Evidence-based criteria for successful digital ...

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Evidence-based criteria for successful digital matching: Inferences for SME business transfers

October 2015

Lex van Teeffelen (PhD) Professor of Finance and Firm Acquisitions HU Business School Utrecht Netherlands [email protected]

Edwin Weesie (MSc) Doctoral Researcher Business Transfers HU Business School Utrecht Netherlands [email protected]

Index

Summary of inferences

3

1. Introduction

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2. Business Transfer Research

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3. Search Criteria

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4. Trust in online matchings platforms

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Different types of trust Empirical Findings Inferences for business transfer matching platforms 5. Online dating

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Access and choice overload Communication No matching algorithms can predict success Inferences for business transfer matching platforms 6. Success of web-based matching platforms

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Inferences for business transfer matching platforms References

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Summary of inferences A search on B2B web-based matching platforms has been performed both on EBSCOhost and Google Scholar. The focus is on B2B (double sided) matching platforms, involving buyers, sellers and/or their intermediaries. In reading the abstracts of some 200 studies, three themes evolved: 1. Research on trust and privacy issues concerning digital transactions on platforms. 2. A well and thoroughly researched area of online dating. 3. Few studies on success of web-based B2B matching platforms. These following outcomes emerged, substantiated with 97different direct references, including dominant research publications in the field of SME business transfers. Trust and privacy issues Trust is vital for any digital service. Appearances of websites in presentation, but more importantly the easiness to use the website and its content quality have proven to increase trust. Empirical outcomes corroborate Sztompka’s (1999) set of criteria to build trust are confirmed: reputation, performance and appearance. Reputation refers to a record of past deeds and increases trust strongly. Performance includes present conduct and currently obtained results. A track record of matching platforms, a seal of approval and third party recognitions, have shown to enhance online trust dynamically both as part of reputation and obtained results. Additional services and offline presence by matching platforms, seem to be of importance in the longer run, since they generate more familiarity of users, word-of- mouth promotion and a variety of services to be assessed by customers. Inferences for matching platforms  Show a track record of success for buyers and sellers to increase traffic and trust.  User-friendliness and a content quality also increase trust in e-commerce.  Create a seal of approval or invite independent third party ratings to increase trust and transactions on matching platforms.  Provide for additional services and offline presence to generate more familiarity, trust and usage. Online dating Sellers finding buyers on the web have characteristics of online dating. The online dating research indicates that assessing implicit preferences, assesing previous similar transactions, increase predictions of matching success hugely. Well organized search procedure and easy to use search engines are vital to match profiles. Presenting an infinite numbers of profiles leads to choice stress and less commitment of buyers and sellers. Fewer initial outcomes generated for a first assessment increase a better likelihood of first contacts and commitment. No heuristic so far can predict matching success, because real life interactions are best predictor 3

in any match. Computer Mediated Contact (CMC) services with mutual consent, like skype, sms or e-mail contact, overcomes part of the superficiality of matching profiles and speed up first real life meetings. Inferences for matching platforms  Introduce procedures to assess implicit preferences.  Introduce smart matching tools showing few rather than a lot of profiles.  Introduce Computer Mediated Communication services on matching platforms. Success of matching platforms An overview study on the topic is missing. First results indicate that in the first period of business transfers platforms the quantity of profiles increases transactions, but at a later point the quality of the portfolio prevails in increasing transactions. Private and public platforms use other types of guiding principles and governance. Where competition/market share and price/profits prevails for private platforms, behavior of all stakeholder and reputation prevails for public platforms. This may complicate cooperation between private and public platforms. The development of dominant parties in the market does not have to interfere with collaboration. Two conditions seem to favor collaboration: the less similar the platforms, the most likely collaboration and the more dominant a platform, being secure of leadership, the more likely collaboration with rivals. Direct network connections seem to be most profitable for collaborating parties, leading to more transactions. Technological upgrading of low-tech platforms is not favorable for them in a highly competetive market. Inferences for matching platforms  Collaboration with other (dominant or smaller) platforms is well possible. The less similar the platforms operate and the more secure dominant platforms are on their leadership, the more likely collaboration is to be successful. Collaboration between platforms and even rivals seem to lead to more transactions.  Matching platforms in less developed markets are likely to be more successful in matching by increasing the number of profiles on their platform first. For matching platforms in developed markets it seems wiser to develop the quality of the profiles, rather than the quantity of profiles.  It is possible that cooperation between private and public platforms may be complicated by different sets of govenance principles. Rather than profit and market share, public platforms will also assess the behavior of all partners for reason of reputation and the public good.

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1. Introduction The EU4BT-project is designed to improve the environment for business transfer by using web-based matching platforms. It aims to develop standards related to buyer-seller platforms to ensure the quality of the services provided. Since the turn of the millennium digital matching platforms for business transfers have become more and more sophisticated, including online tooling for preparation, support and finance (European Commission, 2014). So far overviews on academic studies - discussing characteristics and standards to improve success of matching platforms - are not available This report provides an overview of academic research on web-based matching platforms, resembling the properties and characteristics of online SME business transfer matching platforms. Before doing so we will introduce the present state of research on business transfers and will position the importance of matching platforms within this context. The authors like to express their gratitude to Céline Barredy, Associate Professor of Finance and Family Firm Governance (Université Paris Ouest Nanterre La Défense), Miguel Meuleman, Professor of Entrepreneurship (Vlerick Business School), Martijn Westerlaken (Transeo Board Member and Director MKbase) and the executive partners in EU4BT-project Marie Depelssemaker (Transeo), Nicolas Pirotte (Sowaccess) and Oriol Alba (Reempresa) for their valuable remarks and suggestions in the review process.

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2. Business Transfers Research By presenting a short review of nearly 40 studies, we will link the dominant research themes and the missing research on matching platforms. The study of SME business transfers is a fairly recent phenomenon, started by family firms researchers at the turn of this millennium (e.g. Lansberg, 1988, Morris et al., 1997, Le BretonMiller et al., 2004). Interest in non-family transfers was triggered by the massive expected numbers of business transfers (European Commission, 2002) and the increasing empirical evidence that the proportion of family transfers in small and medium-sized enterprises is decreasing (Grant Thornton, 2005; Howorth et al., 2004; Mandl and Voithofer, 2010; Meijaard and Diephuis, 2004; Stone et al., 2004). The main focus of business transfer studies is to predict and/or model success and failure of the ownership transitions and the posttransfer performance of transferred firms (e.g. Cucculelli and Micucci, 2008; Van Teeffelen, 2012). Sellers and buyers are the most prominent actors in business transfers research. Studies of business transfers generally incorporate some notion of predicting which firm owners will opt for a sale or family succession compared to closure or continuation (e.g. DeTienne and Cardon, 2010; DeTienne, McKelvie and Chandler, 2015; Ryan and Power, 2012; Wennberg et al., 2010, Van Teeffelen and Uhlaner, 2013). Studies also look into the process and dynamics of trust building between incumbents and successors (e.g. Howorth et al., 2004; Sharma et al., 2003; Venter et al., 2003; Van Teeffelen et al., 2011). Finally studies look into identification of proper firm acquirers and family successors, also to distinguish takeover entrepreneurs from start-up entrepreneurs (Block et al., 2013; Coopers and Dunkelberg, 1986; Parker and Van Praag, 2012; Van Teeffelen, 2012). Succession and the process of ownership change are frequently frustrated by delay, postponement due to emotional attachment of incumbents (Flören, 2002; Lansberg, 1999; Sharma et al., 2001; Stone et al., 2004). Lately there is a renewed interest in the intangible aspects of business transfer process (e.g. Durst and Gueldenberg, 2010). More specifically researchers look into the process of preparation of successors (Jaskiewicz et al., 2015), gender issues in succession (Constantinidis and Nelson, 2009), models for emotional pricing (Kammerlander, 2014), turn-arounds in business models (Chirico et al. 2012) and the relation between psychological transfer barriers and coping (Weesie and Van Teeffelen, 2015). It is well known that the SME market for business transfers lack transparency (e.g. European Commission, 2006; 2014, Stone et al., 2004). Smaller firms for sale are hardly visible and identifiable in many countries (Van Teeffelen, 2010). Very few studies look into the possibilities of matching platforms, being developed digitally since the early 2000’s (Transeo, 2012; Qutob and Van Teeffelen, 2009). The potential of web-based platforms for business transfers is to augment the number of transactions and lower the thresholds. One could assume there is a lack of interest for SMEs from the buyers side. Alternatively one may think only top performing SMEs can be sold. Both assumptions have empirically shown to be false. 6

Micro firms with employees are as wanted as the larger medium-sized firms and only half of all sold firms seem to perform well (Amaral et al., 2007; Stone et al., 2004; Van Teeffelen, 2010; Wennberg et al., 2010). What could be the reason that online platforms for business transfers are relatively unpopular or are hardly a reflection of the full population of businesses for sale (Qutob and Van Teeffelen, 2009)? The fear for public exposure in a sale is a well know obstacle in small firms transactions (Transeo, 2014; Van Teeffelen, 2010). Yet privacy is a vital element of all web-based matching platforms and transactions (Beldad et al., 2010) and it did not prevent the enormous growth of other types of web-based matching platforms. Perhaps the target group, older small business owners selling off their firms, are not ready to use newer technologies? Also the unfamiliarity and inexperience of firms owners in succession and sale is a recurring theme, which could explain for the reluctance of firm owners to use matching platforms (e.g. Le Breton-Miller, 2004; Transeo, 2014). A Transeo study (2012) mentions the abundance of search indicators, poor quality of the profiles and the appearances of websites as their main weaknesses. Perhaps also the business model of online matching is hardly viable. Asking very low fees without an additional revenue stream, may prevent rapid innovations (Qutob and Van Teeffelen, 2009). However new opportunities arise on the horizon. Economy is gearing up, bringing more firm owners to the market for sale (Van Teeffelen, 2015). Cross national transactions have been reported to grow (European Commission, 2013). In similar areas, like in web-based matching technologies for dating, the opportunities to identify new partners have grown beyond imagination (Finkel et al., 2012). Can we find evidence in recent studies on matching platforms in general to increase trust, to boost services and to generate new business models? In the next section we describe the search procedure conducted, followed by outcomes of academic studies on increasing trust, improve matching and new business models.

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3. Search criteria To dig in the use of matching platforms, an initial search string “matching platforms for SME business transfers” and “selling (SME) companies online” on the academic search engine of EBSCOhost corroborated the scarcity of publications. We found no hits at all. Widening the search criteria by combining terms like “SME”, “micro firms”, “small and medium enterprises”, “matching platforms”, “business brokers”, “business matching” and “web services” provided well over 17.000 hits. In a first attempt to narrow down these publications, looking at their titles, we focused on B2B (double sided) matching platforms, involving buyers, sellers and/or their intermediaries. This eliminated most studies on single sided SME market places, most B2C e-commerce activities of SMEs, commercial and public co-creation platforms, public services platforms on health, education and community building. In reading the abstracts of some 150 studies, three themes evolved. 1. Trust and privacy issues concerning the use of internet 2. A well and thoroughly researched area of online dating 3. Success and limitations of web-based matching platforms In order to share this information with (academic) practitioners, we chose to avoid long introductions and conceptual discussions. We zoomed in on overview publications first, describing findings of previous studies. Most of these publication are published in the period of 2010 to 2012. Topical studies, if available, or publications of a more recent date than the overview studies are also assessed and taken in.

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4. Trust in online matchings platforms Building trust is essential for firm owners in successions or sale (e.g. Le Breton-Miller et al. 2004, Venter el al., 2003; Van Teeffelen, Uhlaner and Driessen, 2011), even if profiles appear anonymous on web-based matching platforms. The publication “How shall I trust the faceless and intangible” of Beldad et al. (2010) reviews a period of fourty years of trust publications. They particularly pay attention to 24 empirical studies on online trust performed in the last decade. We added two more publications (Benedicktus, 2012; Eastlick et al., 2006) to our review. Beldad et al. (2010) differentiate between three types of trust: the individual experience and tendency to trust (client-based trust), expectations on ease of use, website reliability and security (web-based trust) and trust by reputation of those organizations behind the websites (organization-based trust). Different types of trust Trust as in individual feature (client-based trust) Some people are more trusting than others. Trust in this case is defined as the tendency to believe in the trustworthiness of others (Das & Teng, 2004). People can vary in their trust propensity towards others from high to low (Rotter,1980). Trust can be attributed to (developmental) experiences, personality type and cultural background (Mayer et al., 1995). An important implication of this definition of trust is that there are limited possibilities to influence online trust, since prior experiences, personality traits and cultural background are more or less given. This definition also takes into account that cultural backgrounds may lead to differences in online trust formation. Trust as an expectation, decreasing vulnerability or risk (web-based trust) Trust can alternatively be seen as the expectation regarding the behavior of other people. It is needed in social interactions were behavior of others can not be controlled. Trust is also a neccesity in situations where there is less information than required to be assured of success (Luhmann, 1979). This touches on the information assymetries met in SME business transfer situation between buyer and sellers. Sztompka (1999) defines a set of straight forward criteria to build trust in general: reputation, performance, and appearance. Reputation refers to a record of past deeds. Performance includes present conduct, and currently obtained results. Appearances are influenced by one’s looks and self-presentation. Trust as an expectation is a dynamic approach. It implies that trust can be increased, maintained or lost in the process by events. Clear and easy access web-services, security and privacy policies, certification and third party guarantuees and/or testimonials are subjects which may enhance online trust (Beldad et al., 2010).

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Trust as an institutional phenomenon (organization-based trust) Rather than an individual feature or an expectation in social interaction, Lewis and Weigert (1985) define trust as a sociological term being necessary for collective units to act. Groups, organizations, and institutions must work together and have to rely on each other for effective cooperation. Trust is also vital for economic exchange. In so trust is the expectation that groups of people will not be exploited by institutions (James, 2002). Empirical findings Beldad et al. (2010) discuss the empirical findings on online trust formation, observing that only few studies have taken in all these three perspectives of trust in their design. Disparities in some of the results imply that the effects of online trustworthiness are contextual. Nonetheless we like to present some of the hightlights of findings in a predominent commercial setting. Online trustbuilding seems a necessity for online transactions, since unlike offline transactions the risk of losing one’s money to unknow parties during the exchange is far greater. Also the threat of having one’s private sphere in the open, being distributed instantly and world wide is a definite point of concern. Individual trust characteristics Empirical studies are inconclusive on the importance of the trust as an individual feature. Some studies show effects on online trust formation, others not or yet others only moderating effects (Baldad et al. 2010). Website trust characteristics The impact of perceived ease of use on the formation of trust in e-commerce is supported by several studies (Bart, Shankar, Sultan, & Urban, 2005; Chen, 2006; Flavian, Guinaliu, & Gurrea, 2006; Koufaris & Hampton-Sosa, 2004). Also the content quality of an e-vendor’s website, referring to the usefulness, accuracy, and completeness of the information offered may increase customers’ trust in online transactions (Liao, Palvia, and Lin, 2006). There are inconclusive effects of photo’s of employees and owners on websites on trust formation. Some studies reveal more trust other indicate raising suspicion (Baldad et al, 2010). There is also no evidence for increasing trust by customization of services, due to a shortage of empirical studies. Looking at privacy policies, only one survey (Lauer & Deng, 2007) shows that the introduction of stronger privacy policies results in a higher trustworthiness. All other studies (Arcand, Nantel, Arles-Dufour, & Vincent, 2007; Jensen, Potts, & Jensen, 2005; Vu et al., 2007), show that most users do not read online privacy statements before disclosing their personal data.

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Third-party guarantuees seem to be more effective to raise online trust, especially in the initial encounter (Koehn, 2003). Third-party recognitions – in the form of seals of approval – are effective in promoting customers’ trust in online shopping. Seals of approval also endorse the privacy and security policies of electronic vendors (Cheung & Lee, 2006). Stewart (2003) suggests that trust can be transferred from other contexts to a trust target. Transference can take place when an online party associates itself with another firm using hyperlinks, such as linking to other website to create a perceived relationship with a more trusted target. A more recent experimental study of Benedicktus (2011), not mentioned by Baldad et al. (2010), assigns customers randomly to high or low rated consumer goods platforms. The satisfaction scores on the online sale is clearly visible and provided by an independent leading trusted third party platform, increasing or decreasing over a period of three months. Benedicktus (2011) first shows a profound distinction in customers trust between webfirms with excellent (high trust) and poor (low trust) third party scores. More importantly he shows that trust is a dynamic process. Even firms within the highest consensus ratings - upper limit 98%, lower limit 91% customer satisfaction – showing a drop towards the lower limit of 91%, lost trust of their customers. Inversely Benedicktus (2010) shows that firms within the lowest consensus ratings - lower limit 68%, upper limit 76% customer satisfaction - are able to improve trust perceptions considerably if they move up to their upper limit of 76%. Organizational characteristics and reputation Eastlick et al. (2006), also a study not mentioned by Baldad et al. (2010), shows that the relationship of firm reputation and trust is one of the strongest relationships validated by their investigation. They find that a strong firm's reputation is an effective agent to ease respondents' concerns about sharing personal information. Results of a number of empirical studies reveal that the positive reputation of an e-vendor (Chen, 2006; McKnight et al., 2002; Teo & Liu, 2007) and word-of-mouth within one’s social network, particularly positive referrals, increase trust (Kuan & Bock, 2007). Several other empirical studies show that customer satisfaction with a previous online transactions determines their trust of that company (Casalo et al., 2007; Flavian et al., 2006; Yoon, 2002). Previous online transactions also induce greater usage and familiarity (Yoon, 2002). Additionally Gefen’s (2000) shows that familiarity by itself significantly influences online trust resulting in higher intentions to buy online. Kuan and Bock (2007) reveal that offline presence enhances the customer’s online trust, although these outcomes were not corroborated by Teo and Liu (2007), possibly caused by a difference in context. Along this line of reasoning it can be pressumed that offline presence is to promote online trustworthiness (Baldad et al., 2010). These outcomes closely ressemble outcomes on success factors in ownership transfers, which seem to be dependent on prior experience with selling of buying firms (Van Teeffelen and Uhlaner, 2013) and the importance of familiarity rather then kinship between buyer and seller (Van Teeffelen, Uhlaner and Driessen, 2011).

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Inferences for business transfers matching platforms Web and organizational characteristics have predominantly shown to increase online trust. Appearances are influenced by one’s web-looks presentation, most importantly the easiness to use and select profiles and the accuracy, also think on the date of (re)placement.Though findings are context specific so far we may conclude that Sztompka’s (1999) set of criteria to build trust are confirmed: reputation, performance and appearance. Reputation refers to a record of past deeds and increases trust strongly. Performance includes present conduct, and currently obtained results. So far most matching platforms in business transfers do not show success rate of their selling or buying profiles. A track record of matching platforms, preferably by third party recognitions, is known to enhance online trust dynamically both as part of reputation and currently obtained results. Additional services and offline presence of matching platforms, might be of importance in the longer run, since they will generate more familairity of users, word-of- mouth promotion and a variety of services to be assessed by customers. If these additional services lead to satisfaction, trust will be augmented and distributed further in social and online networks.

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5. Online Dating SME owners selling or selecting successors are known to be picky (Venter et al., 2003; Le Breton-Miller, 2004; Meijaard et al., 2005; Van Teeffelen, Uhlaner and Driessen, 2011), taking in several considerations and a multitude of criteria. Partner choice in online dating resembles this process. Which lessons can be drawn from an overview of publications on online dating research? Finkel et al. (2012) comprehensively describe how online dating changed partner search the past two decades, using three main themes: access, communication and the matching process. Their overview publication is complemented with a study of Akehurst et al. (2012). Access and choice overload Matching platforms on dating have given unprecedented opportunities for individuals to contact partners far beyond their own social networks. Matching platforms, by the sheer number of users and available profiles, open up to unexpected partners never being thought of or sought by earlier. The availability of partners is somewhat mitigated by the emphasis on profiles, which fail to capture the essence of a person (Finkel et al., 2012). The downside of the large number of profiles available is in undermining people’s ability to make decisions. Rather than to make a decision, they tend to avoid making one. A recent meta-analytical review regarding choice overload and paralysis is provided by Scheibehenne, Greifeneder, & Todd (2010). A second possible consequence of offering users access to large rather than small choice sets is it undermines users’ willingness to commit to any party. Research demonstrates clearly that appealing alternatives strongly predicts low commitment and a break-up (for metaanalytic reviews, see Le & Agnew, 2003 and Le, Dove, Agnew, Korn, & Mutso, 2010). Finally a consequence of large choice sets leads to decisions that differ from originally stated preferences. Participants in studies seem to employ relatively careless cognitive strategies under circumstances of large choice sets. Chiou & Yang (2010), Wu & Chiou (2009) and Yang & Chiou (2010) show that participants browsing smaller choice sets (30 to 40) of profiles diverge less from their stated preference than participants browsing larger sets (8090). Additionally Wu & Chiou (2009) show by their data that the larger the choice set, the more time participants of the study spent on profiles with a poor match to their initial preferences. Participants in larger choice sets spend relatively little time on congruent partners matching their initial preferences. In sum rather fewer than a larger set of choice options seem beneficial for matching. Focus may be lost in large sets of profiles, leading to poorly matched partners, relative to their initial preferences and goals.

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Communication Using Computer Mediated Communications (CMC) - like skyping, e-mail, chat, webcam conversations - potential partners on matching platforms can overcome the initial superficialities of the dating profiles. The closer the approximation to live interaction, the more useful CMC. However, if the time between the initial CMC interaction and the initial face-to-face interaction is too long (6 weeks in the research of Ramirez & Zhang, 2007), CMC fails to provide the additional boost. Nothing seems more able for a reality check than a faceto-face meeting, since people are far less likely to misrepresent attributes in face-to-face interactions than by other ways of digital information or communication (Finkel et al. 2012). None of the business transfer matching platforms to our present state of knowledge provide CMC-services where buyers and sellers can contact each other directly. CMC generally provides means to ignore unwanted request. Using a scripted “no thanks” messages or ignoring requests is not considered to be offensive by participants (Tong & Walther, 2011). The range of CMC modes may vary widely from e-mail-like messages, instant messaging, webcam conversations to even avatar meetings. Users who have concerns about privacy, seek for additional information about potential interested partners before consenting in CMC (Gibbs, Ellison, & Lai, 2011). Most dyads (pairs of persons) that start to use CMC, meet within a week to a month (Rosen et al., 2008; Whitty, 2008), using their own means of communication to set the meeting (Day, Hamilton, Hutchins, Maher, & Vance, 2010), rather than the platform offered modes of CMC. No matching algorithms can predict success Finkel et al. (2012, p.48 ) summarize the effectiveness of matching algorithms based on characteristics of parties in a few sentences: “Many of the strongest established predictors of romantic outcomes emerge only from the interactions between two people or from the way they respond to unpredictable and uncontrollable events that have not yet happened. Consequently, the best-established predictors of how a romantic relationship will develop can be known only after the relationship begins. The requirement that Internet match making be based solely on qualities of individuals that can be known prior to their awareness of each other excludes these variables from consideration in matching algorithms ……” Basically Finkel et al. (2012) stress the importance on real life interactions to access success in matching. Also another phenomenon seems to interfer in predicting success in maching by using desired characteristics. The study of Akehurst et al. (2012) shows that explicit preferences predict low levels of success to engage in further contact. On a basis of 115.000 contact proposal, only 39% can be successfully predicted. 61% of all matches deviate from the user’s explicit contact preferences. Looking at past selection patterns of parties, ruled by implict preferences, these preferences were able to improve predictions of successful contact up to 89%. By and large partner search is far less guided by explicit preferences than 14

generally thought off. Akehurst et al. (2012, p. 27) finish their study with the notion that: “Users can benefit from a suitable presentation of their implicit preferences; they can compare the implicit and explicit preferences and adjust the explicit preferences accordingly.” In the field of business transfers this means that challenging preferred characteristics of buyers, successors or looked for firms may increase success in matching initial contacts. Inferences for business transfers matching platforms Some of the findings from dating research can be very useful to increase success in business transfer matching tehchnologies. Asssessing buyers and sellers implicit preferences, either digitally or by an interview, might increase successful matching. Rather than presenting an infinite numbers of profiles, leading to choice stress and less commitment of buyers and sellers, sophisticated search procedure and search engine should be designed to produce a limited number of options. The less initial outcomes generated for a first assessment, the more likely the first contacts will be made. An overload of propositions will tempt buyers and sellers to postpone their choice. CMC-services with mutual consent, like skype, sms or e-mail contact, may overcome the superficiality of profiles available of buying and selling parties. The closer to real life contact, the more helpful CMC shows to be, since it speeds up first real life meetings.

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6. Success of web-based matching platforms We found few publications on success of matching platforms in the B2B market. Lacking overview publications, we performed an additional search with Google Scholar. We used the entrances SME, B2B, matching platforms, success, failure. We found no hits in using all search terms and looked into the the combinations of SME/B2B and SME/matching platforms mainly. In all we read 50 abstract and found but 5 related papers linking on survival, success, governance and cooperation between B2B platforms, not yet covered by mentioned studies in the previous chapters. The study of Kaufmann and Wang (2008) follows 130 listed web platforms during 10 years. Those platforms generated 95% of their revenues out of web business. Their study shows that double sided brokerage platforms show best survival of all. Generally web platforms engaging bringing parties together, facilitating their transactions and having advertisements as primary revenue source survive best. Also smaller internet firms have a better survival rate than larger internet firms. The first 4 years are most critical for survival. About half survive. After six years survival does not change any more and stays at a near 100%. The theoretical paper of Chelariu and Sangtani (2009) stresses different governance principles for matching platforms. The three archetypical types of matching platforms are “Independent exchanges”, “Consortia” en “Private Exchanges”.

Each have different governance mechanisms. Matching platforms for business transfers if private and having a brokerage function can be classified as an Indepent Exchange platform. Their guiding principle is competition and price, they connect many to many and thrive by spot transactions. Their governance is mainly by technology, limited monitoring and screening, output-based and mainly confined by legal systems. However public-private matching platforms for business transfers are other types of platform, classified as a Consortia platform. Their guiding principle also include collaboration. Reputation rather than price alone is their guiding principle. Their governance will be different, also incorporating values and goal alignment, besides technology. They will not only monitor outcomes but also the behavior of all partners. Rather than a legal framework, they also can apply collaborative 16

sanctions, as reputation at stake, when parties show unwanted for behavior. Since Private Exchanges platforms only connect one to few, we do not consider their governance. The study of Li en Penard (2011) investigates the importance of quality and quantity on one worlds most prominent B2B digital marketplaces, both active in the USA and China, covering transactions between 2004 and 2010 for 31 categories of products. Their results show in the early stages quantity of suppliers is vital. But when the critical mass of transactions has been reached, quality rather than quantity of the suppliers is more important in a mature stage of the B2B platform. The managerial implication of this study is that the competitive advantage of a platform relies on finding the optimal mix of quantitative and qualitative network. Like (Zhu and Iansiti, 2012) they find that in each stage of its development this mix differs. Focusing only on the number of members, without monitoring quality in the long run can be detrimental to buyers' trust in the marketplace. An even more interesting study is that of Mantena and Saha (2012) of multisided markets, bringing in generally buyers and sellers, with strong and weaker market players. They focus on matching platforms that grants free access for one party and a (membership) fee for others. Though small differences in (perceived) platform technologies may result in large differences in profitability, this does necessarily interferes with collaboration. Mantena and Saha (2012) find that dominant firms are open to collaboration with rivals as long as they are secure in their leadership position. Collaboration is most profitable when it takes the form of direct network interconnection, as long platforms differ in level technology and their customer base. Sharing customers will add value to both platforms. In a highly competettive market, upgrading technology it is not profitable for low-tech platforms, since they will become an average player, being more similar to the leading and dominant platforms. This blocks cooperation and eventually increases competition. Inferences for business transfers matching platforms We have to be prudent with conclusions since overview studies on the topic are missing. Rather than proof, we may use the outcomes of the five studies as possible guidelines for a further development of matching platforms. First results indicate that brokerage and facilitating transactions seem to enhance survival of platforms. In the first period of business transfers platforms the quantity of profiles increases transactions, but at a certain point the quality of the portfolio prevails in increasing transactions. It is possible that different governance rulings may complicate cooperation between private and public platforms. Where competition and price are important governance principals for private platforms, a multistakeholder approach prevails for public-private platforms. Rather than profit only, public platforms will also assess the behavior of their consortium partners for reason of reputation and the public good. The development of dominant parties in the market does not have to interfere with collaboration. Two conditions seem to favor collaboration: the less similar the platforms, the most likely collaboration and the more dominant a platform, being secure of leadership, the more likely collaboration with rivals. Direct network connections seem to be most profitable. 17

It seems that technological upgrading of low-tech platforms is not favorable in a highly competetive market.

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