sharing Sales and Service Networks with Competitors

by

Alexander Engblom Emma Lundquist

Master of Science Thesis TRITA-ITM-EX 2018:185 KTH Industrial Engineering and Management

Industrial Management

SE-100 44 STOCKHOLM

Att dela sälj- och servicenätverk med konkurrenter

av

Alexander Engblom Emma Lundquist

Examensarbete TRITA-ITM-EX 2018:185 KTH Industriell teknik och management

Industriell ekonomi och organisation

SE-100 44 STOCKHOLM

Alexander Engblom Emma Lundquist

2018-06-11

Lars Uppvall

Bo Karlsson

Scania

Jens Tullberg Oscar Wyckman

In the heavy truck industry, sales and service networks play an important role.

salespersons, high commitment, and patient implementation. This thesis contributes to science by research on co-opetition research in output activities, and by a discussion on the meaning of competition, success and partnerships.

Alexander Engblom Emma Lundquist

2018-06-11

Lars Uppvall

Bo Karlsson

Scania

Jens Tullberg Oscar Wyckman

konkurrens, så kallad co-opetition, är ett komplext och motsägande fenomen, som tidigare forskats på med fokus på samarbete inom utveckling. Denna uppsats

undersöker co-opetition inom sälj, genom att titta på hur tunga lastbilsföretag kan ingå partnerskap inom sälj- och servicenätverk tillsammans med konkurrenter. Detta görs genom en litteraturgenomgång samt en flerfallstudie.

Utifrån vår analys presenteras ett ramverk för att bedöma och utforma potentiella partnerskap med konkurrenter. Ramverket består av sju viktiga faktorer för framgångsrika partnerskap inom sälj- och servicenätverk tillsammans med

konkurrenter; kulturell passform (cultural fit), icke-konkurrerande produkter, kompatibla mål, överbliven kapacitet i nätverket, dedikerade säljare, högt engagemang och

tålmodig implementering. Uppsatsen bidrar till forskning inom co-opetition inom sälj och med diskussion kring begreppen konkurrens, framgång och partnerskap.

Contents

List of Figures

List of Tables

Acknowledgements

Chapter 1 Introduction

1.1 Background

1.2 Problem Statement

1.3 Purpose

1.4 Research Question

1.5 Delimitations

1.6 Positioning

1.7 Terminology Discussion

1.7.1 Competitor Partnership

1.7.2 Partnership Performance

Chapter 2

Literature Review

2.1 Alliance Perspectives

2.2 Alliance Management

2.2.1 Success Factors

2.3 Co-opetition

2.4 Sales and Service Networks

Chapter 3

Methodology

3.1 Research Strategy

3.2 Data Collection

3.2.1 Pre-study

3.2.2 Literature Review

3.2.3 Case Studies

3.3 Data Analysis

3.4 Validity and Reliability

3.5 Research Ethics

Chapter 4

The Heavy Truck Industry

4.1 Industry Description