Customer-perceived value in business-to-business relationships: A study of software customers

Journal of Marketing ManagementVol. 28, Nos. 7–8, July 2012, 887–911

Customer-perceived value in business-to-businessrelationships: A study of software customers

Sara Parry, Bangor University, UKJennifer Rowley, Manchester Metropolitan University, UKRosalind Jones, Bangor University, UKBeata Kupiec-Teahan, Scottish Agricultural College, UK

Abstract Despite the importance of relationships in business-to-business(B2B) contexts, there is limited research as to what customers expect andvalue from relationships in industrial contexts. This study, therefore, seeksto understand customer-perceived value better by investigating actual andprospective customers in the software industry. A two-level analysis of customerperspectives on relationship attributes was conducted. First, semi-structuredinterviews were conducted with customers of a micro software firm. Insightsfrom these interviews were then used to inform the second stage of thestudy, an online survey using Adaptive Conjoint Analysis, to identify the relativesignificance of these attributes. A total of 256 industrial buyers completed thesurvey. A new Customer Relationship Attributes Model (CRAM) is presentedwhich encapsulates major attributes that current and prospective customersconsider when entering into a relationship with their software supplier. TheCRAM identifies five product-related attributes (price, functionality, bilingualcapability, location, and software quality), and seven service-related attributes(communication, understanding of the customer, trust, relationship, service,professionalism, and employee expertise).

Keywords SMEs; software technology; business-to-business; customer-perceived value; Adaptive Conjoint Analysis

Introduction

It is widely acknowledged that customer relationships are important to businesssuccess (Gronroos, 1994; Gummesson, 2002; Reichheld & Sasser, 1990). Similarly,the concept of customer-perceived value has received much attention withinacademic literature (Eggert, Ulaga, & Schultz, 2006; Ulaga, 2001; Walter, Ritter,& Gemunden, 2001), particularly in relation to the notions of relationships andrelationship quality (Lindgreen, Palmer, Vanhamme, & Wouters, 2006; Ulaga &

ISSN 0267-257X print/ISSN 1472-1376 online© 2012 Westburn Publishers Ltd.http://dx.doi.org/10.1080/0267257X.2012.698637http://www.tandfonline.com

888 Journal of Marketing Management, Volume 28

Eggert, 2006). Lichtenthal, Wilson, and Long (1997) argue that organisations needto understand what drivers create value for customers in order to build a competitiveadvantage. However, thus far, perceived value has mainly been considered from theprovider side (Fiol, Tena, & Garcia, 2011). The value perspective of both sidesis useful when investigating relationships (Henneberg, Pardo, Mouzas, & Naude,2009). However, this study aims to contribute to the theory of customer-perceivedvalue by focussing directly upon the customer and their expectations of relationshipsin an industrial context.

The significance of small-to-medium-sized enterprises (SMEs) to economicdevelopment is undisputed (Kumi-Ampofo & Brooks, 2009). In the software sector,many firms are micro firms. For example, in 2007 there were 1990 UK enterprisesengaged in software, of which 89% had turnovers of less than £500,000 and 86%employed fewer than five people (Keynote, 2008). This study, therefore, focuses onmicro software firms as providers of bespoke products and services in the business-to-business (B2B) sector. One of the problems experienced by such firms is effectivemarketing of their software solutions in competition with their larger counterparts(Slevin & Covin, 1987; Todtling & Kaufmann, 2001) and in highly competitiveenvironments (Borg, 2009; O’Dwyer, Gilmore, & Carson, 2009).

Research shows that effective communication and explanation of the softwaresolution to a prospective customer is highly dependent on effective relationships withpotential customers (Helander & Ulkuniemi, 2006). Despite increasing interest intohow small and micro firms market technology products, there is a paucity of researchon marketing approaches used by micro software firms (Jones & Rowley, 2009) andon the marketing of software technologies in general (Alajoutsijarvi, Mannermaa, &Tikkanen, 2000; Helander & Ulkuniemi, 2006; Ojasalo, Natti, & Olkkonen, 2008).One important exception that is of particular relevance for this research is the workof Westerlund and Svahn (2008) who examined relationship perspectives in smallsoftware firms. However, little research has focussed on customer-perceived value orcustomer expectations of relationships in the software industry.

This study aims to address these gaps by investigating the development ofindustrial relationships using the software industry as an example. Specifically,relationships between micro software firms and their current and prospectivecustomers are explored by identifying the attributes which customers deem importantwhen entering into a relationship with a software supplier. We contend that knowingwhat customers expect from a relationship can act as a useful starting point forsmall and micro software firms attempting to develop relationships and market theirsolutions. The study’s objectives are to:

• identify and prioritise the key attributes which customers value/expect in thesoftware supply relationship;

• develop an understanding of the nature and importance of these key attributes;

• propose a Customer Relationship Attributes Model (CRAM) that can be used bysoftware technology SMEs and other similar industrial firms to inform the actionsthat they need to take in order to enhance their relationships with customers andto improve customer-perceived value.

The paper begins with a literature review that considers research on RelationshipMarketing (RM) and value creation in B2B contexts, and on marketing, relationships,

Parry et al. Customer-perceived value in B2B relationships 889

and networking both in SMEs and in the software sector. Next, a two-stageresearch methodology is described. The use of semi-structured interviews withthe customers of a micro software firm to identify key attributes, followed bythe application of these attributes in an online survey using Adaptive ConjointAnalysis, in order to identify the relative significance of the attributes is outlined.Key findings are then presented and discussed; five product attributes and sevenservice attributes are identified. The paper concludes with consideration of thecontribution and application of the CRAM, together with managerial implicationsand recommendations for future research.

Literature review

This literature review will first consider RM and value creation in B2B contexts. Thisis followed by a summary of previous research on relationships and networking, bothin SMEs and in the software sector, along with a brief examination of the softwarepurchasing process and the importance of software quality.

RM is defined as a process ‘to establish, maintain, and enhance relationshipswith customers and other partners, at a profit, so that the objectives of the partiesinvolved are met. This is achieved by a mutual exchange and fulfilment of promises’(Gronroos, 1994, p. 9). In B2B marketing, some form of relationship will always existbetween suppliers and customers, whether it is a simple e-mail exchange or frequentcommunications. Therefore, the strength of a relationship can vary, with a ‘strong’relationship consisting of repeat purchase, willingness to invest in the relationship,and a reluctance to search for alternatives (Barry, Dion, & Johnson, 2008). However,a strong relationship does not necessarily equate to a fruitful relationship for bothparties. Often a supplier and/or customer’s perception of the importance of therelationship can vary. For instance, a relationship may be important to the supplierbut not as vital to the customer who has a large portfolio of suppliers. Building andmaintaining a relationship which is valued by both the supplier and the customerprovides the opportunity to develop a better understanding of the customer, andbuilds trust, commitment, and customer satisfaction (Gil-Saura, Frasquet-Deltoro,& Cervera-Taulet, 2009). This is especially pertinent in B2B contexts such as thesoftware industry where products and services are of a high value and high-risknature, and contact with the customer does not necessarily end once the product isdelivered. Regular communication, a willingness to co-operate on both sides, andeven an element of social bonding can lead to collaborative relationship success(Pinnington & Scanlon, 2009). Effective long-lasting B2B relationships have alsobeen recognised to create mutual value and can result in enhanced firm performance(Caceres & Paparoidamis, 2007).

Partnerships, alliances, and networks are also important aspects of B2Brelationships (Axelsson & Easton, 1992; Brennan, Canning, & McDowell, 2007) andare a means for firms to both identify potential customers and develop relationshipswith these customers. Additionally, business relationships are viewed as servicerelationships, as companies interact over a longer period of time in the buyingand selling process. However, although there is an increasing body of researchon customer satisfaction within business services (Homburg & Rudolf, 2001), thisresearch tends to focus on the buying process rather than the post-purchase outcome(Tyler, Patton, Mongiello, & Meyer, 2007).


The notion of value creation in business markets is a well-researched concept,and much of the literature examines relational value-based drivers and relationshipquality (Powell & Swart, 2010; Ulaga & Eggert, 2006). However, these are mainlyviewed from the supplier’s perspective (Walter et al., 2001). Exceptions includeLapierre (2000), who identified 13 value drivers from the customer perspective,including product quality, product customisation, and flexibility, and a recent studywhich identified functional (quality of the product), emotional (friendliness of theprovider), and social elements (image of the provider) as important dimensions ofperceived value in an industrial context (Fiol et al., 2011).

Effective RM benefits the supplier through increased co-operation, a betterunderstanding of customer requirements, and a dialogue with customers leading toa closer relationship and the co-creation of value through the development of co-created and customised products and services (Ruokonen, Nummela, Puumalainen,& Saarenketo, 2008). Financial benefits include reduced costs due to higher retentionof customers, and increased profits as a result of customers’ loyalty and reducedprice sensitivity (Gronroos, 1994; Reichheld & Sasser, 1990). Proposed benefits forcustomers include reduced anxiety and comfort in knowing their supplier, alongwith social benefits as a result of familiarity with employees, individualised andadditional services, and special treatment (Gronroos, 1994, 2007; Gummesson,2002). Hennig-Thurau and Hansen (2000) also noted that personalised treatmentwithin the relationship is a factor that customers value positively as they becomemore relaxed because they know what to expect from the supplying firm.

Numerous studies have confirmed that the presence of trust and commitmenthas a positive impact on relationships (Barry et al., 2008; Ivens, 2005; Melewar,Hunt, & Bridgewater, 2001; Takala & Uusitalo, 1996). Wilson and Jantrania (1994)observe that trust is a fundamental relationship construct, and, as a result, it isincluded in most relationship models. Using conjoint analysis, Naude and Buttle’s(2000) investigation of relationship quality identified trust and mutual integration ofneeds as the most common attributes of relationship quality.

Little attention has been directed towards the marketing of small and microtechnology or software firms (Alajoutsijarvi et al., 2000; Helander & Ulkuniemi,2006; Ojasalo et al., 2008). However, the few investigations conducted pointtowards the benefits of developing relationships, networking and alliances inthis sector. Helander and Ulkuniemi (2006) and Ruokonen (2008) consider themarketing of software as requiring a relational competency, viewing businesseswhich develop tailored software to be inherently relationship and service oriented,and, for that reason, a deep understanding of both the customer’s businessand the technology is key. In the software sector, relationships with existingcustomers are necessary not only for generating future revenue but also forproviding further business opportunities (Ruokolainen & Makela, 2007). Lastingrelationships with current customers facilitate a clearer understanding of customerrequirements and learning how end users use the software (Ahmed & Capretz,2007).

Networking is frequently cited as a means of developing relationships and isinfluential in the long-term sustainability of firms (Chetty & Stangl, 2010; Stokes,2000), and, in particular, SMEs (Todtling & Kaufmann, 2001). Boussara and Deakins(1999) found that social networks of entrepreneurs and strategic alliances areparticularly appropriate in the technology industry. Alliances also provide technologyfirms with the opportunity to share resources and capabilities, and can result in


a positive impact on value creation in the software industry (Swaminathan &Moorman, 2009).

The rapidly evolving nature of the software industry and fast obsolescence ofproducts makes the software industry an extremely competitive market (Kulmala& Uusi-Rauva, 2005; Ruokolainen & Makela, 2007). The importance of fruitfulrelationships as a way of satisfying and retaining customers is therefore combinedwith the importance of delivering quality software. The software design project isa key element of the marketing process, as achievement of the desired functionalityof the product is a precursor to its perceived quality and software project success(Agarwal & Rathod, 2006). Wilson and Hall (1998) also note that customer serviceis a vital element of software quality, especially to customers or users who are non-technical. This view is supported by Hall, Jagielska, and Badoo (2007) and Isaac,Rajendran, and Anantharaman (2006) who argue that employee competence andcustomer focus can enhance both the overall quality of the software service as well asthe development of relationships with customers. Professionalism is also an aspect ofservice quality, as it refers to the way employees present themselves, a firm’s serviceprocesses, as well as service tangibles (Gronroos, 2007). Software industry researchalso emphasises the importance of the human elements of software delivery as wellas technical expertise (Isaac et al., 2006).

To summarise, the development of mutually beneficial relationships withcustomers is crucial in the B2B context and these relationships depend onthe establishment of constructs such as trust, product and service quality, andpersonalised treatment of customers. However, there is a lack of research as to whatcustomers expect and value from a relationship in an industrial context (Fiol et al.,2011). Additionally, little is known about marketing and relationships in the softwareindustry (Alajoutsijarvi et al., 2000; Helander & Ulkuniemi, 2006; Ojasalo et al.,2008). This research therefore seeks to investigate the key attributes of successfulrelationships in the software industry from the customers’ point of view. The outcomeof this study also aims to inform software firm owner-managers about how to developand manage relationships effectively with their customers and thereby improve theirmarketing and business performance.

Methodology

Introduction

The research was conducted in two stages. The first stage consisted of qualitativeresearch and involved semi-structured interviews with customers of a micro softwarefirm. In order to provide context, a brief profile of the firm is offered below. In orderto maintain anonymity, the real name of the firm and its customers are not disclosed.Qualitative interviews were used both to identify the key attributes that customersfelt to be important in terms of their relationship with the software firm and tooffer elucidation on the essence of these key attributes. The second stage of thestudy used the key attributes identified in the first stage to inform the design ofan online questionnaire that used the Adaptive Conjoint Analysis tool to provokerespondents to make choices between attributes. The analysis from this process ledto the development of the CRAM, with rankings of the relative priorities accorded tothe different attributes. The following sections provide further details of the researchmethods adopted.


Profile of micro software firm

The micro software firm is based in North Wales, UK. The company began as abespoke software solutions company, providing customised database solutions, buthas since developed three ‘off-the-shelf’ software products. There is a sole owner-manager and eight full-time employees. The company has been operating for eightyears and has 20 customers. Core competencies include language support, bilingualsoftware solutions, and web-based information databases. Bilingualism is reflected inthe policies of many Welsh organisations, and therefore the firm caters for this marketby developing software interfaces in both languages. There is also a particular demandfor the advancement of the Welsh language in the field of IT (Welsh Language Board,2006). The firm’s marketing activities consist of networking at local and nationalbusiness exhibitions, developing relationships with prospective and current buyers,a Gold partnership with Microsoft, and collaborations with other Welsh softwareorganisations. This firm has won two awards based on their innovative capabilitiesand bilingual capability, helping them to improve their perceived credibility in themarketplace.

Qualitative interviews with customers

To investigate the attributes of software suppliers that are perceived valuable by theircustomers, semi-structured interviews were conducted with customers of the microsoftware firm. Although the research focuses on micro software firms as providers,in terms of identifying these attributes, the study embraced customer organisationsof all sizes (see Table 1 for respondent data). Although this heterogenous sample ofcustomers may not have similar wants and expectations, this sample was useful as afirst step to explore general buying patterns and customer expectations with regardsto software purchasing.

Interviews are invaluable to identify pertinent attributes for the conjoint analysisdesign (Green & Srinivasan, 1978), but were also chosen to investigate the dyadicrelationships between the micro firm and their customers. Semi-structured interviewshave previously been used to explore marketing in SMEs with the aim of gainingunderstanding and meaning whilst allowing flexibility and an element of structure(Gilmore, Carson, & Grant, 2001; O’Donnell, 2004). A list of themes to cover ininterviews was compiled based on the RM literature and to provide some structure.However, an open approach to interviews was taken, with the intention of allowingcustomers to specify their important attributes without being led. A list of topicsgenerally discussed in most interviews is included in Appendix 1. A total of 15 out of20 customers agreed to be interviewed face-to-face and were assured of completeconfidentiality. A pilot interview was initially conducted with an independentsoftware consultant in order to test the validity and appropriateness of the topics.In total, 18 customers were interviewed, as three firms had two respondents. Allinterviews lasted approximately one hour.

Interview analysis

Interviews were recorded and transcribed verbatim, and content analysis was usedto analyse the data (Hill & Wright, 2001; Miles & Huberman, 1994). The semi-structured interviews resulted in a list of 23 attributes which were cited by the


Table 1 Interview respondent data (customers of the micro software firm).

Customer IndustryNumber ofemployees

Number of respondentsand their roles Sector

1 Financial services 20 1 – Managing Director Private2 Chemical 560 1 – IT Manager Private3 Information services 10 2 – Managing Director

and Head of Systems(separately)

Not forprofit

4 Government 9000 1 – IT Manager Public5 Government 20, 000 1 – Programme Manager Public6 Public services 50 1 – IT Manager Public7 Agriculture 1 1 – Managing Director Private8 Sustainable

development5 1 – Managing Director

and Website Officer(combined)

Public

9 Utilities 15 1 – Finance Assistant Private10 Manufacturing 350 1 – IT Manager Private11 Government 3000 1 – IT Manager Public12 Emergency services 1700 1 – ICT Software

EngineerPublic

13 Government/economicdevelopment

1000 2 – IT Manager andProject Manager(separately)

Public

14 Government 50 1 – Research and GrantsManager

Public

15 Medical 2000 1 – Project Manager Private

respondents. On closer examination it became clear that many of the attributescould be grouped; for example, ease of use and well-tested software were groupedunder ‘quality of software’, after-sales support and customer service were groupedunder ‘service’. Therefore, a process of screening and categorising the attributes intoa manageable number was carried out, and respondent statements and key wordswere coded under each attribute. To achieve inter-rater reliability, an independentcolleague carried out the same tasks, and the coded statements and attributes werecompared until concurrence was reached (Altheide & Johnson, 1994; Hodder,1994; Miles & Huberman, 1994). This process resulted in a finalised list of12 attributes which were considered distinct and thus constituted the variables whichwould be entered into the conjoint survey. Between two and four levels for eachattribute were defined and used as measurement items (e.g. for ‘Communication’,the levels would be ‘Structured communication’ and ‘Ad hoc communication’).In accordance with Hair, Black, Babin, Anderson, and Tatham (2006), levels weredefined using communicable statements, were mutually exclusive, and covered arange of possibilities without being unbelievable. The number of levels was alsobalanced across attributes, as the estimated importance of the attribute tends toincrease as the number of levels increase (known as the ‘number of levels effect’).To improve validity, the attribute levels were created with the participation of asoftware consultant and were verified by an independent researcher with conjoint


analysis experience. A list of all attributes and levels used in the study are providedin Appendix 2.

Conjoint analysis

Conjoint analysis was selected in order to prioritise the attributes and establish amore realistic approximation of consumer purchase decisions. Conjoint analysis isan indirect method of asking prospective and current customers about the relativeimportance of attributes, and therefore avoids the problem of consumers saying onething and doing another, thereby reflecting their actual behaviour (Green, Krieger,& Wind, 2001). Mohr, Sengupta, and Slater (2005) provide a rationale for theapplication of conjoint analysis in the technology sector, and support is providedfor using conjoint analysis in new product development (Kang, Kim, & Park, 2007).Conjoint analysis has also been successfully applied in the research of technologySMEs in exploring strategy development and product offerings (Schmidt & Gary,2002).

Adaptive conjoint analysis

The specific conjoint methodology applied was Adaptive Conjoint Analysis (ACA)from Sawtooth Software. In the first, self-explication stage of the ACA procedurerespondents expressed their preferences and ascribed importance ratings toindividual attributes. Next, they were asked to indicate preferred profiles andperform graded paired comparisons based on varying combinations of the attributelevels. Examples of paired comparisons are provided in Appendix 3.

ACA was selected, as it is a computer-assisted solution, with a direct data inputsystem which can handle a large number of attributes (up to 30). Another advantageof ACA is its ability to ‘adapt’ to each respondent’s answers as he or she goesalong, and capture the areas of greatest interest. The composition of questionsasked varies per respondent and assumes a dynamic approach with active, explicitcompetitors as opposed to a static assessment of pre-defined product profiles.Alongside the ACA procedure, the survey also involved gathering information aboutthe role of each respondent, the size of the organisation, the industry, and theirsoftware purchasing processes (how they normally buy software, what type ofsoftware company are they likely to use). The survey was available online, andpurposive sampling procedures were used to target market-oriented companiesin North Wales. The total number of operating enterprises in North Wales in2009 was 51,615 (StatsWales, 2011), which provides an estimation of the population.A sampling frame of 1000 companies was created by compiling a list using directoriessuch as The Strategic Wales (top 300 organisations in Wales) and The Wales Index.The key decision makers involved in the software purchase process were carefullyidentified by making telephone enquiries within each company and a link wasthen e-mailed to each of the relevant respondents. In smaller firms, the decisionmaking unit usually consisted of one employee (such as the owner-manager or anIT manager), but within the larger public or privately owned companies which hadmore complex decision making units, those who were most knowledgeable andinfluential in the software purchase process completed the survey. A reminder e-mailwas subsequently sent, resulting in 256 surveys being completed, a response rateof 25.6%.


Analysis was conducted by estimating the part worth utilities at the total samplelevel (aggregated data) with Ordinary Least Squares. In order to include individualdifferences, the Hierarchical Bayes (HB) (Allenby, Arora, & Ginter, 1995) modelwas used to estimate the parameters which were used to underpin the developmentof CRAM. The HB algorithm is a recent addition to ACA and is able to producemore accurate results, especially in terms of adequately capturing consumers’heterogeneity in empirical data (Backhaus, Hillig, & Wilken, 2006; Moore, 2004).The HB algorithm works by producing betas that fit each individual’s outcomereasonably well, but ‘borrows’ information from other respondents to stabilise theestimates. HB supports the view that marketers should focus on profiling individualsand segments as opposed to the market average. Finally, cluster analysis was carriedout as a complementary technique to conjoint analysis (Odekerken-Schroder,Ouwersloot, Lemmink, & Semeijn, 2003) in order to establish whether any segmentsexisted in the data.

Findings and discussion

ACA findings

The findings of both the semi-structured interviews and the ACA survey are presentedin a conceptual model displaying customer expectations of software suppliers(CRAM), shown in Figure 1. The ACA works out average importances by dividing

Figure 1 Customer Relationship Attributes Model (CRAM).

Trust

Price

Functionality

Bilingualcapability

Location

Softwarequality

Customer Attributes (ofsoftwaresuppliers)

Understandingof thecustomer

Employee expertise

Relationship

Communication

Professionalism

Service

12.80

4.01

Product-Related Attributes

Service-Related Attributes

4.63

10.33

6.825.29

11.40

8.79

6.10

8.80

9.53

11.50

* Numbers denote weights from ACA/Strengths ascribed to individual attributes


100% points between the different attributes. They are shown in this model asnumbers ascribed to arrows representing the weighting of each attribute in the mindof a customer.

The model shows that customers deem ‘Software quality’, ‘Professionalism’, and‘Understanding of the customer’ to be the three most important attributes whenselecting a software supplier, which is consistent with Lapierre’s (2000) value drivers.The combination of service-related and product-related attributes including theimportance of ‘Functionality’ and ‘Service’ also mirror the findings of Fiol et al.(2011) who noted the importance of functional as well as emotional values. Thescores relating to ‘Relationship’ and ‘Communication’ do not explicitly support thenotion that a relationship with the firm is a primary driver of software purchase(Helander & Ulkuniemi, 2006; Ruokonen, 2008). However, other highly rankedattributes, namely ‘Understanding of the customer’ and ‘Trust’, suggest that anunderlying relationship based on these attributes is expected (Gil-Saura et al., 2009;Wilson & Jantrania, 1994). The importance of ‘Trust’ also corresponds with thefindings of Naude and Buttle (2000) in their investigation of relationship quality. Theleast important attributes are ‘Location’ and ‘Bilingual capability’, suggesting thatthey are desirable rather than essential attributes to prospective customers. Overall,the CRAM provides a basis for measurement of customer-perceived value of businessrelationships in an industrial context, and provides a hierarchy of attributes from thecustomer perspective supported by deeper probing of the attributes by discussionswith customers of the micro software firm. All 12 attributes are discussed in furtherdetail below and illustrated with statements from respondent interviews.

Software quality

‘Software quality’ was ranked as the most important attribute, suggesting thatcustomers consider the delivery of a quality software solution as a primaryexpectation of their software supplier:

the software service was of high quality. (Customer 6)

Words that come to mind when thinking about (the SME) are reliable and highquality. (Customer 10)

However, the interview findings suggest that software customers have differing viewsas to what constitutes software quality. Some referred to software developmentprocesses and the use of formal, rigorous approaches to software development, whilstothers emphasised the role of customer service or user participation, suggesting thatthere is a need for ‘shared understanding of quality’ (Wilson & Hall, 1998, p. 67).Other descriptions of quality software products included software scalability, easyintegration with other products, ease of use, and overall software design. Scalability,integration, and testing are tangible facets of software quality, but the ranking of otherattributes suggests that physical product quality is not all that customers expect. Theservice and intangible aspects of quality, such as a relationship with the supplier andconfidence in the employees, suggest that software quality is a combination of serviceand product quality:

The opportunity to build a relationship with them. This is important because if youhave a good relationship with the company, you are a lot more likely to achieveyour objectives because you can talk to them easily, they will understand you, you


can build up the rapport, they will know where you’re coming from, and you canboth move forward together. (Customer 3)

This corresponds with some of the studies exploring software quality conceptsand supports the idea that the human element is necessary to achieve creation ofquality software (Hall et al., 2007; Isaac et al., 2006). Indeed, Customer 3 (above)acknowledges that having this shared understanding helps them develop qualitysoftware which supports the notion that the need for a regular dialogue improvesunderstanding of quality as well as the overall business relationship (Pinnington &Scanlon, 2009).

Professionalism

According to the ACA findings, ‘Professionalism’ is the second most importantattribute. The interview respondents identified a professional supplier as one whichwas reliable and was committed to following formalised processes (e.g. workingto project management guidelines and following established software developmentprocesses). The respondents indicated that their supplier was clearly working towardsimproving their project management procedures, but some customers remainedreluctant to enter into large contracts without a larger partner to diffuse the risk:

Their smallness does show through their written document skills, theirapproaches to project management. (Customer 13)

If we entered into a big software development project, we would want to formalisethe arrangements and we would want a formal structure. (Customer 4)

This illustrates the importance of strategic alliances and established processes whichcan help micro software firms develop reputation and credibility (Swaminathan &Moorman, 2009).

Understanding of the customer

‘Understanding of the customer’ was ranked as the third most important attribute forcustomers when selecting a software supplier:

They understand what we are trying to achieve, they understand the way that wework and they bend over backwards to accommodate our wishes. (Customer 3)

This further reveals the need for understanding the customer’s requirements,which can be aided by consistent communication between both the firm and theircustomers. This is especially important when a customised software solution isunder development, a view already supported by Helander and Ulkuniemi (2006).This also confirms the views of RM researchers who consider investment in long-term relationships as having a win–win outcome for both parties (Gronroos, 1994).Understanding of the customer is arguably even more important in the context ofinnovation (Hauser, Tellis, & Griffin, 2006), as an innovative solution based oncustomer needs is much more likely to succeed in the marketplace. More specifically,software firms are required to develop an understanding of the end user andencourage customer participation in the dialogue, a finding which aligns stronglywith the work of Isaac et al. (2006) and Saiedian and Dale (2000).


Functionality

‘Functionality’ was ranked as the fourth most important attribute. This is a product-related attribute, as it refers to the software product itself: whether it meets thecustomer requirements and detailed specifications in terms of functionality andwhether it solves the business problem for which it is developed. In terms of thisstudy, the majority of prospective and current customers in both the semi-structuredinterviews and online survey were IT managers with a clear idea about what typeof software functionality they needed. They emphasised the importance for softwarefirms to clarify what functionality their software offers and how it can resolve thecustomer’s business problem. Achieving the desired functionality of the product haspreviously been identified as a vital criterion in defining software project success(Agarwal & Rathod, 2006) and is a basic requirement in delivering software:

We have to know that if we give a spec to a software company, that they are ableto perform, that we will get what we asked for and within the timeframe we’vespecified. (Customer 2)

This emphasises the necessity to deliver the desired functionality within a specifiedtimeframe and also highlights the needs, market, and transaction uncertainty whichbuyers often face in an industrial context (Hakansson, Johanson, & Wootz, 2002).

Employee expertise

‘Employee expertise’ incorporates the technical skills and competencies of theemployees, as well as their manner and approachability:

I think they’ve got an exceptional development team. (Customer 12)

Customers considered that having confidence in a firm’s software employees and theactual product were both important, indicating the significance of the relationshipnot only among the primary contact such as the IT Manager, but between softwaredevelopers and the customer:

Can you work with the people, the company; can they fit into the culture of yourorganisation? . . . You need to be able to get along as individuals. (Customer 8)

Here, Customer 8 highlights the importance of ‘soft’, human elements, as well astechnical expertise in the software purchasing process (Isaac et al., 2006).

Service

‘Service’ was ranked sixth out of the 12 attributes, enhancing the argument that thequality of service provided by software companies is equally as important as productquality. The interview respondents referred to after-sales service, which has beennoted as a valuable dimension of B2B relationships (Tyler et al., 2007), and is animportant expectation of many software buyers:

Software companies now have to look at the whole life cycle from the businessprocess re-engineering, through to the delivery, from specification to deliveryand ultimately the support and then the ongoing optimisation so they don’t justwalk away when they’ve delivered. (Customer 11)


The importance of this attribute is consistent with many studies within the B2Bmarketing realm (Brennan et al., 2007; Gronroos, 2007). In terms of software,service emphasises the importance of delivering a solution consisting of customerrequirements definition, customisation, and post-deployment support, concurringwith the work of Tuli, Kohli, and Bharadwaj (2007). However, as with ‘Softwarequality’, the ‘Service’ attribute was perceived differently by various respondents, butthe consensus was on the integration of professional processes and timely delivery ofthe software.

Trust

The inclusion of ‘Trust’ in the CRAM emphasises the role of trust as a characteristicof successful relationships (Barry et al., 2008; Ivens, 2005; Melewar et al.,2001). Although ‘Trust’ was rated higher than the ‘Relationship’ attribute, theinterviews indicated that some customers are inclined to trust a supplier basedon recommendations from contacts, software firms’ established reputation, andperceived credibility in the marketplace. In this case, there is an element of trustbefore the relationship has begun. Openness, honesty, and communication also arosein the interviews as important constructs of trust. Moreover, respondents commentedthat their software firm’s realism in their capability increased trust as well as theirintegrity:

they’re very clear about what they can and can’t do for us. (Customer 9)

This supports the assertion that customers become more relaxed when they knowwhat to expect from their supplier (Hennig-Thurau & Hansen, 2000).

Price

Many interview respondents identified that the price of software was a critical factorin the software decision-making process, as business customers are forced to workwithin tight budgetary constraints. However, ‘Price’ was not a dominant attribute inthe ACA findings. This suggests that price, when traded off against other factors suchas ‘Service’ and ‘Trust’, decreases in importance. From the customer’s perspective,the price of software has to be competitive, and although total software costs cansometimes not be set in advance, particularly when developing a bespoke solution,the communication of costs to the customer throughout the software project wasdeemed vital:

People need to be happy with the costs and how the company communicates thecosts . . . this communication is vital. (Customer 2)

This further supports the need for regular communication and transparency of costsbetween the supplier and customer.

Relationship

The interviews indicated that customers view a long-term relationship with theirsoftware firm as an important expectation, but the ACA findings show that whenfaced with other options, more definitive factors become apparent. These factorsinclude ‘Trust’ and ‘Understanding of the customer’. Therefore these elements


have been identified as constituents of relationships in the software industry, as isconfirmed by Barry et al. (2008), Ivens (2005), Melewar et al. (2001), and Mohr andSpekman (1996). Therefore, we contend that although potential customers do notexplicitly demand a relationship as a primary expectation in the software purchasingprocess, it remains an important attribute which is linked to attributes that customersdo rate as being very important, such as understanding and professionalism (Brennanet al., 2007). This is consistent with the views of Helander and Ulkuniemi (2006)and Ruokonen (2008) who identified the development of relationships as importantin the software industry.

Communication

Although communication was not ranked highly in contrast to the other attributes,the qualitative findings indicated that regular communication among customers andtheir software supplier plays a key role in furthering the business relationship.Therefore we assert that communication, like relationships, is intimately associatedwith other attributes that customers do value. These include the understanding ofcustomer needs and trust, and are facets of communication also identified by Hallet al. (2007) and Hunt, Arnett, and Madhavaram (2006).

Bilingual capability

‘Bilingual capability’ is a software attribute required by a number of customers inWales. The attribute is functional as well as a symbolic, and was deemed an importantfactor in the customers’ decision-making process:

The bilingual factor is important . . . the ability of the company to deliver bilingualsystems certainly featured when we were looking for a company to work with.(Customer 14)

However, the ‘Bilingual capability’ attribute was traded off against almost all otherattributes in the ACA survey. This indicates that despite the importance for someorganisations in Wales of practising bilingualism, such as those within the publicsector, they do not regard bilingual software capabilities as an essential requirement.

Location

‘Location’ was rated the least important attribute out of the 12 attributes. However,some interview respondents said that they deliberately sought out a local supplier,the main reason being the ease of interaction at short notice, which was deemed vitalin developing a complex bespoke product:

We were definitely after someone local who was within an hour or so distancefrom us, just to make the interaction between the customer and the suppliereasier. (Customer 10)

In some instances, the customer required software developers to work closely within-house employees, which was made easier if they were in physical proximity toeach other. Therefore, although ‘Location’ is not one of the pre-dominant factorsin customers’ minds when selecting a software supplier, it can be a vital attribute


to some, emphasising the importance of dialogue and the firm’s understanding ofcustomer requirements.

Segmentation of ACA respondents

ACA provided an overview of what customers expect from their software firms.However, it did not indicate whether any segments of respondents existed in the data.Any indication of groups or classes which would differ according to their decision-making criteria would be invaluable, as SMEs could thus tailor their marketingapproach to varying segments. Therefore, a two-stage cluster analysis was undertakenbased on the relative importance of the 12 attributes for each respondent, whereby ahierarchical clustering analysis preceded that of K-means clustering. The hierarchicalclustering procedure via the agglomeration schedule showed that a four-clustersolution best fit the data. Thus a K( = 4)-means clustering was applied. A MANOVAwas examined to show that the clusters were different (Wilks’ lambda = .077, F =27.347, p < .001) on the basis of the 12 clustering variables used. Tests of between-cluster effects show that 10 out of the 12 attributes significantly discriminate betweenthe four clusters. (See Table 2).

Figure 2 illustrates the relative importance of each attribute in each cluster. Thefindings show that Cluster 1 prioritises service and employee expertise but is thecluster which is least concerned about price and location. In contrast, Cluster 2regards price as an important attribute along with relationship, location, and bilingualcapability. Cluster 3’s most important attributes include an understanding of thecustomer and software functionality but is least concerned with bilingual capabilityof the software. Cluster 4 is least concerned about a relationship but is the clustermost concerned about software quality. Cross-tabulations of the clusters againstdemographics of the respondent such as size of the firm and industry did not revealany significant findings. In order to verify the clustering, a Multivariate DiscriminantAnalysis (MDA) was applied to check whether the groups were correctly classified(Hair et al., 2006). The sample was split randomly but ensuring an equal numberof cluster membership in both the estimation and the holdout samples. The resultsare in Table 3 and shows that 95.3% (the hit ratio rate) of respondents were

Table 2 Tests of between-cluster effects.

Attribute Mean square F value Sig.Understanding of the customer 6.861 4.159 .007Price 52.400 22.642 .000Relationship 148.606 71.881 .000Software functionality 10.882 8.911 .000Bilingual capability 218.270 162.811 .000Employee expertise 7.595 5.323 .001Trust 1.184 .918 .433Software quality 9.107 4.882 .003Professionalism 1.055 .917 .433Location 53.419 25.570 .000Communication 57.642 26.273 .000Service 21.430 14.801 .000


Figure 2 K-means cluster chart.

0.00

2.00

4.00

6.00

8.00Understanding of the Customer

Price

Relationship

Functionality

Bilingual Capability

Employee Expertise

Trust

Software Quality

Professionalism

Location

Communication

Service

c1

c2

c3

c4

Table 3 Classification results (MDA).

Predicted groupmembership

ClusterNumber of

case 1 2 3 4 TotalCasesselected(estimationsample)

Original count 1 24 0 0 3 272 0 15 0 0 153 0 0 35 2 374 1 0 0 48 49

% 1 88.9 .0 .0 11.1 100.02 .0 100.0 .0 .0 100.03 .0 .0 94.6 5.4 100.04 2.0 .0 .0 98.0 100.0

Cases notselected(holdoutsample)

Original count 1 23 2 0 2 272 3 11 2 0 163 2 0 33 2 374 0 0 1 47 48

% 1 85.2 7.4 .0 7.4 100.02 18.8 68.8 12.5 .0 100.03 5.4 .0 89.2 5.4 100.04 .0 .0 2.1 97.9 100.0

Estimation sample = 95.3% of selected original grouped cases correctly classified.Holdout sample = 89.1% of unselected original grouped cases correctly classified.

correctly classified in the estimation sample, and 89.1% of respondents were correctlyclassified in the holdout sample, indicating a high level of predictive accuracy.The Press’s Q statistic was also calculated to test the discriminatory power of theclassification. The Press Q was 337.5 for the estimation sample and 280.2 for theholdout sample, which greatly exceeds the critical value of 6.63. The maximum


chance threshold and proportional chance threshold were also calculated for bothsamples. For the estimation sample, the maximum chance threshold was 46.88%and the proportional chance threshold was 35.54%. For the holdout sample, themaximum chance threshold was 47.85% and the proportional chance threshold was36.04%. In all instances, the hit ratio rates are substantially higher than the thresholdvalues, indicating an acceptable level of classification accuracy. We therefore concludethat the classification accuracy of the analysis significantly exceeds the classificationaccuracy expected by chance.

Conclusion and managerial implications

This study makes a contribution to the theory of customer-perceived value in B2Bcontexts by providing an insight into the attributes of value in relationships in thesoftware sector, with specific reference to micro firms in this sector. A new CRAMis developed to represent the attributes of value in relationships from the businesscustomer perspective. The model was developed using ACA by embracing the viewsof customer firms of all sizes, but was informed by semi-structured interviews withcustomers of a micro software firm. Therefore, the CRAM is a practically useful toolfor small and micro software firms, but is also applicable to other firms in similarindustrial contexts, as it offers insights into the key customer attributes and theirrelative weightings. It can also be used by firms to plan in greater detail their approachto improving B2B relationships and business performance. In theoretical terms, themodel provides ‘explanation’ and ‘prediction’ (Lee & Greenley, 2008, p. 876) of thecustomer decision-making process in a specific industry on the basis that it prioritisesthe attributes which are important to customers. The ACA method is unique, asit forces respondents to make realistic trade-offs between attributes. This methodcould therefore be used with larger groups of customers in the future with the aim ofadvancing theory even further.

This research also furthers the developing research in software marketing and, inparticular, the issues for small and micro software firms in relation to the marketingof software. SME researchers (Carson, Cromie, McGowan, & Hill, 1995; Gilmoreet al., 2001) observe that small firm owner-managers carry out marketing that isinstinctive, unplanned, and intuitive. However, there is evidence here to suggest thata more structured approach to marketing may offer greater opportunities for smallsoftware technology firms in the long term. Having guidelines to follow is valuable tosmaller firms who often have strong technical but sometimes more limited managerialcompetencies (Scozzi, Garavelli & Crowston, 2005).

On the basis of this research, micro firm managers should note the centralsignificance of software quality in customer evaluation of software suppliers; inthe view of customers, software quality includes ease of use, level of testing,and the software’s flexibility. On the other hand, the weightings placed on otherservice-related attributes imply that service quality is also highly regarded bycustomers (professionalism of the software supplier, their understanding of customerrequirements, and the opportunity to develop a long-term relationship with thesupplier). In essence, the inherent nature of software purchasing means that a humanelement in delivering the service solution is vital in achieving customer satisfaction


and strongly supports the need for development of fruitful, highly interactiverelationships even for the smallest software organisations, even if it is not explicitlystated as a priority requirement. This is particularly relevant for customers in the B2Bsector requiring customised solutions.

In view of the clustering results, future research should aim to ascertain whattype of firm belongs to each cluster (size, industry, etc.). Other recommendations forfurther research include applying the CRAM to SMEs in different industry sectors.This would improve the model’s predictability and yield more precise insights into thekey attributes of successful RM and marketing in these sectors. We have developed amodel which is a useful starting point for small firms across B2B contexts. However,when applied in different contexts, nuances of specific industries are likely to arisewhen investigating different sectors. Therefore, the CRAM can be easily modified toreflect customer expectations in any B2B industry.

Further testing and application of the CRAM in larger software firms could alsobe conducted to identify specific points of difference in the software industry as awhole, and longitudinal case study based research could also be carried out withorganisations that have used the model to inform their practice. Such researchmight offer insights into the challenges associated with responding to customers’expectations or relationships and any impacts on business performance.

This study interviewed a diverse sample of customers, all of which had boughtsoftware from the same firm. Thus future research should focus on differences amongcustomers and their buying situations in the same industrial context. Software qualityis clearly identified as important. However, there is evidence that there may not bea consensus on what constitutes software quality. Therefore, further investigationinto this key concept from the customer perspective and other stakeholders is ofspecific interest to the software sector. This work is instrumental in that it producesa set of criteria about what customers expect from a relationship with their supplier.However, further research into the notion of ‘attraction’ in business markets (Hald,2009) would provide a more holistic perspective of the nature of relationships andhow to satisfy business customers.

Acknowledgements

The authors would like to thank Professor Edward Shiu for valuable comments on an earlierdraft of this manuscript.

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Appendix 1. Topics covered in interviews

• Background to the relationship – how the relationship was formed and why thiscompany was selected as a software supplier.

• Description of the relationship.

• Problems which may have arisen in the relationship – why they occurred,solutions to problems.

• Satisfaction with the product in terms of quality and features.

• Satisfaction with the service offered by the company.

• Communication, dialogue, and feedback within the relationship.

• Behaviour of the staff and their approach to customer service, including after-sales service.

• Strengths and weaknesses of the company.

• Overall impressions of the company – likelihood of recommendations andconsideration of further purchases.

• Responsiveness of the company to customer needs.

• Relationships with other software companies – thoughts and comparisons withcurrent company.

• Important factors influencing choice when purchasing a software product/service.

• Other important attributes in the decision-making process – why they areimportant.

• Expectations of the software product provided by this company.

• Overall expectations of a software company.

• Any other comments.

http://www.byigwlb.org.uk/English/publications/Publications/3963.pdf


Appendix 2 A list of attributes and levels used in the ACA survey

Attributes Levels1. Understanding of thecustomer

The software supplier has a comprehensive understandingof customer requirementsThe software supplier has a good understanding ofcustomer requirementsThe software supplier has a limited understanding ofcustomer requirements

2. Price Low-priced softwareHigh-priced software

3. Relationship Mutual and long-term relationship with the softwaresupplier. Future purchases likely.Transactional and short-term relationship with the softwaresupplier. One-off software purchase.

4. Functionality Software has useful functionalitySoftware functionality is limited

5. Bilingual capability Software supplier offering bilingual softwareSoftware supplier offering English software only

6. Employee expertise Software developers have extensive experience in the ITindustry and recognised qualificationsSoftware developers have extensive experience in the ITindustry but no recognised qualificationsSoftware developers have recognised qualifications but nopractical experience in the IT industrySoftware developers have limited experience in the ITindustry and no recognised qualifications

7. Trust The customer has confidence in the supplier’s reliability andintegrity by means of past experiencesThe customer deems the supplier to be reliable and hasconfidence in the supplier through recommendations andreferences onlyThe customer and supplier have no past experiences andtrust is yet to be established

8. Software quality Well-tested software providing positive user experienceUser-friendly software but not thoroughly testedWell-tested software but difficult for users to useUntested software which is difficult to use

9. Professionalism Supplier is reliable and has high standard formal processesin placeSupplier is reliable but has no formal processes in placeSupplier has formal processes in place but can beunreliableSupplier can be unreliable at times and no formal processesin place

10. Location Local software companySoftware company based anywhere in the UK

(Continued)


Appendix 2 (Continued).

Attributes Levels11. Communication Structured communication with the software supplier

Ad hoc communication with the software supplier12. Service Full end-to-end service offered including training and

after-sales supportAfter-sales support is included but no training provided bythe software companyTraining is provided with the software but no after-salessupport offeredSoftware product delivered only. No training and noafter-sales support included in the price.

Appendix 3. Examples of paired comparisons used in ACA

If these software companies were identical in all other ways, which would youprefer?

If these software companies were identical in all other ways, which would youprefer?

Well-tested software but difficult forusers to use

Ad hoc communication with thesoftware supplier

Structured communication with thesoftware supplier

Local software company

orSoftware company based anywhere

in the UK

StronglypreferLeft

SomewhatPrefer Left

Indifferent SomewhatPreferRight

StronglyPreferRight

StronglypreferLeft

SomewhatPrefer Left

Indifferent SomewhatPreferRight

StronglyPreferRight

The customer has confidence in thesupplier’s reliablity and integrity by

means of past experiencse

User friendly software but notthoroughly tested

The customer and supplier have nopast exepriences and trust is yet to be

established


About the authors

Sara Parry is a lecturer in marketing at Bangor University, Wales, UK. Her research interestsinclude SME marketing, Relationship Marketing, and the marketing of technologies.

Corresponding author: Dr. Sara Parry, Bangor Business School, Room 2.09 Hen Goleg,Bangor University, College Road, Gwynedd, LL57 2DG.

T (0044) 01248 388457E [email protected]

Jennifer Rowley is a professor in information and communications at ManchesterMetropolitan University, UK. Her current research interests are in the use of digitalinformation and technologies, marketing and branding, e-business, innovation, and knowledgemanagement.

T (0044) 0161 247 6137E [email protected]

Rosalind Jones is a lecturer in marketing at Bangor University, Wales, UK. Her research currentresearch interests include entrepreneurial marketing, SME marketing, and B2B marketing.

T (0044) 01248 388790E [email protected]

Beata Kupiec-Teahan is a marketing economist at the Land Economy R&D of ScottishAgricultural College, Scotland, UK. Her research interests include applied marketingmodelling, (food) marketing research, and marketing research methodologies.

T (0044) 0131 535 4186E [email protected]

mailto:[email protected]




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