A classification of social media methods of environmental scanning for entrepreneurial opportunity development

1 1 A classification of social media methods of environmental scanning for entrepreneurial opportunity development Natalie den Engelse University of Twente [email protected] Fons Wijnhoven University of Twente [email protected] Aard Groen University of Twente [email protected] Abstract A limited amount of scholarly literature has focused on environmental scanning and the use of social media by nascent entrepreneurs. This paper aims to address these deficiencies in literature. A theoretical framework is presented that describes the level of scanning towards entrepreneurial opportunity development and includes fifteen social media based methods for scanning the environment with the objective of entrepreneurial opportunity development. This methods are reviewed on their data collection, interpretation and learning. Several implications for both practice and future research derive from this framework and are discussed. Keywords: web 2.0; environmental scanning, entrepreneurship; opportunity development Introduction Entrepreneurs in early stages of developing their entrepreneurial opportunity often do not have a great deal of internal secondary data or historical experience to help fill their information needs (Mohan- Neill, 1995; Lang, Calantone & Gudmundson, 1997; McGee & Sawyerr, 2003). As a result they resort to the usage of information from outside the organization (McGee & Sawyerr, 2003). One way of collecting such information is by environmental scanning, which aims at providing current information that facilitates the identification of opportunities, detect and interpret problem areas and implement strategic or structural adaptations towards the environment entrepreneurs operate in (Daft & Weick, 1984; Daft, Sormunen, & Parks, 1988; McGee & Sawyerr, 2003; McEwen, 2008; Choo, 1999; 2001). With the advent of social media opportunities arise for entrepreneurs to scan the environment. An abundance of user-generated content is created (Chen, 2010) that is freely available to the general public and hence to entrepreneurs. Many customers share their ideas, problems, knowledge and experience via social media (Kozinets,2002). This offers opportunities to listen-in into socially generated knowledge (De Valck, van Bruggen, & Wierenga, 2009; Moor & Weignand, 2005). A majority of social media focus on a specific issue, theme or topic. This enables entrepreneurs to identify target groups relevant to their entrepreneurial opportunity and involve them in developing their entrepreneurial opportunity. Research on environmental scanning and social media are quite limited in the field of entrepreneurship (Stewart, May & Kalia, 2008; Song, Di Benedetto & Perry, 2009; Peters & Brush, 1996). Research on environmental scanning has mostly centered around mature firms and not on entrepreneurs that are in the process of developing their entrepreneurial opportunity. The use of social media by entrepreneurial and established organizations is of sufficiently recent occurrence that there is a limited scholarly literature on the subject (Fischer & Reuber, 2010). Research conducted on social media has mostly centered on social media as marketing tool. Ensuing these deficiencies in literate the following research question arises: What social media methods are available for entrepreneurs environmental scanning activities with the objective entrepreneurial opportunity development? 2 2 The paper is organized as follows. The first section elaborates on the stages of the entrepreneurial opportunity development. The second section discusses environmental scanning. Based on a review of various environmental scanning typologies, four levels of scanning are identified. These levels are described according to the three stages of Daft and Weick‘s (1984) Model of Organizational Interpretation: data collection, interpretation and learning. The third section applies these four level of scanning to the stages of entrepreneurial opportunity development. The fourth section, discusses opportunities social media offers for environmental scanning and proposes fifteen methods for scanning the environment that integrate the use of social media. These methods are mapped on the dimensions stage of entrepreneurial opportunity development and level of scanning. The last section discusses implications for research and practice. Theoretical background Entrepreneurial opportunity development The entrepreneurship literature offers many definitions for entrepreneurial opportunities. This paper follows the definition of the cultural cognitive school. This school views entrepreneurial opportunities as subjective phenomena that are defined and enacted by entrepreneurs through social interaction (Companys & McMullen, 2007). Changes in interpretation of data are the source of entrepreneurial opportunities (Companys & McMullen, 2007). These opportunities do not go from an initial conception of a rough business idea to a running business venture with regular sales in one step (Davidsson, 2005). Just as ideas for new product development (NPD) need further development to become viable (Urban & Hauser, 1993; Veldhuizen, Hulthink & Griffin, 2006; Veryzer, 1998), this also holds for entrepreneurial opportunities (Ardichvili, Cardozo, & Ray, 2003; De Koning & Muzkyka, 1999; Bhave, 1994). In this paper we refer to this as entrepreneurial opportunity development (EOD) and discerns three entrepreneurial development stages (1) identification (2) evaluation and (3) exploitation (Ardichvili et al., 2003; Shane & Venkatarman, 2007). Although the use of stages might imply a linear and stage-gate process it is in practice a nonlinear feedback-driven process (De Koning & Muzkyka, 1999; Bhave, 1994). Information generates feedback that provides new insights that induces a potential revision of the entrepreneurial opportunity (Bhave, 1994; Daft & Weick, 1984). This suggests that the three stages are interconnected through feedback loops. The stages of EOD and the feedback loops are depicted in figure 1. Figure 1: Stages of entrepreneurial opportunity development The initial stage, entrepreneurial opportunity identification, refers to the process of creating an initial vision for an entrepreneurial opportunity (Wakee & Van der Veen, 2004; Ardichvili et al., 2003; Shane & Venkatarman, 2007). This stage is often associated with ‗prior knowledge‘ and ‗entrepreneurial alertness‘ (Kirzner, 1973; Gaglio & Katz, 2001; Ardichvili et al., 2003; Shane, 2000; Venkataraman, 1997). Prior knowledge refers to individuals‘ mental models that represent an individual‘s knowledge and beliefs about how physical and social worlds work (Gaglio & Katz, 2001). Entrepreneurs express a state of heightened alertness to information related to their mental models (Gaglio & Katz, 2001). Although they do not actively search for information they may engage in ‗passive search‘ - a state in which they are receptive to opportunities (Baron, 2006; 2004; Fiet & Norton, 2007). This implies that information and experience is collected without aiming to identify an entrepreneurial opportunity (Csikszentmihalyi, 1996); Lumpkin & Lichtenstein, 2005; Corbett, 2005). 3 3 Mental models enable individuals to perceive connections between seemingly unrelated changes or events ( Baron, 2006). These patterns may form the basis for identifying entrepreneurial opportunities and results in an initial vision of the opportunity. Hence, this stage involves making the decision of whether an entrepreneurial opportunity is identified that is worth investigating its potential to base a new venture on. The intermediate stage is the confidence-seeking part of EOD (Lumpkin & Lichtenstein, 2005; Corbett, 2005). It is in this stage entrepreneurs evaluate the identified opportunity on its value. For this various forms of investigation such as preliminary market testing and financial viability analysis (Corbett, 2005) are used. Entrepreneurs engage in a deliberate effort – following a pre-established plan, procedure or methodology – to secure specific information or information relating to their opportunity to decide to exploit the opportunity or to withdraw. If entrepreneurs decide for exploiting the opportunity they continue to the third stage. If the decision is made to withdraw the opportunity entrepreneurs return to the initial stage. In the last stage the actual business planning begins and the venture is launched (Corbett, 2005). This stage involves selecting options and finalizing choices (Lumpkin & Lichtenstein, 2005). These choices concern strategic launch decisions (e.g. product strategy, market strategy and firm strategy) and tactical launch decisions (e.g. product, price, promotion and distribution) (Hultink, Griffin, Robben, & Hart, 1998). This stage is the most time-consuming part of EOD (Lumpkin & Lichtenstein, 2005; Corbett, 2005). EOD information processing The description of EOD shows that in each stage entrepreneurs need to decide on which course of action they need to follow (Choi et al., 2008). For this they acquire and interpret data to base their actions on (Lumpkin & Lichtenstein, 2005; Corbett, 2005; Minnity & Bygrave, 2001). How entrepreneurs process data is described on the base of Daft and Weick‘s (1984) Model of Organizational Interpretation. This is a widely accepted information-processing model that consists of the three (1) data acquisition (2) interpretation and (3) learning. Data acquisition is the monitoring activity that provides environmental data (Daft & Weick, 1984; Choo, 1999; 2001). Environmental data may include data on the task environment (competitors, customers, suppliers, technology) and/or the general environment (economic, social and political conditions). In the second stage, interpretation, data are given meaning (Daft & Weick, 1984). This involves turning circumstances into a situation that is comprehended explicitly in words and serves as a springboard into action (Weick, Sutcliffe & Obstfeld, 2005). The third stage involves learning and is defined as a new response or action based on the interpretation (Daft & Weick, 1984). In the initial stage of EOD data acquisition is driven by alertness towards issues of interest related to an entrepreneur‘s prior knowledge. This suggest that acquisition takes place by passive attention – data is obtained without intentional seeking- and passive search – other data collection activities result in the acquisition of data that happens to be relevant (Wilson, 1997). Data is given meaning by entrepreneur‘s mental models. This aligns with Klein‘s (Klein, 2008) statement that opportunities are subjective phenomena that are neither discovered nor created, but imagined. Opportunities only exist in the minds of decision makers. Interpretation of data results in the initial vision of an entrepreneurial opportunity. This outcome is for this stage considered to be the learning aspect. In the evaluation stage, entrepreneurs actively seek for data to verify the value of the opportunity. For this they conduct various forms of investigation. This implies that data collection in this stage takes place by active search (Wilson, 1997) and that this search is focused on an issue or event and with specific information needs in mind. Conducting methods like preliminary market testing and financial viability analysis imply that entrepreneurs do not mere use their mental models for interpretation but base it on results of statistical data analysis. Based on the interpretation of the obtained data, entrepreneurs decide whether or not the opportunity is worth pursuing. This decision is the learning aspect of this stage. 4 4 In the last stage of EOD entrepreneurs build on their framework of knowledge, ideas and beliefs that were established in the previous EOD stages. Entrepreneurs in this stage focus on extending their knowledge and collect data by ongoing search (Wilson, 1997). Interpretation of data is mainly centered on futuristic forecasting methodologies that help decide on which target markets entrepreneurs need to focus on and help finalizing choices regarding the launch of the product or service. These decisions imply the learning aspect of this stage. Level of environmental scanning Many entrepreneurs that are still in the process of developing their entrepreneurial opportunity use information from outside the organization because many often lack internal secondary data or historical experience (Mohan-Neill, 1995; Lang et al., 1997; McGee & Sawyerr, 2003). One way of acquiring data from outside the organization is through environmental scanning. Environmental scanning aims at providing current information that facilitates the identification of opportunities, detect and interpret problem areas and implement strategic or structural adaptations towards the environment entrepreneurs operate in (Daft & Weick, 1984; Daft, Sormunen, & Parks, 1988; McGee & Sawyerr, 2003; McEwen, 2008; Choo, 1999; 2001). Literature provides diverse typologies of environmental scanning that help build understanding how individuals undergo the process of data collection, interpretation and learning. Aguilar (1967) and Choo (1999; 2001) identified four modes of scanning. In undirected viewing individuals are exposed to information with no specific information needs in minds (Aguilar, 1967). Information seeking centers on a broad diversity of sources and information use is focused on sensing (Choo, 1999, 2001). Conditioned viewing involves directed exposure, not involving active search, to a more less clearly identified area or type of information (Aguilar, 1967). Hence, information seeking is directed on pre-selected sources and pre-specified topics of interest and information use involves sensemaking (Choo, 1999; 2001). Formal search is a deliberate effort usually following formal methodologies for obtaining information for specific purposes or information relating to a specific issue (Aguilar, 1967). Information needs derive from specific formulated queries, information seeking focuses on an issue or event and information use centers on increasing knowledge within narrow limits. Informal search consists of a relatively limited and unstructured effort to obtain specific information or information for a specific purpose (Aguilar, 1967). Information needs focus on specified targets, information seeking is characterized by systematic information gathering following some method or procedure and information needs center on formal use of information for planning, acting an deciding (Choo, 1999; 2001?) Daft & Weick (1984) expand the four modes of Aguilar by emphasizing that organizations vary in their beliefs about the environment and their intrusiveness into the environment. Undirected viewing reflects a passive approach of data collection and assumes the environment to be unanalyzable (Daft & Weick, 1984). Actions are based on intuition, rumors or chance (Daft & Weick, 1984). Conditioned viewing assumes an analyzable and objective environment. There are no specific information needs that request active data acquisition (Daft & Weick, 1984). Enacting mode reflects an active, intrusive manner of data collection and perceives the environment as unanalyzable. Information search includes testing or manipulating the environment, performing trials in order to learn what error may occur and discover what is feasible by testing presumed constraints. Discovering mode includes active data acquisition that is focused on detecting the correct answer to an information need. Jain (1984) describes scanning in terms of four phases instead of modes. Phase one reflects the most primitive stage of scanning and phase four the most sophisticated. The first phase depicts a situation in which organizations face the environment as it appears. Exposure to information is without purpose and effort. In phase two organizations are vigilant for a likely impact of the environment. They are sensitive to information on specific issues to enhance understanding of a specific event. In phase three organizations deal with the environment to protect the future. They scan the environment in an unstructured and random effort to make an appropriate response to markets and competition. In phase four organizations scan the environment to predict the environment for a desired future. They 5 5 engage in a structured and deliberate effort to collect specific information and use pre-established methodology. Another way of looking at scanning is described by Fahey, King and Narayanan (1984). Their typology describes three types of scanning systems used by organizations: irregular, periodic and continuous. Irregular systems are crisis initiated, are used on ad hoc basis and make use of simplistic data analyses (Fahey et al, 1984). Periodic systems are forecasting oriented, are limited in their scope and methodologies, and use simple statistical methodologies (Fahey et al, 1984). Continuous systems are focused on opportunity finding and problem avoidance, information gathering is a continuous, structured activity and uses forecasting methodologies (Fahey et al, 1984). Based on the aforementioned scanning typologies, four levels of scanning are identified. These levels are described in terms of the three information-processing stages: data collection, interpretation and learning. Table 1 summarizes the four levels of scanning. Table 1: Levels of scanning Environmental scanning modes Information process Description Level 1: Undirected viewing Data collection: General areas of interest; scan broadly a diversity of sources; passive attention; irregular data collection Interpretation: Based on mental models Learning: Serendipitous discovery Level 2: Conditioned viewing Data collection: Able to recognize topics of interest; browse in pre-selected sources and pre-specified topics of interest; passive search; periodic data collection Interpretation: Simplistic data analysis Learning: Increase understanding of a specific event, trend or issue Level 3: Formal search Data collection: Able to specify target; systematic gathering of information on a target; following some method or procedure; ongoing search; extension Interpretation: Futuristic forecasting methodologies Learning: Formal use of information for planning, acting, deciding; make an appropriate response to markets and competition Level 4: Informal search Data collection: Search is focused on an issue or event, active search; continuous Interpretation: Statistical forecasting oriented Learning: Increase knowledge within narrow limits; lookout for competitive analysis The classification of table 1 has several implications for EOD. In the initial stage of EOD data collection takes place through alertness and prior knowledge (Gaglio & Katz, 2001; Shane, 2000). There are no specific information needs and the initial vision for an entrepreneurial opportunity is more based on judgment and intuition than on objective data. Hence, it is argued that entrepreneurs in this stage perform scanning on level one and two. In the evaluation stage, entrepreneurs follow a specific procedure or method for data collecting and for testing their opportunity. This suggest that they scan on level three. In addition, it is argued that entrepreneurs mental models are always active and entrepreneurs are alert to information related to their mental models. Therefore, we propose that level three scanning consist of an accumulation of level one, two and three. In the last stage of EOD, entrepreneurs focus on extending their knowledge by ongoing search. This indicates that entrepreneurs perform scanning on level four. In addition, entrepreneurs may also have specific information needs which requires a specific procedure or method for data collection. Also entrepreneurs‘ mental models 6 6 are also argued to direct data collection on a unconscious level for the reason as mentioned before. This indicates that scanning activities performed in this stage is an accumulation of level one, two, three and four. Social media methods, environmental scanning and EOD Social media is often used to describe a variety of online sites and tools that enable individuals to contribute and share their knowledge freely with other individuals; that enable the joint and simultaneous creation of content by many end-users (Kaplan, 2010; Adebanjo, 2010); that enable users to connect, to collaborate and have online social interactions with other individuals (Lee, 2007; Schillewaert et al., 2007; Adebanjo & Michealides, 2010). There are numerous tools and sites that are labeled as social media and new ones appear in cyberspace everyday (Kaplan & Haenlein, 2010). The academic literature provides an overview of social media classifications. These are presented in Appendix A. The abundance of user-generated content created with social media implies new opportunities for environmental scanning. Social media enable collective consumer creativity – social interactions trigger new interpretations and new discoveries that consumers ―thinking alone‖, could not have generated (Kozinets, 2008, p.341). Every individual brings his knowledge and experience to the social media application. This generates a great variety of ideas to use for ideation and solutions to particular consumption-related problems (Kozinets, 2008; Von Hippel, 2005) that is unelicited and unobtrusive. This socially generated knowledge is freely accessible to the public (Fuller et al., 2006; Sawhney et al., 2005) and hence to entrepreneurs. Using social media tools are proposed to effect the opportunities entrepreneurs recognize and the ways they can go about bringing those opportunities to fruition (Fischer & Reuber, 2010). Including social media in environmental scanning activities is proposed to provide better insight in latent and future consumer needs than information obtained via formal methods like surveys or through informal methods like personal networks. Second, as mentioned before, individuals with a common interest gather on specific social media platforms. This allows entrepreneurs to target a specific population that is relevant to the entrepreneurial opportunity and obtain information from that sample. Fifteen methods are proposed for environmental scanning that make use of opportunities social media have to offer. These methods are commonly used for NPD and encourage consumer involvement (Jansen & Dankbaar, 2008; Van Kleef, van Trijp & Luning, 2005). Since these methods are mainly conducted in an offline setting, the principles of these methods are converted into a social media environment. Figure 2: Theoretical framework social media based environmental scanning methods for EOD 7 7 Figure 2 maps the fifteen methods on their level of scanning and appropriateness to EOD stages. All methods are analyzed according to the information processing stages data collection, interpretation and learning. Following the description of information processing the level of scanning and social media categories appropriate for conducting the method are proposed. (N)etnography Ethnography is an anthropological method that is based on participation and observation in particular cultural arenas ( Kozinets, 2002). Netnography is ethnography on the internet and studies the cultures and communities that are emerging through computer-mediated communications (Kozinets, 2002). It is a marketing research technique that uses the publicly available information to identify and understand customer needs (Kozinets, 2002). ● Data collection: Data collection takes place by copying the computer-mediated communications between online community members that take place on the pre-selected social media platforms. In addition, notes are made on observations of the community and its members‘ interactions and meanings. ● Interpretation: Obtained data is analyzed with content analysis software like Atlas.ti and N‘Vivo. In addition there are social media applications available that facilitate interpretation of data like sentiment- and opinion mining tools and tag clouds (see Apendix A). ● Learning: Understand consumer behavior and determine consumer needs. ● Level of scanning: Informal search. ● Social media categories: social networking, syndication, tracking and filtering content, trends, blogging, sentiment analysis. Category appraisal Category appraisal refers to a set of procedures to obtain a visual representation of positions that products hold in the consumers mind. The basic principle of this method involves selecting a set of competing products and presenting them to respondents. Subsequently, they are asked to rank, rate or sort the products on sensory preference or perceptual attributes (Van Kleef et al., 2001). ● Data collection: The sets of competing products are posted on pre-selected social media platforms that enable respondents to rate or rank the sets of competing product like poll tools. ● Interpretation: In an offline setting statistical analysis like factor anlaysis and multidimensional scaling are often used. In a social media environment poll tools provide simple data analyses options like visualizing the frequency of chosen options that help to interpret the data. ● Learning: discover product opportunities and identify attributes which drive product choice; learn the intensity of competition between products; segmentation. ● Level of scanning: formal search ● Social media categories: social networking; content rating and reputation management. Free-elicitation Free elicitation is a personal interviewing technique in which the respondent is asked to express the attributes he/she considers relevant in the perception of a particular product set (Van Kleef et al., 2005). Respondents are presented with stimulus probes or cues (usually words) and subsequently are asked to verbalize the concepts that come to mind. ● Data collection: Data collection takes place by asking visitors of pre-selected social media platforms what comes to mind when thinking of the concept that is subject of the research or ask them to tag a picture that visualizes the concept. A more passive variation to this approach is to examine how individuals have tagged pictures that visualize the concept on platforms like Flickr, Google Images or Picasa. ● Interpretation: Statements and elicited attributes need to be categorized. Categorization can be done manually or with the use of social media tools like word clouds or data mining tools. Word clouds are visual representations for text data, in which tags are listed alphabetically and 8 8 the importance of each tag is shown with font size and/or collar. This suggests that word cloud mainly assist with determining the importance of the elicted attributes. Data mining tools like Rapid Miner provide more sophisticated analysis like cluster analysis that help categorize terms. ● Learning: Determining attributes consumers perceive as relevant to a particular product. ● Level of scanning: informal search ● Social media categories: Social tagging, social networking sites, syndication, data mining Conjoint analysis The basic principle of conjoint analysis is determining user preferences‘ regarding product attributes and the ideal combination of these preferred attributes (Green, Krieger, & Wind, 2001). The first part of the method is developing a set of product attributes (e.g. price) and corresponding attribute levels (€10 or €20). This set could be based on the output of in-depth-interviews, focus groups or personal expertise (Van Kleef et al., 2005; Green et al., 2001). Based on these attributes and corresponding attribute levels hypothetical products are presented to respondents and are asked to rate these on criteria like preference, acceptability or likelihood of purchase (Van Kleef et al., 2001; Green et al., 2001). ● Data collection: For developing a set of product attributes and corresponding attribute levels, free elicitation techniques or focus groups are proposed for data collection. For the rating of hypothetical products, it is proposed to post these on pre-selected social media platforms and ask respondents to rate these products. A variation to this is asking respondents to customize their own product based on the set of product attributes and corresponding attribute levels. Thus, respondents develop their own hypothetical product. ● Interpretation: Traditionally statistical procedures like multiple regressions and MANOVA are used to interpret the data. ● Learning: Determine importance of attributes as function of consumers‘ preferences; learn how consumers might react to changes in current products or to new products introduced into an existing competitive array (Green et al., 2001). ● Level of scanning: informal search. ● Social media categories: social networking sites, collaboration platforms and content rating applications. Focus groups The basic principle of this method is based on the systematic questioning of several individuals simultaneously in a formal or informal setting (Fontana & Free, 2005, p. 703). ● Data collection: Questions are posted on social media applications. The first question should be broad and unfocused so that it allows the participant to lead the researcher ― on a grand tour of the topic or setting‖ (Deggs et al., 2010, p. 1031). Then follow up with questions that are based on the comments of participants. Such questions elicit more information and greater feedback from the participants (Deggs et al., 2010). Researcher must have a consistent presence in the online focus group without attempting to steer the conversation or attempting to coerce participants. Instead of posting question and responding to comments, free webinar software could be used to conduct vocal focus session. In this case, the webinar sessions should be recorder and notes should be taken. ● Interpretation: When bulletin boards or other comparable social media are used for focus groups, the output of the communication between research and respondent is text based, interpretation takes place by content analysis. This can be done manually or social tools like data mining and sentiment analysis also be used for interpreting the data. When focus groups are held via webinars, interpretation is mainly done manually. The focus is on categorizing what respondents have said during the session. ● Learning: Identify the most important drivers of consumer choice for a particular product, learn more about consumer views and opinions. 9 9 ● Level of scanning: informal search ● Social media categories: social networking sites, online message boards, question and answering applications Information acceleration Information acceleration (IA) is a concept testing method employing multimedia stimuli and experimental set-ups (Urban et al., 1996). In this method a virtual buying environment is created that simulates information accessible in a future buying environment (Urban et al., 1997; Eliasberg, Lilien, Rao, 1995; Herstatt & Lettl, 2004). This method already embraces the possibilities offered by social media. However, conducting this method costs about a hundred thousand dollar, which makes it not a very accessible method. Therefore, a variation to this method is proposed. ● Data collection: Already existing virtual reality worlds related to the product concept can be observed. For this the guidelines for netnography apply. Another variance is developing a variety of mock-ups and spread them via various social media channels that include potential customers. ● Interpretation: Reactions and comments can be analyzed manually by following the guidelines for content analysis. In addition, free content analysis tool can be used for interpreting the obtained data. ● Learning: Determine customer perceptions, preferences or buying intentions, forecast sales, develop strategy alternatives. ● Level of scanning: informal search ● Social media categories: social networking sites, crowdsouring, question and answer platforms. Lead user technique Lead user technique is based on identifying customers who face needs months before the bulk of the marketplace and are expected to gain high benefits from obtaining a solution to the needs they face (Von Hippel, 1986; Von Hippel & Franke, 2009). Finding lead users involves screening a large number of potentially relevant users and by asking them questions regarding user innovations and lead users characteristics (Eric von Hippel, 2005). ● Data collection: Collecting data for identifying lead users is the guidelines of netnography apply (Baumbach, 2010). Next to this, social media tools that focus on identifying experts like Klout and Listorious, are also relevant to include in the data collection stage. ● Interpretation: Obtained data is mainly in the form of text. For interpreting the data content analysis is suggested. Analysis should be centered on on the following lead user characteristics: ahead of trend, dissatisfaction, product-related knowledge, use experience, involvement and opinion leadership (Baumbach, 2010; Spann, Ernst, Skiera, & Soll, 2009). ● Learning: Identify lead users for detecting future problems and to find solutions for problems lead users experience regarding the product/service. ● Level of scanning: informal search. ● Social media categories: trends, blogging, collaborating, social networking, crowdsourcing, question and answer application. Laddering Laddering is ―an in-depth, one-on-one interviewing technique used to develop an understanding how consumers translate the attributes of products into meaningful associations‖ (Reynolds & Gutman, 1988). The objective is to uncover how product attributes, usage consequences and personal values are linked in a person‘s mind (Wansink, 2003; Veludo-de-Oliveira, Ikeda, & Campomar, 2006). The first part of this technique involves asking individuals to express spontaneous thoughts and other associations linked to the product category (Van Riel et al., 1998). The line of questioning proceeds from product characteristics to user characteristic, which attempts to abstract reasons of why an attribute is important. 16 16 Venkataraman, S. (1997). The distinctive domain of entrepreneurship research. Advances in entrepreneurship. Veryzer, R., Jr. (1998). Discontinuous innovation and the new product development process. Journal of Product Innovation Management. Wilson, T. (1997). Information behaviour: An interdisciplinary perspective. Information Processing & Management, 33(4), 551-572. Yasai-Ardekani, M., Nystrom, P.C. (1996). Designs for environmental scanning systems: Tests of a contingency theory. Management Science, 42(2), 187–204. Zaltman, G., & Coulter, R. H. (1995). Seeing the voice of the customer: Metaphor-based advertising research. Journal of advertising research, 35(4), 35–50. 17 17 Appendix A: Categories of social media tools Category social media application Description Example application Social networking Facilitate meeting people, finding like minds, sharing content, using ideas from harnessing the power of the crowd, network effect and individual production/user generated content. Facebook, Hyves, Linkedin, Ping.fm, Friendfeed.com, Hyves. Syndication Gather information from diverse sources across the Web and publish in one place. Example of such applications is the RSS feed aggregator that creates a single webpage with all your feed and email in one place. It facilitates enhanced decisionmaking by speeding the dissemination of information about preselected topics to people who want it. Friendfreed, Technorati, Google Blog Search, Blogpulse, Twitter Search, Dipity, Kosmix, knowabout.it, Addict-o-matic, Social mention, Scour, Samepoint, Whos Talkin, BoardTracker, Omgili, TalkDigger, Google Alerts, RSS . Data mash-ups Web services that pull together data from different sources to create a new service (i.e. aggregation and recombination) Yahoo Pipes, Mashmaker, Mashup Feeds Tracking & Filtering content Services that keep track of, filter, analyze and allow search of web 2.0 amounts of web 2.0 content from blogs, multimedia sharing services Facebook Analytics, Google Analytics, Topsy, Youtube Insight, Tweetstats, Twitter Analyzer, Klout, Social Mention, Twitter Counter, Backtype, Boardreader, CoComment, HowSociable Collaborating Platforms that provide a place for people to collaborate. People can directly edit and hence extend, updata, modify or even delete the content. Dropbox, Wikis, google docs, presentlyapp.com, buzzable.com Source ideas or work form the crowd/question and answer application Seek ideas, solutions to problems or get tasks completed by outsourcing to users of the web Yahoo! Answers, Aardvark, Yahoo! Groups, Google Groups, Twitter, Facebook, Hunch, Quora, Linkedingroups. Trends Applications that monitor how popular or unpopular a particular topic have been over a range of time. Google Trends, big xRank, Trendrr, Blogpulse, Trendpedia, Hashtags.org. Social Tagging/Social Bookmarking Social tagging describes the collaborative activity of marking shared online content with keywords or tags as a way to organize content for future navigation, filtering, or search Flickr, del.icio.us, connotea.org, bluedot.us, magnolia, digg, simpy, blogmarks, reddit, furl.net, blinklist, spurl, delirious, Content rating and reputation management Applications that let participants rate other participants or content StumpleUpon, Last.fm,, Facebook likes Competitive research Applications that provide information about competitors, such as who works there, what kind of roles they‘re hiring, opinions on the company from former employees, current employees, industry analysts and outsiders. Linkedin, Jigsaw. Crunchbase, ChubbyBrain, Glassdoor Blogging Blogs are expressions of personal or professional opinion or experience which other people can at most comment Googleblogs; Opensiteexplorer (blogrank). Alltop, blogsearch, Icerocket Sentiment analysis Tweetfeel, Twendz, Twitrratr, Rankspeed, Newssift, Moodviews, Socialmention, Alertrank, Sentiwordnet, Opinionfinder