How images and color in business plans influence ...

JBV-05741; No of Pages 17 Journal of Business Venturing xxx (2014) xxx–xxx

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Journal of Business Venturing

How images and color in business plans influence venture investment screening decisions☆ C.S. Richard Chan a,⁎, Haemin Dennis Park b a b

College of Business, Stony Brook University, Stony Brook, NY 11794, United States Department of Management, Bennett S. LeBow College of Business, Drexel University, Philadelphia, PA 19104, United States

a r t i c l e

i n f o

Article history: Received 27 March 2014 Received in revised form 11 November 2014 Accepted 2 December 2014 Available online xxxx Field Editor: E. Fischer Keywords: Visual cues Heuristic perspective Venture investment screening decisions

a b s t r a c t We explore how product images and color in business plans influence venture investment screening decisions. Because images are accessible, memorable, and influential, we argue that product images in a business plan will increase the likelihood of favorable judgments during screening decisions. Moreover, because red and blue automatically affect an individual's cognition in different manners such that red elicits negative associations and blue elicits positive ones from the evaluators, we predict that the use of red in a business plan will decrease the favorability of judgments during screening decisions, while the use of blue will increase their favorability. Using a quasi-experimental field study and a series of controlled experiments, we find partial support for a positive effect of product images on favorable screening decisions and a consistent negative effect of red on favorable screening decisions. Published by Elsevier Inc.

1. Executive summary Prior studies have considered how venture investors use various venture characteristics associated with positive performance outcomes (e.g., venture management team human capital, industry attractiveness, product novelty, and so on) to make rational investment decisions. However, few have considered how visual cues affect investment decisions. Visual cues are important because people tend to automatically attend to and process images more easily than written information. Indeed, prior studies show that, although people attempt to make rational decisions, they are predominantly affected by heuristic processing using available information at hand, particularly for rapid decision making tasks. We explore how product images and color in business plans affect the likelihood of favorable judgments during screening decisions. Using a quasi-field experiment from a business plan competition (Study 1) and a series of controlled experiments recruiting subjects from an international franchise exhibition (Study 2), we find partial support for the prediction that a greater amount of product images will lead to greater favorability in screening decisions (Study 1 supports it, but Study 2 does not), whereas we find consistent support for the prediction that red negatively affects favorability in screening decisions.

☆ We thank Ming-Jer Chen, Wen Hai, Kevin Yuk-fai Au, Tori Yu-wen Huang, Meng-hua Hsieh, David Gomulya, Bradley P. Owens and conference participants at the Advances in the Psychology of Entrepreneurship and Academy of Innovation and Entrepreneurship. We also thank seminar participants at Bond University, City University of Hong Kong, Maastricht University, University of Calgary, University of Nottingham Ningbo China, Peking University HSBC Business School, and Stony Brook University, for their valuable comments on previous versions of this paper. We thank the Center of Innovation and Entrepreneurship at the University of Washington, Melody Luo, Wen Xia, and Wei Yang for their assistance in data collection. Our paper also benefited greatly from Field Editor Eileen Fischer and three anonymous reviewers. ⁎ Corresponding author. Tel.: +1 631 632 5308. E-mail addresses: [email protected] (C.S.R. Chan), [email protected] (H.D. Park).

http://dx.doi.org/10.1016/j.jbusvent.2014.12.002 0883-9026/Published by Elsevier Inc.

Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

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C.S.R. Chan, H.D. Park / Journal of Business Venturing xxx (2014) xxx–xxx

This study contributes to entrepreneurship and managerial decision making research by providing theory and empirical evidence on how visual cues affect such decisions. Our findings complement the traditional rationality framework that explains how venture investors make their decisions based on both informational and visual cues embedded in the planning documents. Moreover, although prior studies have documented staging of venture investment decisions into various phases (e.g., searching and screening, due diligence, and deal structuring phases), most have overlooked how decisions in each stage might differ. Indeed, most studies have only considered the final outcome; i.e., whether an investment is materialized or not, leading to somewhat inconclusive results on how much business plans matter. This study unpacks this black box of venture investment decision making process by focusing on the initial screening phase, where heuristic processing of information by investors is more likely to be salient. 2. Introduction Speed is essential in making decisions in high-velocity and dynamic entrepreneurial environments (Baum and Wally, 2003; Eisenhardt, 1989). Decision makers often rely on speedy screening decisions to narrow choices down to a manageable shortlist before making a final decision. For instance, employers compile a shortlist of candidates before making a hiring decision or firms negotiate terms using a shortlist of potential alliance partners before making a final deal (Brown and Campion, 1994; Tyler and Steensma, 1998). Likewise, venture investors often shortlist a few ventures out of hundreds of proposals for preliminary due diligence (Metrick and Yasuda, 2011). Scholars have traditionally considered how people rely on analytical processing to consciously attend task-related informational cues for making screening decisions. For example, studies on ventures investment decisions suggest that investors decipher venture characteristics, such as founder capabilities and industry characteristics, to determine cue validity, i.e., the conditional probability that an object falls into a particular category using presented information for investment decisions (e.g., Chen et al., 2009; Kirsch et al., 2009). However, this framework often lacks psychological grounding and empirical fidelity (Powell et al., 2011). Indeed, a growing number of studies complement this framework by illustrating how heuristic processing, a fast, automatic, and effortless cognitive processing, prompts people to rely on various heuristic cues or mental shortcuts such as affect, and message framing, to make decisions (Baron, 2008; Cardon et al., 2012; Kahneman, 2003). One often overlooked category of heuristic cues are visual ones (Ambady et al., 2006; Mehta and Zhu, 2009). These cues can affect decision makers' motivation and cognitive evaluations because they are automatically captured by human senses and their effects are robust and unlikely influenced by informational load, time pressure, or fatigue (Ambady and Gray, 2002; Friedman and Förster, 2010). We explore how two visual cues, product images and colors, influence venture investment screening decisions. Because visual images are accessible, memorable, and influential (Ambady and Gray, 2002; Blossom and Morgan, 2006), we hypothesize that product images lead to more favorable screening decisions. We then delineate how color influences screening decisions. Different colors have strong learned associations with danger or safety in the environment and their effects on human motivation and cognitive evaluation are well documented across various tasks (Crowley, 1993; Friedman and Förster, 2010; Mehta and Zhu, 2009). Consistent with prior color research (e.g., Bellizzi and Hite, 1992; Mehta and Zhu, 2009), we focus on the effect of red and blue on investment screening decisions involving business plans. Combining a field study and a series of controlled experiments, we find a robust negative effect of red on screening decisions. 3. Theory and hypothesis 3.1. New venture investment decisions Early qualitative studies found that venture investors evaluate investment opportunities in multiple stages (Hall and Hofer, 1993). Venture investors (e.g., venture capitalists and angels) first search and screen potential opportunities (i.e., searching and screening stages). They then select a few opportunities from the initial set for further comprehensive assessment (i.e., due diligence stage) and eventually negotiate terms (i.e., deal structuring stage) with the few selected ventures to finalize an investment contract (Hall and Hofer, 1993). However, subsequent studies mostly overlook such sequences and simply explore how various venture characteristics influence investment decisions (e.g., Chen et al., 2009; Eckhardt et al., 2006; Kirsch et al., 2009; Shepherd, 1999). These studies predominantly adopt a rationality framework borrowed from signaling theory in economics (Spence, 1973) or models from classical cognitive psychology, including the lens model (Brunswik, 1955), prototype theory (Rosch, 1975), and unimodel (Kruglanski and Thompson, 1999). This framework assumes that investors base their decisions on various venture characteristics that reflect what they “believe to be the basis for their judgment” (Chen et al., 2009, p. 202). In order for these characteristics to be influential, investors use the characteristics they believe to have high cue validity, e.g., a greater association with such desirable outcomes as venture survival and profitability, to reach investment decisions (Chen et al., 2009; Kirsch et al., 2009; Shepherd, 1999). For instance, using conjoint analysis, scholars found that cues associated with an entrepreneur's ability to manage new ventures (e.g., educational background and prior experience) and industry-related characteristics (e.g., timing of entry, competitive rivalry, and period of monopoly) influence investment decisions (Shepherd, 1999; Shepherd et al., 2000). Other characteristics, such as top management team composition and entrepreneur preparedness, were further validated outside laboratory settings and have been shown to communicate valuable signals to investors and thus influence their decision outcomes (Chen et al., 2009; Foo, 2010; Foo et al., 2005; Kirsch et al., 2009). Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


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Some recent studies, however, postulate that these cues might not influence investment decisions. Institutional theorists argue that certain cues merely serve a ceremonial role designed to show that firms conform to mimetic norms, thus signaling their legitimacy to relevant stakeholders (e.g., DiMaggio and Powell, 1983; Kirsch et al., 2009). For example, although business plans are often created to communicate to both internal and external stakeholders, these documents may serve a largely symbolic purpose and may be infrequently used to facilitate internal decision making or updated or referenced by entrepreneurs, and thus have a limited influence on resource acquisition (Honig, 2004; Honig and Karlsson, 2004; Kirsch et al., 2009). Indeed, a study that looked at the funding decisions of a venture capital firm found that business planning documents were weakly associated with actual funding outcomes, and many venture characteristics embedded in these documents did not predict funding decisions (Kirsch et al., 2009). Despite their different predictions on the influence of business plans and venture characteristics on investment decisions, these studies share similar shortcomings. First, these studies often overlook the earlier documentation of different stages of venture investment process (Hall and Hofer, 1993). Indeed, most simply investigate the impact of certain venture characteristics (e.g., management human capital, technology, industry characteristics, and so on) to final investment outcomes. Yet, investors at different stages are likely to process information differently. For example, compared to making final investment decisions at the due diligence stage, screening decisions are more likely to involve less rational processing because investors often have to evaluate hundreds of business plans (Metrick and Yasuda, 2011). Hence, they are only willing to spend a few minutes reviewing business plans (many simply look at executive summaries) to reach screening decisions (Hall and Hofer, 1993). Nonetheless, studies typically assume that investors use analytical processing to consciously attend and evaluate the cue validity of those factors. Where they primarily differ is in their assumptions about which particular cues are more valid (Chen et al., 2009; Kirsch et al., 2009; Shepherd and Zacharakis, 2002). Such a rationality framework is useful in guiding current research on venture investment decisions, yet it cannot fully account for human behavior. This framework assumes that decision making is a series of if–then chains of reasoning. Such reductionism oversimplifies heuristics in the cognitive processes and results in a narrow understanding of human decisions (Payne and Jacoby, 2006; Petty and Brinol, 2006; Pryor and Reeder, 2006; Wyer, 2006).

3.2. Heuristic perspective Many studies have illustrated the importance of another type of cognitive processing, i.e., heuristic processing, on human decisions. This type of processing is rapid, implicit, automatic, and reliant on heuristic cues shaping behavior (Evans, 2008; Kahneman, 2003; Kahneman and Klein, 2009). Studies found that although analytical processing occasionally governs human cognition, heuristic processing appears to play the more dominant role (Kahneman, 2003; Kahneman and Frederick, 2002). This is because heuristic processing occurs rapidly, influences behavior spontaneously and prompts people to rely on heuristic cues for decision-making (Evans, 2008; Kahneman, 2003; Kahneman and Klein, 2009). Indeed, studies have consistently found that people tend to rely on the availability of information and their affective experiences to make judgments and decisions (Baron, 2008; Evans, 2008; Kahneman and Tversky, 1979; Schwarz and Clore, 1983). For instance, weather can sometimes affect decision-making, e.g., college admissions officers rely on applicants' academic records more on overcast days than sunnier ones (Simonsohn, 2007). One influential heuristic cue that has been overlooked in entrepreneurship and strategic decisions literature is visual heuristics, i.e., how people rely on visual cues to make decisions. Visual cues are one of several perceptual cues, along with olfactory and auditory cues, that influence human behavior through heuristic processing (e.g., Alter and Oppenheimer, 2006; Liljenquist et al., 2010; Middelstadt, 1990; Zhong et al., 2010). Visual cues are more accessible than others because about 30% of human brain is devoted to processing vision, compared with 3% for hearing and 9% for touch (Grady, 1993). Indeed, most early human learning experience occurs through visual perception (e.g., Blossom and Morgan, 2006) and people directly perceive visual cues and automatically process embedded information to form quick judgments (e.g., Ambady et al., 2006). For example, managers typically take about thirty seconds to select job candidates and evaluate job performance based on visual cues, such as facial expressions and gestures (e.g., Ambady et al., 2006; Kenny et al., 1994). The effect of visual cues has been found to be quite robust even in the presence of heavy informational load, time pressure, fatigue, boredom, or monetary incentives (Ambady and Gray, 2002; Ambady et al., 2000). Visual cues are often abundant during the initial screening stage of venture investment decisions because investors typically receive many business plans that contain visual cues such as visual images, charts, and colors.1 To avoid information overload and evaluate a large number of business plans, investors tend to rely on heuristic processing (e.g., Åstebro and Elhedhli, 2006; De Clercq and Sapienza, 2006; Franke et al., 2006) to reach “a go/no-go decision in an average of less than six minutes” (Hall and Hofer, 1993, p. 25). We thus consider how two visual cues, i.e., visual image and color, influence new venture investment decisions because these cues are often presented in business plans and could influence decisions.

1 For example, we found that in the one page executive summaries of 36 business plans submitted to a competition held in 2010, 36% included product pictures, 19 % included charts, and 100 % included colors in addition to black and white. 89% of these ventures used colors in the logos of company names, 56% used colors to highlight a particular segment, 53% used colors to highlight section titles, 22% used colors to divide different sections, 19% used colors to highlight keywords, 31% used colors to highlight financial data, and 61% used colors in figure or tables.


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3.3. The effect of visual images on new venture investment decisions A visual image is easier to remember than written information because it takes less time to recognize and interpret than written information (e.g., Lutz and Lutz, 1977). Visual images are so influential that they could create false memories (Wade et al., 2002). For instance, when information is presented in both visual and written form, visual effect will likely play the dominant role influencing decisions and may even negate the effect of written information, particularly when individuals are making swift decisions. We suggest that the presence of product images will positively influence a venture's favorability in screening decision. First, because visual images are easier to access and remember (e.g., Ambady et al., 2006; Lutz and Lutz, 1977), investors are more likely to recall a venture that presents product images compared to another lacking those images. The former is more likely to be approached and evaluated by more investors. Second, because product images such as product prototype or actual products are more salient and influential than information presented through other channels, e.g., written or auditory (Ambady et al., 2006), and they are thus more likely to be perceived as an important proxy of venture profitability and assigned high cue validity by investors compared to information presented through other channels. Hypothesis 1. The more product images featured in an executive summary, the more favorably it will be evaluated.

3.4. The effects of red and blue on new venture investment decisions Color not only has aesthetic value and enhances information presentation (Benbasat and Dexter, 1985) but it also influences decision makers automatically (e.g., Mehta and Zhu, 2009). Colors are associated with positive or negative connotations about the environment. Repetitive associations can result in approach and avoidance motivation (Elliot and Maier, 2007) that automatically guide the human cognitive process (Friedman and Förster, 2010). However, the effects of color on behaviors are context dependent. For example, red often signals attractiveness in relationship contexts such as dating (Beall and Tracy, 2013; Elliot and Niesta, 2008), but it signals danger and warming in achievement contexts such as classroom and investment settings (Elliot and Maier, 2007; Elliot and Pazda, 2012; Mehta and Zhu, 2009). Further, there are cross-cultural differences in color preference (Madden et al., 2000; Wiegersma and Van Der Elst, 1988). In many Asian countries, red is associated with fortune and prosperity and used to indicate upward moving trend of stock markets, which is opposite than its usage in North American countries. Venture screening decision contexts are a type of achievement context because those engaged in venture screening are ultimately attempting to achieve returns on their investments. Further, because the primary colors red and blue have been frequently shown to affect behavior (e.g., Bellizzi and Hite, 1992; Mehta and Zhu, 2009), we focus on how red and blue affect screening decisions. In the U.S., red is frequently associated with danger and warning, particularly in achievement settings (Mehta and Zhu, 2009). For example, red pens are used to note errors on papers, and stop or warning signs are usually red. It is also linked to the highest level of hazard alert (Braun and Silver, 1995). In investment settings, red often indicates a downward moving trend of stock markets or loses in financial statements. These connections result in a learned association of red and negative outcomes. People automatically perceive red as a negative cue about the environment, leading to vigilant and risk-averse behavior (Friedman and Förster, 2010; Koch et al., 2008). Because of the automatic avoidance motivation, red significantly influences our cognitive processes and evaluation. First, red induces more vigilant and risk aversive behavior. People who detect red in the environment are more likely to pay attention to details associated with ongoing tasks, narrowing their scope of attention (Maier et al., 2008). Because red signals danger in the environment, people tend to dislike its presence (Mehta and Zhu, 2009; Silver and McCulley, 1988). Further, its association with negative outcomes causes people to negatively react or even avoid red-associated objects, events, or outcomes (Bellizzi and Hite, 1992; Middelstadt, 1990). The use of red in business plans will negatively impact screening decisions because red is associated with danger and warnings and cause investors to become more vigilant and risk-averse (Friedman and Förster, 2010; Mehta and Zhu, 2009). The use of red in business plans is likely to induce the same risk aversion and discourage investors from considering such opportunity. As a result, a venture that uses more red in its business plan is likely to attract fewer investors. Because red elicits vigilant and detail-oriented behavior (Maier et al., 2008), investors are more likely to carefully evaluate detailed aspects of the business, resulting in the use of various criteria to make screening decisions. Because red is generally less preferred than other colors, such an attitude may transfer to objects associated with it (Crowley, 1993; Middelstadt, 1990) and lead to negative evaluation of criteria for the screening decisions. Thus,

Hypothesis 2. Compared with business plans that feature proportionately less of the color red, those that feature more will be evaluated less favorably. Blue is another primary color that has been shown to influence the cognitive evaluation process, particularly in performance evaluation settings (e.g., Mehta and Zhu, 2009). It is often associated with positive outcomes such as tranquility and safety (Kaya and Epps, 2004; Mehta and Zhu, 2009). Blue signals a benign environment, resulting in approach motivation which prompts individuals to behave in a more exploratory manner, i.e., one welcoming risk (Friedman and Förster, 2010; Schwarz, 1990). Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


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Because blue promotes approach motivation, it influences various cognitive processes and evaluative tendencies. A blue-induced approach motivation broadens the scope of attention, prompting innovative problem-solving strategies and creativity (Maier et al., 2008; Mehta and Zhu, 2009). A learned association between blue and environmental safety may explain the relationship between blue and a positive evaluation tendency. In studies, blue was consistently preferred over other colors and elicited a positive reaction to given objects (Mehrabian and Russell, 1974; Mehta and Zhu, 2009; Silver and McCulley, 1988). A retail environment featuring blue was perceived as more attractive, and elicited more positive reaction than a red environment (Bellizzi and Hite, 1992; Middelstadt, 1990). Thus, when investors initially scan for opportunities, a new venture that employs blue in its planning document will likely motivate exploratory and risk-taking behavior (e.g., Mehta and Zhu, 2009), allowing investors to associate the new venture with profitability. Moreover, because blue is preferred over other colors, this preference may transfer to the venture's documents (e.g., Bellizzi and Hite, 1992; Crowley, 1993; Mehta and Zhu, 2009; Middelstadt, 1990). These effects reinforce an investor's decision to positively evaluate a new venture. As a result, such a venture may be more likely to be approached and evaluated further by investors, compared with one that does not feature blue in its business plan. Further, because heuristic processing causes investors to be influenced by a color rapidly and automatically, the effect of blue will be unlikely to interfere with other factors. Overall, a new venture that uses more blue in its planning documents should attract a greater number of investors likely to positively evaluate this particular venture, resulting in a higher level of investment. Thus, Hypothesis 3. Compared with business plans that feature proportionately less of the color blue, those that feature more will be evaluated more favorably.

4. Methods We conducted one quasi-experimental field study, Study 1, and two experiments, Studies 2a and 2b, to test our hypotheses. The combination of these studies complements each other by allowing us to establish generalizability of our findings using different empirical settings. These studies also reduce common method bias from the effects of item characteristics, item context, and measurement context (Podsakoff et al., 2003) because we temporally and methodologically separate the collections of our dependent and explanatory variables in Study 1 and use a different context and research designs in the other studies. Moreover, the use of controlled experiments in Studies 2a and 2b allowed us to isolate causal inference of our theoretical framework by eliminating confounding factors and manipulating focal variables (Campbell and Stanley, 1966). 4.1. Study 1 In Study 1, we tested the hypotheses in the context of business plan competition. In this naturally-occurring setting, we collected data over a four year period (2007–2010) using a counter-balanced design. This quasi-experimental design has several advantages. All judges had the same opportunities and resource sets, two important factors that may influence investment behavior. Moreover, judges in different groups evaluated ventures in a different order of treatment, reducing internal validity threats such as maturation and history (Campbell and Stanley, 1966). Most importantly, similar to actual investors making screening decisions (Hall and Hofer, 1993), the judges in our study evaluated multiple ventures within a short span of time, allowing the findings to be more generalizable compared to true experiments. 4.1.1. Empirical context, sample, and material An entrepreneurship center of a major university in the U.S. hosts an annual business plan competition. We used the Road Show round of the competition as our research setting. One week before the round, participating venture teams submitted electronic files of one page executive summaries to a staff member of the entrepreneurship center. These one-page summaries were then organized into PDF files and distributed to judges, who were mostly entrepreneurs or investors and few lawyers involved in the local entrepreneurial community. These judges had approximately one week to review these one-page summaries and prepare detailed evaluations of venture teams. In the Road Show round, judges had a thousand virtual dollars to invest in a portfolio of the ventures they considered most viable and had 4 hours to visit teams' physical booths, where teams pitched their products or ideas using visual aids or sample products. These pitches were informal and were not carefully controlled or timed. Judges took an average of two and a half hours, roughly a few minutes to evaluate each venture, similar to the time spent by venture capitalists evaluating potential investment opportunities at the screening stage (Hall and Hofer, 1993). Judges visited these booths in no particular orders. Some started their evaluations by visiting a particular booth at one corner, whereas others started at different corners. Some moved counter-clock wised, some moved clock wise, and a few moved depending on the traffic flow. At the end of the round, all investments were tallied, and the 16 teams that amassed the most investment dollars advance to the next round. Between 2007–2010, we complied the screening decisions made by 644 judges of the one page executive summaries of 134 ventures. The ventures were not only started by student participants but also by non-students. Moreover, although the majority of teams were from the metropolitan area in which the competition was held, new ventures from other nearby cities participated in the competitions. Of these, 41 were in the technology arena, 22 in consumer goods, 19 in health care, 16 in industrial, 14 in consumer services, 9 in energy, 5 in non-profit, 3 in basic materials, 3 in financials, and 2 in the telecommunications business. On average, each venture Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

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team was comprised of 3.26 members, 11.89% of whom had earned bachelor's degrees, 16.18% had received MBA degrees, 9.37% had an advanced technology degree, and 9.78% possessed entrepreneurial experience. Each member had worked for an average of 0.46 companies; 40 ventures had patents pending, provisional patents filed, or patents obtained. 4.1.2. Dependent variable We measure the favorability of screening decisions by aggregating the total amount of imaginary dollars that judges allocated to each venture. This measure allows us to estimate screening decision outcomes of these ventures. Because the number of judges varied across years, total investment amounts for ventures also varied. Thus, we group-mean centered these investment amounts by year to facilitate comparisons across annual competitions. This is a scoring mechanism of screen decisions, where a high value reflects high favorability for a venture in the competition. 4.1.3. Explanatory variables We measured visual images of product prototype or actual product/service using the following steps. We independently looked at a venture's business plan to determine whether images were included and then determined if any displayed images reflected a product or service and then counted the number of images that reflected the product or service. We compared our scores, discussed differences, and compiled a unified score. A higher value reflected a greater number of images to illustrate a venture's products or service. Similar to prior studies that captured the presence of a particular characteristic associated with other characteristics (e.g., Pfarrer et al., 2010), we employed two steps to measure the usage of red compared to other colors in a venture's business plan. In the first step, we asked two research assistants to use the Color, Name, and Hue tool from a color classification website (http://www.colblindor. com/color-name-hue) to identify colors presented in the PDF files of executive summaries. These were then assigned to six groups: green, red, blue, orange, yellow, and violet. The assistants estimated the percentage of the executive summary document in which each color appeared using a 6-point scale, with zero representing no appearance by a color group, 1 being up to 20% of a particular color, and 5 being up to 100% use of a color group. Because the color coding showed a high inter-rater reliability (Krippendorff's Alpha of 0.84), we averaged scores to indicate the percentage of each color group. Using these average scores, we calculated the relative presence of red compared to other color groups by dividing the use of red by total coverage (percentage scores) of all color groups. We used the same procedure to measure the relative use of blue. We calculated the relative appearance of blue by dividing it by the total percentage of coverage by all color groups. A higher value reflected a greater use of blue compared to the use of other colors. 4.1.4. Control variables Because all new ventures competed for the same investment funds and were evaluated by judges encountering these ventures for the first time, our research design enabled us to control for the effects of social network and social capital of new ventures as well as investment levels (e.g., Hallen, 2008). The design also enabled us to mitigate the effect of geographical distance which might bias investment behavior (e.g., Sorenson and Stuart, 2008). We controlled for new venture characteristics that might be noticed by investors (e.g., Foo et al., 2005; Kirsch et al., 2009; Shepherd, 1999). We asked the research assistants to extract these from executive summaries. To ensure that all assistants had the same understanding of what different characteristics represented, the first author provided definitions of various venture characteristics obtained from past studies (e.g., Kirsch et al., 2009), and reviewed them with the research assistants. They were then asked to practice extracting these characteristics from 20 executive summaries whose teams had participated in the 2006 business plan competition for over a month. During this period, the research assistants and first author met on a weekly basis to discuss issues and clarify any confusion. Finally, the assistants extracted these characteristics from the executive summaries of new ventures who had participated in the 2007–2010 business plan competitions. The overall inter-rater reliability of the resulting coding was high (Krippendorff's Alpha of 0.91). We then averaged the assistants' independent scores to generate a final score for each of the following control variables. We controlled for venture age due to its perceived effect on investment decision as it may relate to its profitability or survival. We controlled for plan completeness by measuring the presence of eight elements—product/process description, target market/industry description, value proposition, competitive advantage, business stage, management team description, marketing plan, and financials and revenue model—in all executive summaries. This measure ranged from 0–8 in terms of structural conformity, and has been used to illustrate how new venture characteristics may serve a merely ceremonial function in influencing investment decisions (Kirsch et al., 2009). We controlled for the human capital of ventures' top management teams (TMT) as it may affect investment decisions (e.g., Foo et al., 2005; Shepherd, 1999). In order to control for the effects of education on investment decisions, we measured the proportions of team members who held a bachelor's degree, an MBA, or an advanced technology degree, as reported in executive summaries, as educational background conveys important information to investors (Matusik et al., 2008). We also counted the proportion of TMT members who had founded new ventures, the average number of companies where team members had worked, and the team size because those factors have shown to influence investment decisions (Kirsch et al., 2009; Matusik et al., 2008). We controlled for the human capital management that may influence investment decisions (e.g., Kirsch et al., 2009) using the following measures: the number of managerial roles TMT members had held, as reported in executive summaries for the following categories: Chairman, Chief Executive Officer, Chief Financial Officer, Chief Engineering Officer, corporate development, business administrator, human resources, sales, manufacturing, management and information systems, purchasing, quality control, research and Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


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development, sales, strategic planning, technology transfer, and others. Based on these, we calculated team specialization by dividing team size by the number of managerial roles. It is possible that investment by experienced investors could act as a proxy for confidence in the profitability of opportunities being pursued (Kirsch et al., 2009). Thus, we measured the level of prior investment made by real investors in new ventures. We also measured the investment amounts requested, because investors might use this as a proxy for the profitability of new ventures. We controlled for whether new ventures had obtained legal protection for their products or services by counting the number of patents granted, provisional patents filed, and patents pending. We then aggregated these numbers into one measure we called “legal protection”. Because it seemed plausible that the stage of product development could influence new venture investment decisions, we measured degree of product development using the following 5-point scale, 1 meaning that products or services were at the idea stage, 2 being products or services that have been fully developed conceptually, 3 representing prototypes of products or services, 4 meaning that products or services would be in production, and 5 for products or services that had already been purchased. We also considered the ways that the executive summaries were written by controlling for readability of executive summaries using the Flesch–Kincaid Grade Level scale and the frequent usage of positive affective words relative to negative affective words using General Inquirer, a free online content analysis program (http://webgi.stone-center.eu/index.php). Although there are a few studies investigating the effect of green on human behavior, they did not find a distinct effect (e.g., Elliot, Maier, Moller, Friedman and Meinhardt, 2007). Nonetheless, we controlled for the possible effect of green in our study. Finally, we counted the total number of words, tables, figures, and generic pictures presented and the number of distinguishable colors, including black and white, presented in the executive summaries. Because the distribution of some control variables was skewed, we transformed firm age, team size, prior investment, and requested amount by taking the natural log of (1 +) these variables. 4.1.5. Results Table 1 shows the mean, standard deviation, and correlation of variables. A number of variables were significantly correlated with others. We assessed possible multicollinearity using variance inflation factor and found no such problem (Kennedy, 2003). We used a standard hierarchical regression with group-mean centered variables to test the hypotheses. Table 2 presents the ordinary least square (OLS) results. We included all control variables in Model 1 with Favorability of Screening Decision as the dependent variable. Model 1 was statistically significant (R2 = 0.50, p b 0.001). Those ventures with a high proportion of TMT members with entrepreneurial experience received higher favorability (β = 0.22, p b 0.05), whereas those ventures whose TMT teams worked in a large number of companies received less investment (β = −0.18, p b 0.05). Moreover, ventures with later product development stages (β = 0.18, p b 0.05) or with larger number of patents (β = 0.21, p b 0.05) received higher favorability. Model 2 introduced the main effect of product image to test Hypothesis 1 (H1), which predicted a positive association between use of product image and favorability in screening decision. The addition of a product image made a significant impact over and above the previous model (ΔR2 = 0.02, p b 0.05), whereas the effects of all control variables were similar to Model 1. A significant and positive coefficient for product image was found (β = 0.18, p b 0.05) even after controlling for product development stage. Thus, H1 was supported. Model 3 introduced the main effect of red and blue to test Hypotheses 2 and 3, controlling for the effect of green. The addition of these color variables made a significant contribution beyond the prior model (ΔR2 = 0.03, p b 0.05). Similar effects were found for all control variables. A significant and negative coefficient for red (β = − 0.20, p b 0.05) was obtained. Thus, Hypothesis 2 (H2) was supported. However, Hypothesis 3 (H3), which predicts a positive association between the use of blue in business planning documents and greater investments, was not supported. We found a non-significant and negative coefficient for blue (β = − 0.09, p N 0.10). 4.1.6. Robustness tests To rule out alternative explanations for the operationalization of the dependent and explanatory variables (Table 3), we performed a number of robustness tests. Models 4 and 5 measure investment allocation using different proxies, i.e., the number of investors and investment amount divided by number of investors, respectively. Model 6 measures the presence of red and blue using a different proxy, i.e., simply indicating whether red or blue was used in executive summaries. Model 7 includes three other color groups: orange, yellow, and violet. Model 8 adds variables that reflect the functional role of color, i.e., whether red, blue, or green was used to indicate potential profit or loss of new ventures. Model 9 uses year indicators instead of a group-mean centering method to parse out year effects. In general, our results were consistent throughout. 4.1.7. Effect sizes from Study 1 The effect sizes of our study may appear small at a first glance, but they are qualitatively meaningful. Because Model 9 uses the raw score, instead of the group-mean scores, we use its results to estimate the effect size of key variables on total investment. One standard deviation increase in the use of product images resulted in an increase of 12% in invested virtual dollars, while one standard deviation increase in the use of red decreased 13% in invested virtual dollars. Further, we used Johnson's relative weights method to estimate the relative contributions of explanatory variables on the dependent variables (Johnson and LeBreton, 2004), to estimate the range of the contribution of visual cues on total investment. We found that visual cues Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

8


Table 1 Variables, descriptive statistics, and correlations.a Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26

Favorability of screening decision Firm age Plan completeness Bachelor degree MBA degree Advanced tech. degree Team size Entrepreneurial exper. Work experience Number of team roles Team specialization Prior investment b Requested amount b Product dev. stage Number of patents Number of words Readability Number of tables Number of figures Number of generic pix. Number of colors Positive tone Product image Red Green Blue

Mean b

S.D.

4.81 3.26 0.42 1.22 7.70 0.58 0.12 0.25 0.16 0.30 0.09 0.21 3.26 1.79 0.10 0.22 0.12 0.21 2.69 1.75 0.96 0.79 51 271 1313 2647 3.02 0.92 0.58 1.44 587 149 13.31 2.07 1.04 0.69 0.50 0.90 0.69 1.57 3.75 2.25 0.64 0.42 0.26 0.44 0.13 0.20 0.21 0.28 0.30 0.31

1

2

3

4

5

6

7

8

9

10

11

1.00 0.00 −0.01 0.19 −0.09 0.36 0.17 0.16 −0.07 0.18 0.05 0.17 0.10 0.09 0.39 0.12 0.24 0.04 −0.01 0.12 0.10 −0.07 0.29 −0.07 0.09 0.07

1.00 0.12 0.01 −0.08 −0.16 0.01 0.14 −0.03 −0.11 −0.11 0.10 −0.18 0.48 −0.08 0.12 −0.09 −0.01 −0.01 −0.05 −0.16 0.19 0.05 −0.05 −0.02 0.12

1.00 0.12 0.09 −0.01 0.38 −0.01 0.13 0.25 0.26 0.08 0.03 0.05 −0.18 0.36 −0.02 0.04 0.01 0.00 −0.06 −0.02 0.07 0.10 −0.08 −0.12

1.00 −0.07 0.03 0.13 0.21 0.30 0.19 0.01 0.04 0.07 −0.15 −0.02 0.17 −0.06 0.08 −0.04 −0.02 0.07 −0.03 0.05 −0.07 0.17 −0.06

1.00 0.19 0.17 0.06 0.10 0.01 −0.01 −0.11 0.10 −0.09 −0.10 0.12 0.08 0.08 −0.02 −0.12 −0.03 −0.06 −0.03 0.14 −0.19 0.06

1.00 0.19 0.04 −0.02 0.18 0.04 0.20 0.18 −0.07 0.26 0.06 0.13 0.03 −0.02 −0.01 0.03 −0.13 0.07 0.12 −0.14 0.18

1.00 0.09 0.18 0.52 0.46 0.19 0.09 −0.06 −0.07 0.09 0.16 0.09 0.13 −0.06 0.08 −0.02 0.04 0.06 −0.13 0.16

1.00 0.54 0.06 0.03 0.26 0.01 −0.07 0.05 0.21 0.03 −0.07 0.16 0.01 0.10 0.03 0.16 −0.10 −0.06 0.12

1.00 0.02 1.00 0.05 0.17 1.00 0.05 0.19 0.11 0.03 0.17 −0.02 −0.02 −0.14 −0.07 −0.00 0.07 −0.05 0.14 0.09 0.12 −0.03 0.05 0.06 0.05 0.04 0.09 0.06 0.19 0.01 −0.07 0.11 −0.03 0.08 0.15 0.07 0.00 −0.13 0.15 −0.07 0.10 0.07 0.03 0.10 0.05 −0.12 −0.06 −0.12 0.03 0.10 0.02

a All values greater than 0.14 or less than−0.14 are significant at 5% level. b Means and standard deviations in thousands.

explained 17%–21% of the R2 of our regression model. Thus, small differences in the use of visual cues resulted in meaningful outcomes. 4.1.8. Discussion of Study 1 As predicted, we found that the presence of product images led to higher favorability in screening decision. We also found that red in a business plan lowered favorability, but blue did not increase it. These results of Study 1 were generally supportive of our hypotheses. However, because we were unable to control all confounding factors, there are still other alternative explanations. For example, we did not take into account the interactions between investors and entrepreneurs during the Road Show rounds of competitions. It is plausible that these interactions could confound the effect of visual cues on venture screening decisions. Entrepreneurs who used images and/or colors in their plans could also be more interesting and exciting presenters who could better entice judges during their interactions than others. Thus, we conducted two controlled experiments in Study 2. 5. Study 2: controlled experiments We used between subject designs (Campbell and Stanley, 1966) in Study 2a and Study 2b to manipulate the focal variables of product image and colors and rule out extraneous factors. Participants for these studies were potential franchise investors who attended an international exposition on franchise businesses held in a major city in the East Coast of the United States. Many were actively seeking and evaluating franchise investment opportunities. During this event, the first author and a research assistant, stood at different locations (the main entrance and a break room) to recruit conference attendees. Using identical data collection protocols, we approached passing-by attendees to solicit their participation in a franchise investment decision study without providing incentives. Participants then received a one-page task evaluating a franchise and most spent no more than a minute on the task. By using a different screening decision context and a different type of participants, we sought to boost the generalizability of our findings. 5.1. Study 2a In Study 2a, we used a between subjects design to manipulate the focal variable of product image by randomly assigning participants to one of two conditions, control and image. Because product image might signal a business' preparedness, which has been Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


9

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

1.00 0.14 −0.12 0.25 0.07 0.00 −0.06 0.08 −0.01 0.01 0.04 0.03 −0.03 −0.10 0.06

1.00 −0.29 0.21 0.08 −0.01 0.08 0.16 −0.07 0.02 −0.12 0.01 0.18 −0.03 −0.10

1.00 −0.02 0.09 −0.06 0.04 −0.19 0.16 0.03 0.17 0.02 −0.07 0.09 −0.04

1.00 −0.02 0.11 −0.12 −0.03 .0.02 0.11 0.02 0.19 0.12 0.05 −0.06

1.00 −0.00 −0.05 −0.06 0.01 −0.09 0.15 0.08 0.13 −0.12 −0.02

1.00 −0/09 −/0.03 −0.16 −0.09 −0.12 −0.03 0.01 0.07 −0.02

1.00 −0.29 0.10 0.12 −0.12 −0.07 0.07 −0.06 0.10

1.00 0.08 0.20 −0.05 0.13 0.03 −0.03 −0.03

1.00 0.52 −0.15 0.23 0.00 0.01 −0.06

1.00 −0.06 0.41 −0.01 0.01 0.07

1.00 0.05 −0.11 −0.03 0.02

1.00 −0.01 −0.08 0.05

1.00 −0.17 −0.24

1.00 −0.37

1.00

found to influence investment decisions (Chen et al., 2009), it is likely that a product image could be picked up by analytical processing. We asked participants to evaluate both the expected profitability and preparedness of an investment opportunity to better understand the underlying mechanism of how product images influence screening decisions.

5.1.1. Procedure, sample, and material We recruited 54 participants who attended an exposition on franchise businesses for this study. We randomly distributed different versions of a one-page task, and briefly described the study and the scales used to evaluate new ventures. Participants often spent no more than a minute reading the decision task and evaluating a company's expected profitability and attractiveness. Twenty-six participants were assigned to the control condition while twenty-eight were assigned to the image condition. For both conditions, participants received a one page document containing identical content asking them to evaluate a franchise business as potential investors. This decision task is a modification of decision tasks used in prior venture investment decision studies (Shepherd, 1999; Shepherd et al., 2000). Participants first read a brief description of a food truck franchise business and then rated the expected profitability and preparedness of a franchise, using a 11-point Likert-type scale, anchored by the endpoints “Very Low Profitability/Very High Profitability” and “Very Unprepared/Very Prepared” respectively. These scales allowed us to measure two key components of screening decisions. For the image condition, participants received a one-page document that contained the same description and evaluation task, but included an image of a newly designed product at the top or at the bottom of the documents (http://www. fastcompany.com/1751577/redesigning-food-trucks). A post-hoc one-way analysis of variance test indicated no effect of the image position (F[1, 26] = 1.65, p = 0.21). We thus included different image position documents into the image condition. All documents are produced in black and white copies (Please see the Appendix A for a sample document used in the image condition) and black pens were supplied for the use by participants.

5.1.2. Analyses and results The Levene statistics (0.50, n.s.) suggested no violation of the homogeneity of variances assumption. Although participants in the image condition reported higher expected profitability (M = 7.14, SD = 2.08) than those in the control condition (M = 7, SD = 1.67), a one-way analysis of variance test indicated that the effect of image on the expected profitability of the franchise was not significant (F[1, 52] = 0.08, p = 0.78). Similarly, although participants in the image condition rated the franchise's preparedness more highly (M = 7.75, SD = 2.22) than those in the control condition (M = 7.38, SD = 1.81), a one-way analysis of variance test indicated that the effect of image on preparedness of franchise was not significant (F[1, 52] = 0.43, p = 0.51). Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

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Table 2 Multiple regression analyses: the effects of visual cues.a

In (firm age) Plan completeness Bachelor degree MBA degree Advanced technology degree In (team size) Entrepreneurial experience Work experience Number of team roles Team specialization In (prior investment) In (requested amount) Product development stage Legal protection Number of words Readability Number of tables Number of figures Number of generic pictures Number of colors Positive tone Green Product image Red Blue Industry dummies N F-statistic R square Adjusted R2 Change in R2

Model 1 DV: favorability of screening decision



−0.07 0.06 0.21 −0.15 0.18+ 0.14 0.22⁎ −0.18⁎ −0.06 −0.01 −0.05 0.06 0.18⁎ 0.21⁎

−0.08 0.04 0.19 −0.14 0.21+ 0.15 0.19+ −0.14 −0.06 −0.03 −0.04 0.05 0.16+ 0.19⁎⁎

−0.07 0.04 0.17 −0.12 0.23⁎

0.02 0.10 0.07 −0.05 0.14 −0.01 0.05 0.16+

0.02 0.11 0.10 −0.05 0.13 −0.08 0.04 0.16+ 0.18⁎

Yes 134 3.30⁎⁎⁎ 0.50 0.35

Yes 134 3.44⁎⁎⁎ 0.52 0.37 0.02⁎

0.16 0.17+ −0.13 −0.04 −0.03 −0.06 0.06 0.14 0.21⁎ 0.04 0.09 0.11 −0.07 0.13 −0.07 0.03 0.12 0.17⁎ −0.20⁎ −0.09 Yes 134 3.65⁎⁎⁎ 0.55 0.40 0.03⁎

a Standardized regression coefficients are reported in the table. One tailed tests were performed for independent variables while two tailed tests were performed otherwise. + p b 0.10. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

5.1.3. Discussion of Study 2a Study 2a did not replicate the effects of product image on screening decisions after controlling for extraneous factors. The inconsistent findings of product image in the two studies could be driven by alternative factors. One such factor could be the interactions between investors and entrepreneurs during the Road Show rounds of competitions. Could these factors also confound the effects of color on screening decisions? We conducted Study 2b to examine such a possibility. 5.2. Study 2b Study 2b considers the effects of color on screening decisions using a controlled experimental design. To rule out alternative explanations, we used a between subjects design and randomly assigned participants to three conditions of control, red and blue. Because colors might signal attractiveness in relationship contexts, we asked participants to evaluate attractiveness, in addition to expected profitability, of an investment opportunity to rule out color's effects on attractiveness in achievement context. 5.2.1. Procedure, sample, and material One hundred and twenty-three attendees in the same exposition participated in Study 2b. They were randomly assigned to three conditions—control, red, and blue—where 41 participants were assigned to the control condition, 40 were assigned to the red condition, and 42 were assigned to the blue condition. For all conditions, participants received a one page document containing an identical decision task modified based on decision tasks from prior new venture investment studies (Shepherd, 1999; Shepherd et al., 2000). Their one page documents differed in the color manipulations. Similar to prior studies (e.g., Elliot, Maier, Binser, Friedman, and Pekrun, 2009; Maier et al., 2008), this manipulation was done by presenting task material inside a colored rectangle. In the control condition, participants received a one-page investment decision task printed within a gray rectangle. The shade was selected using the hue–saturation–lightness (HSL) scheme (hue = 0, saturation = 0, lightness = 120). In the red condition, participants received a one-page document that presented the decision task inside a red rectangle (hue = 0, saturation = 240, lightness = 120). In the blue condition, the decision task was presented from inside a blue rectangle (hue = 160, saturation = 240, lightness = 120). (Please see Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


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Table 3 Robustness tests.a Model 4 Year 2008 Year 2009 Year 2010 In (firm age) Plan completeness Bachelor degree MBA degree Advanced technology degree In (team size) Entrepreneurial experience Work experience Number of team role Team specialization In (prior investment) In (requested amount) Product development stage Legal protection Number of words Readability Number of tables Number of figures Number of generic pictures Number of colors Positive tone Product image Green Red Blue Orange Yellow Violet Function of red Function of blue Function of green Industry dummies N F-statistic R square Adjusted R2

−0.07 0.07 0.14 −0.16 0.23⁎ 0.22⁎ 0.17+ −0.18⁎ −0.09 −0.05 −0.06 0.06 0.13 0.20⁎ 0.06 0.10 0.06 −0.09 0.11 −0.04 0.00 0.16⁎ 0.13 −0.15⁎ −0.08

Yes 134 3.14 ⁎⁎⁎ 0.51 0.35

Model 5

−0.01 −0.06 0.21 0.00 0.09 −0.01 0.11 0.05 0.01 −0.02 −0.02 0.13 0.16 0.10 0.03 0.09 0.07 0.01 0.09 −0.07 0.02 0.14 0.00 −0.25⁎ −0.18⁎

Yes 134 2.31⁎⁎ 0.43 0.25

Model 6

−0.05 0.00 0.24+ −0.15 0.17 0.16 0.18+ −0.13 −0.04 −0.02 −0.05 0.05 0.14 0.22⁎ 0.01 0.10 0.09 −0.05 0.13 0.09 0.06 0.18⁎ 0.07 −0.21⁎ −0.19⁎

Yes 134 3.56⁎⁎⁎ 0.54 0.39

Model 7

−0.08 0.03 0.15 −0.11 0.25⁎ 0.16 0.18+ −0.14 −0.04 −0.04 −0.06 0.06 0.15+ 0.20⁎ 0.05 0.09 0.10 −0.06 0.11 −0.01 0.04 0.15⁎ 0.06 −0.23⁎ −0.15 −0.01 −0.11 −0.06

Yes 134 3.35⁎⁎⁎ 0.55 0.39

Model 8

Model 9 0.08 0.11 0.17 −0.07 0.04 0.15+ −0.02 0.30⁎⁎ 0.15 0.19+ −0.16+ −0.05 −0.03 −0.07 0.06 0.12 0.23 ⁎⁎ 0.05 0.10 0.11 −0.06 0.13 −0.08 0.02 0.18⁎ 0.09 −0.19⁎

−0.07 0.04 0.18 −0.12 0.23⁎ 0.16 0.17+ −0.12 −0.04 −0.03 −0.06 0.07 0.15 0.21⁎ 0.05 0.09 0.09 −0.09 0.13 −0.07 0.02 0.17⁎ 0.11 −0.22⁎ −0.10

−0.08

−0.02 0.03 0.02 Yes 134 3.26⁎⁎⁎ 0.55 0.38

Yes 134 3.66 ⁎⁎⁎ 0.58 0.42

a Standardized regression coefficients are reported in the table. One tailed tests were performed for independent variables while two tailed tests were performed otherwise. + p b 0.10. ⁎ p b 0.05. ⁎⁎ p b 0.01. ⁎⁎⁎ p b 0.001.

Appendix B for a sample document from the gray condition). Instructions, task descriptions and evaluation criteria for all three conditions are presented in white color for easy reading. We supplied black pens for participants who typically spent less than one minute reading the task and evaluating a company's expected profitability and attractiveness. The decision task consisted of a description of a generic franchise business based on a modification of two investment criteria: resources and competition (Shepherd, 1999; Shepherd et al., 2000). Two different descriptions were randomly assigned to participants within each color condition. The first described a franchise facing intense competition and possessing considerable resources, whereas the second described a franchise facing little competition with few resources. A post-hoc one-way analysis of variance test indicated no effect of the franchise descriptions (F[1, 121] = 1.36, p = 0.25). We thus lumped samples into their associated color groups. After reading one of the descriptions, participants were asked to rate the expected profitability and attractiveness of a particular franchise using an 11-point Likert-type scale, anchored by the end points “Very Low Profitability/Very High Profitability” and “Very Unattractive and Very Attractive,” respectively. These measures allowed us to investigate the color effects on a key component of screening decision, i.e., profitability estimation, and rule out possible color effects on attractiveness in our achievement context. After completing this task, they were asked to take a short version of the Ishihara color deficiency test (adopted from http://www.toledo-bend.com/ colorblind/Ishihara.asp). Only a few declined to take the test and their results were not included in the final analysis. Responses from those who passed the test were included in the final analysis. 5.2.2. Analyses and results The Levene statistics (0.10, n.s.) indicates that the assumption of homogeneity for variances was not violated. A one-way analysis of variance test indicated that the effect of color on the expected profitability of the franchise was significant (F[2, 120] = 4.93, Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

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p b 0.01). We calculated the effect size of color using eta-squared (Cohen, 1988; Miles and Shevlin, 2001) and found that colors contributed to 7.59% of the total variance in profitability estimation. Participants from the blue condition rated expected profitability the highest (M = 7.10, SD = 1.96), followed by participants from the control condition (M = 6.56, SD = 1.86), and those from the red condition (M = 5.77, SD = 1.91). A planned contrast test was performed to compare the ratings of expected profitability for the control, blue, and red conditions. Using a one-tail test, the results supported the hypothesis that the red condition yielded lower ratings of expected profitability compared with the blue condition (mean difference = −1.32, t = −3.13, standard error = 0.42, p b 0.01), and the gray (control) condition (mean difference = −0.79, t = −1.85, standard error = 0.43, p b 0.05). However, there was no significant difference between the blue and gray conditions (p = 0.103), although participants from the red condition rated the attractiveness of a franchise business lower (M = 6.22, SD = 2.57) than those in the control condition (M = 6.80, SD = 2.39) and those in the blue condition (M = 6.74, SD = 2.21). A one-way analysis of variance test indicated that the effect of color on the attractiveness of a franchise was not significant (F[2, 120] = 0.71, p = 0.49). 5.2.3. Discussion of Study 2b The findings of Study 2b were consistent to those in Study 1. We found that red documents yield lower ratings of expected profitability, and blue documents did not influence profitability rating. Further, this study indicates that color does not significantly influence the attractiveness rating of a franchise business, supporting our assertion that in achievement context red carries negative connotation influencing mainly one's preference toward associated objects instead signaling attractiveness as illustrated in romantic contexts. 6. General discussion and conclusion We considered how two visual cues, product image and colors, influenced venture screening decisions. First, because visual images are more accessible, memorable, and influential compared to written information, we argued that the presence of product images results in higher favorability in screening decision compared with presentations without such images. However, we obtained mixed findings from our empirical tests. While the finding in Study 1 confirmed the proposed effect of product images, we failed to duplicate this effect in Study 2a. Our inconsistent findings suggest that perhaps the effect of product images in Study 1 may be due to confounding variables. For example, the use of product images could reflect the capability of entrepreneurs to entice investors, which may be related to the inherent quality of the venture. It is also possible that the investment opportunities in Study 2a may not have been very attractive, prompting investors to ignore product images as an important cue. These conflicting findings suggest that future studies could investigate plausible boundary conditions for the effect of product images. In contrast, the effects of red on screenings decision were consistent and robust across different contexts in our studies. This is perhaps because color, compared with other visual cues such as product images, affects our motivation and cognitive evaluation in a more automatic manner. The null effects of blue from Study 1 and Study 2b were somewhat surprising and inconsistent to prior literature (e.g., Middelstadt, 1990). One reason for this may stem from the influence of negativity bias, a well-documented tendency to perceive negative informational cues more strongly than positive ones (Baumeister et al., 2001). For example, decision makers could be more influenced by the fear of losing a hundred dollars than the prospect of gaining the same amount (Kahneman and Tversky, 1979). Thus, it is possible that the association between red and danger will be more likely to influence individuals making fast screening decisions compared to the linkage between blue and positive impression. 6.1. Theoretical contributions This paper contributes to entrepreneurship and managerial decision research by answering recent calls to renew the cognitive foundation of managerial decision-making (e.g., Hodgkinson and Healey, 2011; Hu et al., 2011; Levinthal, 2011; Powell et al., 2011). The traditional framework such as signaling theory has focused on how managers rationally make decisions using relevant informational cues, yet it has overlooked possible effects of less relevant visual cues that are not costly to obtain. Given the dominant role of heuristic processing influencing behavior, our study illustrates how visual cues influence people making speedy screening decisions. These screening decisions are ubiquitous in business settings as managers often make swift decisions on selecting a shortlist of strategic alliances, product development, investment opportunities, or competitors (e.g., Chen et al., 2007; Kirsch et al., 2009; Powell et al., 2011). Our study also contributes to venture investment research by considering an often overlooked stage of new venture investment decision process, i.e., screening decisions. Most prior studies have primarily focused on whether business plans significantly impacted the final funding decisions by angel investors and venture capitalists. However, these studies have often ignored that these investors go through a lengthy investment process where numerous factors (e.g., personal interactions with entrepreneurs, due diligence outcomes, and so on) other than business plans may play more important roles influencing their final decisions (e.g., Hall and Hofer, 1993; Kaplan and Strömberg, 2004). Isolating the screening stage, our study illustrates that executive summaries may significantly influence screening decisions through the usages of visual cues and venture characteristics in business plans. These effects on initial screening decisions are important because they determine the advancement to a later investment stage and the likelihood for receiving funding. Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002


13

This study also contributes to visual cue research by extending the generalizability of the effects of color to more complex tasks, i.e., investment decisions. Because prior research has primarily focused on how color affects decisions through simple psychological functions using tightly controlled experiments, it has been unclear whether such effects could be generalized to more complex tasks in realistic contexts (Elliot and Maier, 2014). Our study joins recent studies extending visual cue research to understand complex decisions (Bagchi and Cheema, 2013), illustrating that even in uncontrolled and naturally occurring context, i.e., business plan competitions, visual image and red significantly influence individuals making complex decisions. Judges evaluated multiple opportunities while processing a multitude of information related to color, visual images, and new venture characteristics. Our results provide additional support that in achievement context, red has negative connotation, resulting in less favorable investment decisions. Our results also illustrate the negativity bias (Baumeister et al., 2001) shown in the effect of colors on decisions involving complex tasks. 6.2. Practical implications In tandem with a lengthy evaluation process, screening decisions are not inconsequential, particularly for identifying potential investment opportunities (Hall and Hofer, 1993; Zacharakis and Shepherd, 2001). More than 80% of new ventures are excluded from further evaluation at this stage (Roberts, 1991). Entrepreneurs can thus use our findings to improve their chances of moving to the next round of evaluation by including product images and excluding the use of red in business plans. Venture investors may use our findings to avoid irrational decisions. Although generally unrelated to a venture's profitability, investment decisions may be influenced by visual cues associated with business plan documents. These effects often are subthreshold, i.e., individuals are unaware of these effects influencing their decisions (e.g., Nisbett and Wilson, 1977). However, it may be plausible that investors could avoid these visual cues biasing their decisions by being consciously aware of their presence. For example, a study showed that by simply asking participants about the current weather, the weather no longer influenced their assessment of general happiness and life satisfaction (Schwarz and Clore, 1983). Organizations, such as venture capital firms or corporate venture capital units of established firms, could provide employees training as a way of attenuating the effect of visual cues or even take more proactive measures, such as reproducing documents in black and white versions to facilitate employees making screening decisions. 6.3. Limitations and future research directions The limitations of this study provide future research opportunities. Although we found a positive effect of product images on screening decisions in Study 1, we did not duplicate that effect in Study 2a. This inconsistent finding suggests that product image might not be an effective visual cue influencing screening decisions. However, this further suggests that because we did not capture the interaction between entrepreneurs and investors, the significant effect in Study 1 may capture confounding variables, such as entrepreneurs' overall capability to entice investors in person. Future studies could collect data on more fine grained data on other aspects of ventures that may confound their tendency to use image and ultimate investment outcomes in real time (e.g., Parhankangas and Ehrlich, 2014; Uy et al., 2010; Welbourne et al., 2009) and relate how these factors may influence screening decisions. Further studies may corroborate the generalizability of our findings on colors in two ways. First, although our empirical settings did not allow us to capture actual investment decisions, they allowed us to capture important components of such screening process, i.e., screening investment decisions in Study 1 and profitability estimation in Study 2a and Study 2b. However, future studies could explore the generalizability of our findings using even more realistic investment decisions. Second, although our studies suggest that the use of red decreases favorable screening decisions for two different screening tasks, we caution against applying these findings to all decisions. One possible boundary condition is the stages of investment decisions. At later stages, the effects of visual cues could be attenuated by a lengthier and more analytical decision making process because investors would have access to additional information and have more time to analyze new ventures. Another boundary condition has to do with the limited implications of our study findings in settings outside the U.S. due to cross-cultural differences in color preference (Madden et al., 2000; Wiegersma and Van Der Elst, 1988). It is possible that the effect of color might be more salient or attenuated in other countries, calling for more research to test our hypotheses in different contexts to understand these boundary conditions. Finally, visual cues might not only directly affect decisions but might also interact with other factors affecting decisions. Red elicits avoidance motivation and enhances performance in detail-oriented tasks (Maier et al., 2008; Mehta and Zhu, 2009). It thus prompts people to be more attentive to negative informational cues compared to other colors. Indeed, a recent study finds that red, compared to gray, prompts people to be more responsive to loss-framed message (a negative informational cue) than to a gain framed message (a positive informational cue) (Gerend and Sias, 2009). Thus, scholars might want to investigate how visual cues could moderate the effect of various informational cues on various decisions. Also, not all investors may be influenced to the same degree by information embedded in business plans. Depending on their levels of education, experience, and values, they are likely to reach different conclusions. Investors and managers also differ in cognitive processing styles (e.g., Chan and Park, 2013), which could moderate their reliance on visual and informational cues to make decisions. Future Please cite this article as: Chan, C.S.R., Park, H.D., How images and color in business plans influence venture investment screening decisions, J. Bus. Venturing (2014), http://dx.doi.org/10.1016/j.jbusvent.2014.12.002

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studies could unpack how individual differences affect investor perception and cue interpretation from business plans (e.g., Shepherd, 2011). We applied a heuristic perspective to understand how visual cues affected venture investment screening decisions. We found partial support for a positive effect of product images on favorable screening decisions and consistent support for a negative effect of red on favorability of those decisions. We hope that by employing this framework, this paper can enhance our understanding of the impact of perceptual factors on strategic decisions.

Appendix A. A sample document for the image condition



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Appendix B. A sample document for the gray condition

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