TECHNOLOGY FORESIGHT: A bottom-up perspective

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A bottom-up perspective

Master Thesis

Faculty of Economics and Business MSc Business Administration Specialization: Business & ICT

June, 2013

GUIDO KLEIN WOLTERINK

Student number: 1629662 Nieuwe Ebbingestraat 31a

9712 ND, Groningen Tel: +31616006400

E-mail: g.klein.wolterink@student.rug.nl

Supervisor: DongBack Seo

University of Groningen Nettelbosje 2, Groningen,

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Abstract

With the rapid need for new and better technologies, companies try to find ways to boost innovation for their organization. This is often done in a top-down manner, e.g. through Research and Development to create corporate technological foresight. However, there has been little to no research done on the other way around; bottom-up technology foresight. In this research, I will look at what factors influence bottom-up technology foresight and how bottom-up foresight affects the creation of weak signals. Through a literature review, several constructs were found and a model was derived from similar, grounded literature. These constructs were tested in a survey and prove to be significant. Bottom-up foresight has a positive effect on creating weak signals, and the results show that people who are engaged in foresight, often share this online on web forums, or with colleagues, friends and family. Managers can use this information to stimulate bottom-up foresight, and therefore the creation of weak signals.

Key words: technological foresight, individual foresight, bottom-up innovation, weak signals

TECHNOLOGY FORESIGHT:

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The importance of innovation for businesses is nothing new; some of the most influential and well-known classic articles ever written stress the concern of innovating. Christensen’s (2000) article on disruptive change, Porter’s (1979) competitive forces, Kanter’s (1982) middle manager as innovator; they all share the idea that in order for a company to survive, it has to incorporate innovation in its business. According to Ansoff (1975), firms can achieve that by focusing on the ‘weak signals’, in contrast to conventional strategic planning that depends on strong signals. Weak signals are defined as the early warnings of change, which will become stronger over time when combined with other signals (Kuusi, Hiltunen & Linturi, 2000). If a company’s capability to pick up weak signals is well developed, then it has a strong competence that can be an advantage with respect to the competition. Detecting weak signals is therefore a good source of innovation. Because of major developments in information technology, these signals are not only visible to specialized research and development departments anymore; employees from all levels have access to the internet, where they can search, find, and share ideas about future technologies. These developments in information technology enable anyone with a computer to write, create and distribute information, accessible to anyone. This means that weak signals are not necessarily created by companies, but can originate from individuals—whether or not they work for a company. Aguilar (1967) described that weak signals can be detected by scanning the organizational environment. By engaging in ‘environmental scanning’, firms systematically scan the environment for signals. Through the years, this has been used as an effective tool for organizational strategy to assess the environment (Andersson & Bateman, 2000; Daft, Sormunen & Parks, 1988; Hambrick, 1982; Sutcliffe, 1994). However, scanning for weak signals also serves another purpose: forecasting the future for new technologies. This is also known as a firm’s ‘foresight’ or ‘forecast’ capability. Although these terms have been used as interchangeable concepts, in this research I will refer to foresight1.

In the last several decades, interest in foresight has been rapidly increasing. In as early as the 1960s, many authors have described the notion of technological forecasting (Cetron, Happel, Hodgson, McKenney & Monaghan, 1966; Jantsch, 1967; Roberts, 1969; Linstone, 1969;

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Porter et al. (2004) define technology foresight as “a systematic process to identify future technology developments and their interactions with society and the environment for the purpose of guiding actions designed to produce a more desirable future” and technology forecasting as “the systematic process of describing the emergence, performance, features, or impacts of a technology at some time in the future”.

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Martino, 1969). It is no surprise that in 1969, a journal was created solely around this topic; Technological Forecasting (which is the predecessor of the popular academic journal Technological Forecasting & Social Change). Since then, academics have been writing papers for this journal to be published in biannual issues (which now sometimes go up to nine issues per year), which means that there is a very large theoretical base covering this subject today.

Throughout the years, there have been some popular papers about technology foresight. Grupp and Linstone (1999) for instance evaluated several (finished and ongoing) technological foresight activities around the world, their focus being the application of the Delphi technique. Martin and Johnston (1999) also wrote a paper evaluating different technological foresight activities, but situated in the United Kingdom, Australia and New Zealand. Other popular papers that were written later (Phaal, Farrukh & Probert, 2004; Porter et al., 2004) were also evaluations of existing technological foresight techniques.

Now, with the internet enabling capabilities like online collaboration, sharing of ideas, consumers becoming producers, and just generally the introduction of ‘Web 2.0’2, there has been a shift in where innovation/idea creating is coming from. Typically research about technological foresight has been done in a top-down approach, e.g. by research and development departments. Given the aforementioned trends, it seems like the shift is moving towards bottom-up innovation; e.g. coming from employees, or the grassroots.

Despite the ever-growing interest in technological foresight, there is a gap in the literature on bottom-up foresight. With this thesis, I aim to partially fill in this gap by conducting an empirical research about bottom-up technology foresight. The research is organized around two research questions: (1) What factors affect bottom-up technology foresight? and (2) How does bottom-up technology foresight affect the creation of weak signals? First, the literature gap will be defined by a literature review, where the research questions will be addressed. Following that, I will develop a conceptual model for the research questions. To gain empirical data, I will be conducting surveys and test my research constructs. The results and their interpretations will be concluding this thesis.

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Web 2.0 was coined as a term in 1999 to describe websites that use technology beyond the static pages of earlier websites. Social networking, blogs, wikis, video sharing sites are examples of Web 2.0 sites.

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2. LITERATURE REVIEW

In this literature review, I am going to investigate what the current state on technology foresight is in the business literature to show the body of literature on top-down technology foresight. After that, the current state of bottom-up technology foresight is investigated to show the literary gap. The next step is a comparison of the business literature with the social sciences literature to search for similarities that can be used to form a model for bottom-up technology foresight.

2.1 Current state on technology foresight literature

This section is about finding the current literary state on technology foresight literature. Ever since the 1960s, technological foresight (then: forecasting) has been an important subject. In 1969, the journal Technological Forecasting was founded as a result from this growing increasing popularity. This interest came to a pinnacle in 1999, when the journal “Technological Forecasting & Social Change” published several very popular papers in one issue (Martin & Johnston, 1999; Grupp & Linstone, 1999; Kuwahara, 1999; Blind, Cuhls & Grupp, 1999). In the literature review, these four were amongst the most cited papers, all of which reported about top-down technological foresight activities: scenarios (Martin & Jonhston., 1999; Blind et al., 1999), expert techniques (Kuwahara, 1999; Grupp & Linstone, 1999; Blind et al., 1999; Martin & Johnston, 1999). In 2004, Porter et al. published a paper that has the most comprehensive framework to this day. Every method and technique that has been used or mentioned in the literature is in that framework; field anomaly relaxation method (a scenario analysis technique), long wave analysis (a trend analysis technique), Delphi (an expert technique), and many more (Porter et al., 2004).

The techniques mentioned in the paper by Porter et al. (2004) are often mentioned in the literature. The list greatly exceeds, but confirms, the findings of the literature review: all the methods mentioned in the article are top-down. Examples of techiques are: trend analysis (Roberts, 1969; Sharif & Kabir, 1976; Sharif & Haq, 1979; Cunningham & Kwakkel, 2011), scenarios (Martin & Johnston, 1999; Coates et al., 2001; Banuls & Salmeron, 2007; Bezold, 2010), expert techniques (Coenen, 1971; Dietz, 1987; Cho, Jeong & Kim, 1991; Kuwahara,

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1999; Saritas & Oner, 2004; Chen, Wakeland & Yu, 2012), and roadmapping (Kappel, 2001; Saritas et al., 2004; and Phaal, Farrukh & Probert, 2004) for example. Trend analysis uses historical data to make predictions of the future (e.g. by extrapolating a value against time). With scenario planning, several alternative scenarios or descriptions of possible futures are predicted. Expert opinion methods pool the views of well-informed individuals (Dietz, 1987). Technology roadmapping provides an organized resource for exploring and interacting the relationships between evolving and developing markets, products and technologies over time (Phaal et al., 2004). These approaches are top-down in the sense that all ideas and input come from the top, e.g. the R&D department.

The journals included in the literature review are: Long Range Planning, Academy of Management Journal, Journal of Product Innovation Management, MIT Sloan Management Review, MIS Quarterly, Research Policy, Technological Forecasting and Social Change, and Technovation (other journals have been selected, but did not return any results). The literature review was done by selecting only top and very good journals (as indicated on the site of the University of Groningen3) and searching through their archives for foresight (i.e. ‘technological/technology foresight’, ‘technological/ technology forecast(ing)’). Every relevant paper has been scanned, documented and chronologically listed. There were over sixty different papers documented (see the documentation at Appendix A).

2.2 Current state on bottom-up technology foresight literature

The next step is to determine what the current state of the literature on bottom-up approaches (i.e. input and ideas coming from lower organizational levels, e.g. from the work floor or employees) is in the same top journals. One way to do this is by searching for several terms that describe bottom-up approaches. Ten terms (e.g. ‘crowdsourcing’, ‘grassroots’ and ‘co-creation’) were carefully selected and used in the search engines that look through the top journals. The same method was used as the previous literature review; looking for relevant papers, scan through and document them. Although the searches returned a large list of papers about bottom-up innovation approaches; almost none of them mentioned anything about foresight. One example is crowdsourcing: user generated innovation, or the move of an organization outsourcing functional

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http://www.rug.nl/research/gradschool-economics-and-business/organization/criteria/top-very-good-journals TECHNOLOGY FORESIGHT:

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work that is normally performed by employees of the company, now being done by making an open call to an undefined (and normally big) network of people (Afuah & Tucci, 2012; Feller, Finnegan, Hayes & O’Reilly, 2012; Howe, 2008). Another example is the notion of the prosumer: “consumers who become actively involved in production of goods and services for their own consumption” (Flowers, 2008; Franke & Piller, 2004; Shim & Lee, 2009). Bottom-up innovation is related to foresight; however it is for the near-sighted future (e.g. product innovation), whereas technological foresight is meant for making statements about the longer-term future (Banuls & Salmeron, 2007; Kuwahara, 1999; Shin, 1998). Crowdsourcing is an example of near-sighted future because people work together to solve a (often small) problem. Another difference is that bottom-up innovations mostly are solutions to problems, whereas technological foresight looks at the future. This part of the literary research has shown that there is a gap in the business literature on bottom-up foresight.

2.3 Business literature versus social sciences literature

So far, we have seen that there is literature written about a bottom-up approach for innovation in the business literature. However, there is no literature written about a bottom-up approach to foresight; therefore, I will look in other areas for this subject. One science area that has covered a lot about bottom-up approaches is the social science area (or sociology). The subject of grassroots is described as “involving the common people as constituting a fundamental political and economic group” (source: Wordnet), which covers the area of interest well. That is why I will compare the grassroots in the sociology literature to the grassroots in the business literature. This is done by comparing three important parts of grassroots: the actors (i.e. people involved in the grassroots), their characteristics and the factors that influenced the grassroots. These three elements will show similarities between the different sciences.

Grassroots is a term that is widely known in sociology and the public policy area, and is often referenced in the context of a political movement. Seyfang & Smith (2007) describe grassroots innovation as follows: “innovative networks of activists and organizations that lead bottom-up solutions for sustainable development; solutions that respond to the local situation and the interests and values of the communities involved”. To determine the current state of literature on

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grassroots in the sociology and public policy area, another literature review was done. To determine the top journals in these areas, Thomson & Reuters's Journal Citation Reports database was searched for, and presented the top twenty journals (based on their 5-year Impact Factor). The sociology journal list includes: Annual Review of Sociology, American Sociological Review, European Sociological Review and British Journal of Sociology. The public policy journal list includes: Journal of Public Administration Research and Theory, Public Administration, Environment and Planning C and Policy Studies Journal. The object of this review is to identify the state-of-the-art of grassroots literature and to see if there is any methodology that can be used for technological forecasting. As seen in the previous paragraphs, there is a lack of research on a bottom-up foresight approach in the business literature. With this review, I am hoping to find a well-grounded methodology or framework which can be used to relate grassroots methods to bottom-up foresight. Because the term grassroots is very similar to bottom-up approaches, this will be sought for in the sociology literature.

The review resulted in eighteen papers, most of them using the term merely as a reference to public communities (Goode & Ben-Yehuda, 1994; Stall & Stoecker, 1998; Kirkpatrick, 2007) or employee democracy/participation (Rothschild & Russell, 1986; Akard, 1992), but mostly a way to describe lower level (citizens, employees or activists) political/social movements (Goode & Ben-Yehuda., 1994; Rothschild & Russel, 1986; Evans & Kay, 2008; Staggenborg, 1988). However, there were a few important similarities in both sides (business and sociology) of literature, which will be discussed in the next paragraphs.

To understand the similarities and differences between the grassroots in the social science literature and the grassroots in the business literature, it is important to look at the actors involved, the characteristics and the factors that influence the grassroots. This makes it easier to compare the approaches from the different literature areas. This will also provide an argument of why grassroots in the sociology is similar to the grassroots in the business literature.

2.3.1 Actors

It is important to understand who the grassroots consists of. In that way, you know for sure who you are talking about. The actors from the sociology literature have several matches with the actors found in the business literature. In his often cited paper, Akard (1992) describes

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employees, customers, suppliers, stockholders and the local Chambers of Commerce as grassroots actors. In the business literature, employees (Afuah & Tucci, 2012; Bhattacherjee, 1998; Dos Santos & Spann, 2011; Gloor & Cooper, 2007; Lee, Rho, Kim & Jun, 2007; Morgan & Wang, 2010; Nijhof, Krabbendam & Looise, 2002; Obstfeld, 2005; Soukhoroukova, Spann & Skiera, 2012 and Van Dijk & Van Den Ende, 2002), customers (Gloor & Cooper, 2007; Mahr & Lievens, 2012; Ngo & Cass, 2012; Thomke & Von Hippel, 2002 and Wagner & Majchrzak, 2006), and suppliers (Ramaswamy & Gouillart, 2010) were also found as grassroots actors. The actors described in the sociological papers of Evans and Kay (2008); Goode and Ben-Yehuda (1994); and Bray (2006), were also mentioned in the paper by Afuah and Tucci (2012), namely; the public. In Staggenborg’s (1988) paper about the pro-choice movement, and Wollebæk’s (2010) paper about rural associations, the actors consisted of volunteers and voluntary associations, which is also the case in the paper by Monaghan (2009) from the business literature, where voluntary organizations played a role in grassroots innovations. See table 1 for an overview.

TABLE 1

Actors mentioned in the sociology and business literature

SOCIOLOGY ACTOR BUSINESS

Akard, 1992

Employees

Afuah & Tucci, 2012 Bhattacherjee, 1998 Dos Santos & Spann, 2011 Gloor & Cooper, 2007 Lee, Rho, Kim & Jun, 2007 Morgan & Wang, 2010 Nijhof, Krabbendam & Looise, 2002 Obstfeld, 2005 Soukhoroukova, Spann & Skiera, 2012 Van Dijk & Van Den Ende, 2002 Akard, 1992

Customers

Gloor & Cooper, 2007 Mahr & Lievens, 2012 Ngo & Cass, 2012 Thomke & Von Hippel, 2002 Wagner & Majchrzak, 2006

Akard, 1992 Suppliers Ramaswamy & Gouillart, 2010

Evans and Kay, 2008

Goode and Ben-Yehuda, 1994 Bray, 2006

The public

Afuah and Tucci, 2012

Staggenborg, 1988 Wollebæk, 2010 Volunteers / Voluntary organizations Monaghan, 2009 TECHNOLOGY FORESIGHT:

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2.3.2 Characteristics

Although the actors of the grassroots on both sciences are similar; if they don’t possess similar characteristics, you’re still comparing two different things. That is the reason why characteristics are being compared.

Communities are mentioned a lot in the business literature on grassroots characteristics (Burger-Helmchen & Cohendet, 2011; Cachia, Compañó & Da Costa, 2007; Dahlander & Wallin, 2006; Franke & Shah, 2003; Fredberg, 2009; Füller, Jawecki & Mühlbacher, 2007; Hienerth & Lettl, 2011; Monaghan, 2009; and Peredo & Chrisman, 2006). Peredo and Chrisman (2006) mention a community that is acting corporately as both entrepreneur and enterprise in pursuit of the common goal. In 2007, Fuller et al. mention that members of selected online basketball communities seem to be motivated to contribute to the community to share their own designs, by inner satisfaction. In 2010, Füller confirms this in another paper, stating that members of a community were intrinsically interested participants. Staggenborg (1988) and Penner, Dovido, Piliavin & Schroeder (2005) state that communities or voluntary organizations have expectations of greater effectiveness (compared to “regular” employees at an organization). Mahr & Lievens (2012), Franke & Shah (2003), and Hienerth & Lettl (2011), who state that lead-users in communities also have higher expectations of effectiveness, share this view.

Instead of describing each single characteristic separately, two types of communities will be compared side by side; grassroots, associations and movements on one side (sociology) and the innovating, creative communities on the other side (business); see table 2.

TABLE 2

Comparison of communities from sociology and business literature

SOCIOLOGY CHARACTERISTIC BUSINESS

Goode & Ben-Yehuda, 1994 Staggenborg, 1988

Wollebæk, 2010

Intrinsically motivated

Füller, Jawecki & Mühlbacher, 2007 Füller, 2010 Greaves, 2004

Wollebæk, 2010

Goode & Ben-Yehuda, 1994

Shared goal

Peredo & Chrisman, 2006 Hienerth & Lettl, 2011 Abele, 2011 Staggenborg, 1988

Penner, Dovido, Piliavin & Schroeder, 2005

Expectations of greater effectiveness

Mahr & Lievens, 2012 Franke & Shah, 2003 Hienerth & Lettl, 2011

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Staggenborg, 1988 Wollebæk, 2010 Penner et al., 2005 Voluntary Monaghan, 2009 Fredberg, 2009 Füller et al., 2007

Although ‘volunteers’ were mentioned before as grassroots actors, they are also worth mentioning as a characteristic. Characteristics are often subject to many different views, and the relation between a characteristic and an object that it describes is never a one-to-one relation. Therefore we need a definition of ‘volunteerism’ to help us describe the difference between volunteering as an actor and as a characteristic. In a comprehensive and often-cited study published in the Annual Review of Sociology, Wilson (2000) described volunteering as “any activity in which time is given freely to benefit another person, group or cause”. In another article about voluntary actions and groups, Smith (1975) defines voluntary action as “the action of individuals, collectivities, or settlements insofar as it is characterized primarily by the seeking of psychological benefits (e.g. belongingness, esteem, self-actualization) and by being discretionary in nature (not determined primarily by biosocial factors, coercive factors, or direct remuneration; direct, high-probability payment or benefits of an economic sort)”. Briefly: people that engage in voluntary activities (work in community service) commit time for the benefit of another, whether that is a person, group or cause (Wilson, 2000), to seek psychological benefits (belongingness, esteem, self-actualization) and are not motivated by some kind of compensation (Smith, 1975). The Merriam-Webster dictionary describes volunteerism as “the act or practice of doing volunteer work in community service”. Like Merriam-Webster’s definition states: volunteering in general is often linked to community service, which is basically a donated service or activity that is performed by someone or a group of people for the benefit of the public or its institutions, however, performing community service is not the same as volunteering, since it is not always done voluntarily (Wikipedia). The Dutch equivalent of the Merriam-Webster dictionary, the Van Dale, defines volunteering as “voluntarily performed, unpaid work”. In a paper about pro-social behavior, Penner, Dovido, Piliavin and Schroeder (2005) mention that “(…) the relationship between economic status and volunteering (…) may involve other factors, such as more awareness of the problems of others, greater empathy for their distress, and an expectation of greater effectiveness”. Especially the last part of this quote is interesting, as it links volunteering to lead-user characteristics. In Staggenborg’s (1988) paper, he describes volunteers as “cause-oriented members who have higher expectations about what can be done and expect more out of

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people and projects”. This characteristic also describes the so-called lead-user theory, stating that lead-users are ahead of the majority of users in their populations with respect to an important market trend, and they expect to gain relatively high benefits from a solution to the needs they have encountered there (Urban & Von Hippel, 1988). Lead-users were mentioned in the business literature as grassroots actors by Mahr and Lievens (2012); Franke and Shah (2003); and Hienerth and Lettl (2011). Lead-users often come forward in the context of a group or community (which is represented by Mahr and Lievens, 2012; Franke and Shah, 2003; and Hienerth and Lettl, 2011; in table 2). Together with the paper of Penner et al. (2005), this shows several similarities between the characteristics from the different types of grassroots.

2.3.3 Factors

Several factors influenced the social grassroots mobilizations and impacts. The most mentioned factor is the ‘sentiment’. In Akard’s (1992) paper, “defections by moderate Democrats in the House were caused by a perception of changing public sentiment”. Staggenborg (1988) wrote that “entrepreneurs can mobilize sentiments into movement organizations without the benefit of precipitating events (…) and without established constituencies”. Goode and Ben-Yehuda (1994) mention in their paper that “a sentiment needs a vehicle (e.g. media, politics) to elevate a latent fear or concern into widespread, mutual awareness”. Greaves (2004) noted that policymakers in Chile created mechanisms for grassroots organizations to participate in local governance. What is important here is that sentiments can be of great influence, but they need something (e.g. media, politics, or entrepreneurs) that channels this into something tangible or tactile.

In the business literature on grassroots, many papers describe the importance of properly channeling ideas. Fredberg (2009) describes how channels can direct customer attention and support community activities. Van Dijk and Van Den Ende (2002) state that “to handle employee creativity effectively, it is important to organize the process of idea extraction to idea follow-up properly”. Dos Santos and Spann (2011) confirm this by saying that “employees need to be motivated to communicate their ideas and provided with a channel for this communication”. In other words, ideas (like sentiments) need to be looked for, and properly channeled in order to get the most out of it.

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The notion of properly channeling ideas relates to weak signals. As stated in the introduction, Ansoff (1975) argues that instead of focusing on strong signals (or threats); a firm should admit weak signals as a basis of decision-making. He calls this “strategic issue management”, and claims it “overcomes a basic shortcoming of the strategic planning technology which has become increasingly evident in practice—the inability of strategic planning to handle quickly and efficiently individual fast-developing threats and opportunities” (Ansoff, 1975). Similar to channeling ideas in the paper of Dos Santos and Spann (2011), weak signals need to be properly addressed to harvest good ideas and eliminate threats before they become a significant problem. This adds to the list of similarities that the business literature and sociology literature share on grassroots.

One paper drew attention by mentioning grassroots innovations. Donahue and O’Leary (2012) cite Kanter’s Theory of Change, which outlines five forces that must converge in order for major change to occur from a shock. The first force was the idea of the grassroots innovations; ‘aberrations’ that often accidentally or deliberate pop-up in an organization, but are seen as insignificant or non-threatening. The second force is a “crisis or galvanizing event”, the third force is changing strategists and strategist decisions. The fourth force is the individual prime movers, and the fifth force is action vehicles. The last four forces are not relevant to this study, because they do not relate to the weak bottom-up signals that we are looking for (Donahue & O’Leary, 2012).

Just like Ansoff, Kanter (1987) emphasizes the importance of being alert to signals. In one of her videos (about how firms should anticipate on, and stay ahead of change and innovation), she states that there are seven key ingredients that characterize change masters. The first ingredient is: being aware of emerging trends and new ideas. The rest of the ingredients describe a leader-type champion at firms in a changing environment; “combining old ideas to open up new possibilities, communicating a clear vision, building coalitions, working through teams, persisting and persevering, and sharing credit and recognition with all who work on the project”. She also states that firms should “be suspicious of new ideas from below”, which also can be interpreted as looking for weak signals from the lower levels of a firm.

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2.4 Volunteering

The previous sections show that volunteering is a social construct that resembles bottom-up foresight. That is why this section is devoted to volunteering. A model will be derived from the social sciences literature, which will be cross-referenced to the business literature to show the relation with the business literature.

The sociology literature describes different theories about what affects the choice to volunteer. A few of the best-known theories are the volunteer functions inventory, the exchange theory, and the theory of human and social capital. Although the volunteer functions inventory is the best-known theory about volunteering, and it adequately describes what motivations (and rewards) people have for volunteering, it is not relevant to this study. The functional approach is concerned with the personal and social functions served by an individual’s thoughts, feelings and actions (Snyder, 1993). It states that people volunteer in order to satisfy social and psychological functions and that different people can, and do the same volunteering activities in order to fulfill different underlying ‘motive’ functions. Wilson (2000) explains that, although volunteering seems altruistic, people always weigh cost and benefit before deciding what they will be volunteering in; this is called the ‘exchange theory’. For example, parents will more likely join the Parent-Teacher Association when their own children are entering school. The exchange theory therefore states that motivations for volunteering are inherent to the volunteer, thus not relevant, because for this study, we do not care why people volunteer.

The human capital theory predicts what factors influence the likeliness of someone becoming a volunteer. In the 1960s, neo-classical economists such as Schultz (1963) and Becker (1962) introduced the notion of human capital, arguing that a society's endowment of educated, trained, and healthy workers determined how productively the orthodox factors could be utilized (Woolcock, 1998). Human capital means (but is not limited to): an individual’s education, work, income, experience and skills. Years later, Coleman (1988) introduced social capital, defining it as social relationships, social networks and social contacts a person has. Social ties, including friendship networks and organizational memberships, supply information, foster trust, make

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contacts, provide support, set guidelines, and create obligations. These social ties are resources, a form of social capital.

Human and social capital were first linked to volunteering in 1995, when Janoski and Wilson (1995) acknowledged that human capital and social resources are good indicators of why people engage in voluntary activities. Wilson (2000) explains how education, jobs, free time and income are predictors of volunteering. Education increases the awareness of problems, heightens empathy, and increases self-esteem. Take education for example: people with a higher education are also more likely to be asked to be a volunteer, which is partly explained because they are a member of more organizations where they develop social skills, like running a meeting. Social capital is a predictor of volunteering because extensive social networks, multiple organizational memberships, and prior volunteer experience all increase the chances of volunteering. Wilson (2000) also mentions that the effect of social capital on volunteering is stronger among higher-status people. With the information from the previous paragraphs, it is possible to develop a conceptual model of the constructs. We know that human capital and social capital influence the choice of becoming a volunteer, and because motivations are inherent to the volunteer, they are seen as a given and fall under the category of exchange theory. Based on this, the model on the next page is presented (figure 1).

FIGURE 1 Model of volunteering

These constructs were cross-referenced with business literature, using the same journals as in the previous paragraphs. Amanatidou and Guy (2008) mention the importance of human capital by stating that knowledge creation, diffusion and absorption have significant impacts on technological foresight. However, social capital and networking also seem to be important factors. Canongia, Antunes and Pereira (2004) stress the importance of reacting to opportunities TECHNOLOGY FORESIGHT:

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and threats, the ability to capitalize on tacit knowledge of personnel like intellectual capital and expertise (human capital), and the promotion of network-based activities (social capital). Könnölä, Brummer and Salo (2007) argue that diversity in educational or professional background (relates to human capital) is vital in context characterized by uncertainty and technological discontinuities (relates to weak signals). Companies should focus on more action-oriented and comparable reflections of future developments. According to Könnölä et al. (2007), networking (relates to social capital) is an “increasingly essential dimension of foresight activities”. In a recent paper by Heger and Rohrbeck (2012), some key characteristics of a ‘foresighter’ were described as necessary: being curious and receptive (relates to human capital because it is a quality characteristic of a person), being open-minded and passionate (relates to human capital), having a broad and deep knowledge (human capital), and having an internal and external network (social capital). Finally, Roveda and Vecchiato (2008) stress the importance of socialization, working together and social skills (social capital).

To complete the derivation of the volunteering concept from the sociology literature, a similar term has to be found to describe lower-level foresight that pertains to foresight on a personal level. Slaughter (1995) states everybody (does not matter if you are a professional foresighter or not) engages in foresight; whether you are doing groceries for the next week, dressing appropriately for the weather, or planning a journey; you are engaging in what he calls ‘individual foresight’. Havas (2005) also uses this term, stressing the importance of distinguishing ‘visionary thinking’ (what he describes as individual foresight) from collective foresight efforts (‘foresight programs’) that are launched by an organization (or several ones). Figure 2 represents the conceptual model of individual foresight.

FIGURE 2

Conceptual model of individual foresight

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Recall from the introduction paragraphs that this research is organized around two research questions. The first question revolves around the relation between human/social capital and individual foresight: what factors affect individual foresight? The second question relates to the link between individual foresight and signals: how does individual technology foresight affect the creation of (weak) signals?

2.5 Hypotheses

To test the relations between the aforementioned constructs, hypotheses will be developed. Below, each construct is mentioned with their respective descriptions and measurement types.

Several papers describe the importance of social capital in technology foresight. Networking, or network-based activities, is an increasingly important factor of technology foresight (Canongia et al., 2004; Könnölä et al., 2007). Having internal and external networks is a key trait for a ‘foresighter’ (Heger and Rohrbeck, 2012). Roveda and Vecchiato (2008) argue that socialization, working together and social skills are important characteristics for an individual predicting the future. Therefore, the following hypothesis will be tested:

Hypothesis 1a: Social capital is positively related to individual foresight.

Amanatidou and Guy (2008) mention the importance of human capital by stating that knowledge creation, diffusion and absorption have significant impacts on technological foresight. Canongia et al. (2004) stress the importance of reacting to opportunities and threats, and the ability to capitalize on tacit knowledge of personnel like intellectual capital and expertise. To test this, the following hypothesis is developed:

Hypothesis 1b: Human capital is positively related to individual foresight.

As Wilson (2000) stated, the effect of social capital on volunteering is stronger among higher-status people. This means that people who are well educated and have a higher socio-economic status, are more likely to volunteer; they participate in more organizations and are also more

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likely to be active in those organizations (Wilson & Musick, 1997). Thus, the next hypothesis is developed to test this:

Hypothesis 2: Human capital positively affects social capital in its relation to individual foresight.

Although it is proven that foresight helps picking up on weak signals (Kanter, 1987; Donahue & O’Leary, 2012; Day & Schoemaker, 2004; Day & Schoemaker, 2005; Ilmola & Kuusi, 2006), it is not yet proven that foresight fosters the creation of weak signals. This research tries to find out what the relation is (if any), thus:

Hypothesis 3: Individual foresight is positively related to signaling.

FIGURE 3

Hypothesized conceptual model

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3. METHODOLOGY

In this section, I will describe how I conducted my research. First, a description will be given about how the literature review was conducted. Then, the development of the survey will be given. This survey will test the model and its constructs. Hypotheses will be composed to test the relations between the different constructs. This will be continued by a brief description of the data collection, followed by a delineation of the data analysis. Concluding this research will be the discussion of the results, and the limitations of this research.

3.1 Theoretical development

In this part, I am going to describe the process of doing the literary review described in the previous chapter. Several literary databases have been used for obtaining the literature. The sources used for the literature review include, but are not limited to: Business Source Premier, Academic Source Premier, and the University of Groningen (UOG) e-Journal database.

The selected journals were all of top and very good quality, according to the website of the UOG4. A selection of key words was entered in every journal, for an initial top-down search approach. After collecting the relevant results, a bottom-up search approach was done: following references in the initial results, looking for similar key terms and re-entering those terms in the journal search engine. The journals included in the literature review are: Academy of Management Journal, Journal of Product Innovation Management, MIS Quarterly, Research Policy, Technological Forecasting and Social Change, and Technovation (other journals have been selected, but did not return any results). The selected keywords were (combinations of): corporate foresight, technological foresight, technology foresight, strategic foresight, social foresight, foresight capabilities, foresight knowledge, technology forecasting, technological forecasting, technology trend research, trend research, technology trends, emerging drivers of change, emerging technologies, strategic technology analysis, prospective analysis, future oriented techniques, future intelligence gathering process, future analysis, future analysis and future technologies. Every relevant paper has been scanned, documented and (chronologically) listed. There were over sixty different papers documented (see the documentation at Appendix

4

http://www.rug.nl/research/gradschool-economics-and-business/organization/criteria/top-very-good-journals TECHNOLOGY FORESIGHT:

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A). The keywords that were used to search for bottom-up foresight approaches were: crowdsourcing, grassroots, collective intelligence, participatory design, collaborative innovation network, ideas bank(s), outside-in innovation, prediction market(s), prosumer(s), co-creation, hivemind, shared sourcing, open science and technology scouting. This yielded over ninety papers, of which only a few of them were useful for this research. The potential usability for these literature reviews were based on information presented in the title and abstract of a paper. Whenever I doubted, or needed a more in-depth image of a paper, the introduction and conclusion were assessed.

To determine the top journals in the social science area, I searched for Thomson & Reuters's Journal Citation Reports5 database, and found the top twenty journals, based on (and ranked by) their 5-year Impact Factor. The sociology journal list includes (but is not limited to): Annual Review of Sociology, American Sociological Review, European Sociological Review and British Journal of Sociology. The public policy journal list includes (but is not limited to): Journal of Public Administration Research and Theory, Public Administration, Environment and Planning C and Policy Studies Journal. These journals were also compared to the top sociology and public policy journals according to Microsoft Academic Research6, and matched very well. Again, the initial conducted review was done in a top-down approach, followed by a more bottom-up approach. The initial potential usability in this review was not based on the title and abstract alone, but the paper as a whole, because the term ‘grassroots’ often is used as an adjective and is therefore harder to evaluate on relevance.

3.2 Survey development

To obtain data for testing the model, a questionnaire was developed based on the conceptual model presented in the previous paragraph. This survey is intended for people who work at a technological company, or people who have affinity with technology and work at a(ny) company. The three constructs to be tested are the human capital and social capital constructs, the individual technology foresight construct and the (weak) signaling construct. Like the model, the questions were derived from the volunteering theory and cross-referenced them with foresight

5

http://admin-apps.webofknowledge.com/JCR/JCR

6

http://academic.research.microsoft.com/ TECHNOLOGY FORESIGHT:

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literature. Omoto and Snyder (1995) for instance, used educational attainment as a demographic question, although in this research this can be used as an indication of human capital (Wilson, 2000 for social reference; Canongia et al., 2004 for business reference). The signaling (translated in the survey to ‘idea creation’) construct could not be cross-referenced, because of the lack of literature in the social sciences. However, the importance of weak signals has been widely recognized (see for example the theme number of Long Range Planning, April 2004). A research paper by Hiltunen (2008) was a source for survey questions about weak signals, developed for this research. The survey was first reviewed through a pilot test. Several changes were made to improve significance and clarity. This resulted in a total of sixteen statements, to be rated on a 7-point Likert scale ranging from strongly disagree to strongly agree (see Appendix B). The questionnaire was designed in the survey website Qualtrics7 because it offered more functionality than other free survey websites (e.g. forcing users to answer all statements, no time limit on keeping the survey available to fill in).

3.3 Data collection

The Qualtrics link that contained the online survey was mailed to a large number of friends, colleagues and acquaintances, who in turn mailed it to people they know and fitted the target group (which was anyone with a professional background in technology). The survey was also posted on several forums (which was chosen based on their audience) and social media. The data was gathered in March and April of 2013. A total of 267 people responded to the survey, 118 of which were usable. The other 149 people were removed (using a CSV editor) because they failed to finish the survey; they either just read the introduction, or answered only the first few questions and then exited the survey. Demographic characteristics of the 118 respondents are depicted in table 3.

TABLE 3

Demographic characteristics (Sample N = 118)

Sex Professional Background

Male 85% Administrative 6% Female 15% Art/Cultural 3% Age Commercial 3% 7 http://www.qualtrics.com TECHNOLOGY FORESIGHT:

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18 – 23 22% Communication & Design 4%

24 – 29 31% Media 3%

30 – 35 16% Business 19%

36 – 41 9% Financial/Accounting 3%

42 – 47 8% Human Resource Management 2%

48 – 53 8% Logistics 2%

54 – 59 4% Legal/Law 3%

60 – 65 2% Marketing/PR 4%

66+ 0% Medical 7%

Working experience prior to current job Production 2%

None 21% Computer Science 47%

1 – 4 32% Engineering 25%

5 – 9 18% Other 20%

10 – 14 11% Attained educational degree

15 – 20 11% High school 14%

> 20 7% Vocational school (MTS/MBO) 2%

Years employed by current company Bachelor (HTS/HBO) 41%

< 2 46% Master 36%

2 – 5 30% Doctorate 7%

6 – 9 16% Average number of working hours per week

10 – 3 5% < 15 20%

14 – 17 1% 16 – 25 3%

18 – 21 1% 26 – 35 8%

22 – 25 0% 36 – 45 45%

> 25 2% > 45 25%

Several data entries had to be removed to obtain higher statistical accuracy. These entries were removed because they were either not entered seriously (answering every question with “completely agree”, just answering for the sake of filling in the survey) or were too extreme (answering every question with either “completely disagree” or “completely agree”). This resulted in a refined data set of 100 respondents. Even though the number of respondents is low, the respondents fitted the target audience well; a large proportion was IT-professionals or had an academic background in technology.

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4. RESULTS

To validate and check the research model, SmartPLS software was used. After registration, this software is freely available for download at their website (http://www.smartpls.de). As the name says, the software provides Partial Least Squares analysis to facilitate the model of formative constructs. SmartPLS is a software application for (graphical) path modeling with latent variables (LVP). In this software, it is easy to set up your conceptual model and test it. The initial model is shown below (figure 4). The outer constructs (the constructs labeled with HC1, HC2, etc.) represent the questions from the questionnaire.

FIGURE 4

Complete model in SmartPLS 2.0

HC1

HC2

HC3

HC4

Human

Capital CapitalSocial

SC1 SC2 SC3 H2 Individual Foresight IF1 IF2 IF3 IF4 Signaling SIG1 SIG2 SIG3 SIG4 SIG5 H3

To determine the convergent and discriminant validity, the internal consistency is presented, inter-construct correlation and item reliability (Fornell & Larcker, 1981). First, the Cronbach’s alpha was calculated for each construct. This measures the internal consistency of items with

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equal weighting. A construct is judged to be reliable if the value of the Cronbach’s alpha is higher than 0.70. In their book on statistical analysis, George and Mallery (2003) state that a Cronbach’s alpha between 0.7 and 0.8 is acceptable for a survey. To measure the internal consistency, the composite reliability was calculated; the amount of scale score variance that is accounted for by all underlying factors (Brunner and Sub, 2005). Next, the Average Variance Extracted (AVE) for each latent construct is calculated to demonstrate the convergent validity. Fornell and Larcker (1981) state that the AVE is a variance measure (percentage) of the constructs, which are explained by the individual items. They recommend that the value of the composite reliability should be higher than 0.70, and that the value of the AVE should be higher than 0.50. The computed composite reliability and AVE constructs are shown in table 4.

TABLE 4

Factor Analysis and Reliability Statistics

Construct Mean (Std) Standardized Factor Loading Cronbach’s Alpha (>0.70) AVE (>0.50) Composite Reliability (>0.70) All Items After

Dropping Items Human Capital HC1 6.11 (0.90) 0.788 0.783 0.7675 0.5868 0.8502 HC2 5.48 (1.24) 0.747 0.743 HC3 5.63 (1.25) 0.742 0.745 HC4 5.47 (1.17) 0.787 0.791 Social Capital SC1 3.78 (1.47) 0.786 0.791 0.7221 0.6423 0.8426 SC2 3.66 (1.99) 0.730 0.724 SC3 5.41 (1.40) 0.881 0.881 Individual Foresight IF1 5.42 (1.27) 0.833 0.885 0.7957 0.7098 0.8799 IF2 5.05 (1.47) 0.770 0.810 IF3 5.16 (1.23) 0.817 0.831 IF4 5.63 (1.19) 0.679 Dropped Signaling SIG1 3.52 (1.85) 0.678 0.802 0.7034 0.6263 0.8341 SIG2 2.56 (1.73) 0.720 0.775 SIG3 3.52 (1.84) 0.621 Dropped SIG4 5.30 (1.52) 0.659 Dropped SIG5 3.63 (2.02) 0.761 0.796

To obtain discriminant validity, the square root of the AVE is also calculated, where every square root of the AVE is greater than its correlation with any of the other constructs (see table 5). This matrix also shows that convergent and discriminant validity holds for each latent construct in which its square root of AVE loads above 0.50. Discriminant validity appears satisfactory at the construct level in the case of all constructs.

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Human Capital Social Capital (R²=0.24 6) Individual Foresight (R²=0.531) Signaling (R²=0.232) H1b: 0.282* H1a: 0.547*** H2: 0.496*** H3: 0.481***

*** Correlation is significant at the 0.001 level ** Correlation is significant at the 0.01 level

* Correlation is significant at the 0.05 level TABLE 5

Inter-Construct Correlation Matrix (*Square-root of Average Variance Extracted)

Figure 5 shows the results of the tested constructs. All the relations have a positive effect on the constructs. Human capital has the least significant effect (on individual foresight), and human capital has a very significant effect on social capital. Overall, there are no hypotheses showing opposite results. Even though the relationship between human capital and individual foresight is not as significant as the other relationships (hypothesis 1b), it still is a positive relationship. In the next chapter, the results will be discussed and the conclusions will be drawn.

FIGURE 5

Result of Structure Model Analysis Human Capital Individual Foresight Social Capital Signaling Human Capital 0.77* Individual Foresight 0.55 0.84* Social Capital 0.50 0.69 0.80* Signaling 0.44 0.48 0.60 0.79* TECHNOLOGY FORESIGHT:

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5. DISCUSSION & CONCLUSION

The objective of this research was twofold. First, I wanted to find and test what factors affect individual technology foresight. Second, I wanted to research how individual technology foresight affects the creation of weak signals. In order to answer to these research objectives, the following hypotheses were formulated and tested:

Hypothesis 1a: Social capital is positively related to individual foresight. Hypothesis 1b: Human capital is positively related to individual foresight.

Hypothesis 2: Human capital positively affects social capital in its relation to individual foresight.

Hypothesis 3: Individual foresight is positively related to signaling.

The results of the empirical test of the hypotheses show several findings. First, the results reveal that people with a higher social capital have a stronger effect on individual foresight. Respondents who come together with friends, relatives or family and talk about future technologies, are active members of a webforum or social network; and know a lot of people who also think about future technologies. Human capital, however, does not have a significant impact on individual foresight. This can partly be explained by the fact that a large part of the population is self-taught programmers, who most likely do not need a master’s degree to pursue the job they want. Evidence of this can be found in the results. Not shown in the model (because of insignificance) is the “Educational” item. This measuring item asked for what the respondent’s highest achieved educational degree was. The results showed that education was negatively related to human capital, individual foresight and signaling. These findings support the theories of Canongia et al. (2004), Könnölä et al. (2007), and Roveda and Vecchiato (2008); stating that network-based activities are increasingly important factors of technology foresight and that socialization, working together and social skills are important characteristics for an individual predicting the future (hypothesis 1a). Although the relation is not as significant as the other relations, hypothesis 1b is supported: “human capital is positively related to individual foresight” (Amanatidou & Guy, 2008; Canongia et al., 2004).

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Hypothesis 2 states that human capital positively affects social capital in its relation to individual foresight. People who score high on human capital easily pick up new information, can explain this new information easily in their own words, are eager to share their knowledge with others and are frequently relied on as a source of information. These characteristics are often prerequisites of people that work a lot with others, thus human capital increases the effect of social capital. The results support the theory of Wilson (2000), stating that the effect of social capital on volunteering is stronger among higher-status people and people who are well educated and have a higher socio-economic status, are more likely to volunteer; they participate in more organizations and are also more likely to be active in those organizations (Wilson & Musick, 1997). With a coefficient of 0.496, this relation is very significant and therefore this result supports the theory.

The last hypothesis states that individual foresight is positively related to signaling. That means that respondents who are always on the lookout for new opportunities related to future technologies, are early followers of what is new in technology, and focus on future questions about technology, share their ideas about future technologies with colleagues, friends, family, or others through the use of webforums, blogs and social networking sites. This relation has not been tested before and therefore adds to the empirical literature. The results show that there is a positive and significant relation (a coefficient of 0.481) between individual foresight and signaling, thus the hypothesis is supported. Furthermore, the results show that people are motivated to use the internet to share their ideas about future technologies.

This research contributes to the literature in several ways. The study made the subject of individual foresight more discussable by giving it a proper name, characteristics and a model derived from similar theory that is already grounded in the literature. Because this subject and its related theories are relatively new (i.e. individual foresight has not been linked to any of the used theories before), a lot of work has been put in the theoretical research. The extensive theory section and literature reviews can therefore be used as a base or starting point for further research. Although this study adds to existing literature, research on this subject is by no means finished. In this study, it is only proven that certain factors influence and affect other factors; that does not mean other factors are not important. Although the empirical findings are not as extensive as the theoretical section, they do confirm the theoretical findings, making it a usable source for future

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research. In addition, a relation not yet found before between individual foresight and weak signals indicates that there is indeed a lot of potential and shows that there is still a lot that can be researched.

There is a lack of studies on bottom-up technology foresight, but an abundance of literature on top-down technology foresight. There is also some literature on weak signals, but nothing on the creation of weak signals. This research aims to fill these gaps partially by investigating what factors influence individual foresight and how individual foresight affects the creation of weak signals. This study reveals that social capital is a very good predictor of individual foresight, and human capital strengthens that relation. A ‘good foresighter’ should have a knowledge absorption ability, have good social skills to work in a team, is eager to learn and shares new information with others. Other characteristics of a foresighter are that they are always on the lookout for new opportunities related to future technologies and are early followers of what is new in technology. The aforementioned characteristics are also indications of people who are actively creating weak signals. This means that people who are early followers of new technology and focus on future questions about technology, independently create weak signals. Managers can stimulate employees by motivating them to share their ideas with colleagues, using webforums, social networks, or blogs. A large part of the respondents indicated that they often share ideas about future technology with colleagues, friends, family, or others by face-to-face meetings. Company managers can easily stimulate this by setting the right environment for people to share their ideas, or hold monthly meetings to present new ideas.

This research has limitations. Firstly, only a few factors were tested to see what kind of effect they had on specific constructs. As mentioned before, the tested constructs are significant, however, this study only showed the tip of the iceberg. The research was limited due to time constraints. Secondly, the sample size was relatively low. The number of samples obtained is 118. The sample size is quite limited knowing that there are far more people that could be surveyed. None of the companies that were approached, agreed (or even reacted) to an invitation to fill in (and spread amongst the employees) the survey. Therefore, I had to rely on social networks, internet forums, friends and relatives to get as many respondents as possible. A large part of the respondents came from technological forums, which was a well-fitted target audience

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for this study; however, further research should be conducted by using a larger sample size and preferably amongst employees at technological companies.

In this research, I investigate what factors affect individual foresight and how individual foresight affects the creation of weak signals. In the empirical study, a theoretical model is developed by deriving from salient and relevant frameworks from sociology literature. That model is tested by conducting a survey. Social capital appears to have a significant effect on individual foresight, and is strengthened by the influence of human capital. Individual foresight has a positive and significant effect on creating weak signals, and the results show that people who are engaged in foresight, often share this online on web forums, or with colleagues, friends and family.

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TECHNOLOGY FORESIGHT: A bottom-up perspective

A bottom-up perspective

Master Thesis

GUIDO KLEIN WOLTERINK

Supervisor: DongBack Seo

TABLE OF CONTENTS