1

THETHETHETHE INFLUENCEINFLUENCEINFLUENCEINFLUENCE OFOFOFOF DISPOSITIONALDISPOSITIONALDISPOSITIONALDISPOSITIONAL AFFECTAFFECTAFFECTAFFECT ANDANDANDAND COGNITIONCOGNITIONCOGNITIONCOGNITION ONONONONVENTUREVENTUREVENTUREVENTURE

INVESTMENTINVESTMENTINVESTMENTINVESTMENT PORTFOLIOPORTFOLIOPORTFOLIOPORTFOLIO CONCENTRATIONCONCENTRATIONCONCENTRATIONCONCENTRATION

Chien-ShengChien-ShengChien-ShengChien-Sheng RichardRichardRichardRichard ChanChanChanChan (Corresponding(Corresponding(Corresponding(Corresponding author)author)author)author)Peking University HSBC School of Business

University Town, Nanshan DistrictShenzhen, 518055 ChinaTel: 86-0755-2603-2179

Email: csrchan.pku@gmail.com

HaeminHaeminHaeminHaemin DennisDennisDennisDennis ParkParkParkParkBennett S. LeBow College of Business

Drexel UniversityPhiladelphia, PA 19104

andInstitute for Entrepreneurship and Innovation

Henry W. Bloch School of ManagementUniversity of Missouri – Kansas City

Kansas City, MO 64110Email: hdp323@gmail.com

We thank University of Washington Foster School of Business and Peking University HSBC

Business School for their financial supports. We thank the Center of Innovation and

Entrepreneurship at the University of Washington for their assistance in data-collection at the

Business Plan Competition. We also thank Warren Boeker, David Gomulya, Tori Yu-wen Huang,

Michael Johnson, and Terry Mitchell for their valuable comments on previous versions of this

paper. Our paper benefited greatly from Field Editor Michael Frese and four anonymous

reviewers.

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THETHETHETHE INFLUENCEINFLUENCEINFLUENCEINFLUENCE OFOFOFOF DISPOSITIONALDISPOSITIONALDISPOSITIONALDISPOSITIONAL AFFECTAFFECTAFFECTAFFECT ANDANDANDAND COGNITIONCOGNITIONCOGNITIONCOGNITIONONONONONVENTUREVENTUREVENTUREVENTURE INVESTMENTINVESTMENTINVESTMENTINVESTMENT PORTFOLIOPORTFOLIOPORTFOLIOPORTFOLIO CONCENTRATIONCONCENTRATIONCONCENTRATIONCONCENTRATION

ABSTRACTABSTRACTABSTRACTABSTRACT

We explore how an investor’s dispositional affect and cognitive style influence venture

investment portfolio concentration. Based on a field study using a sample of 128 judges from a

business plan competition, we find that high positive affectivity investors construct more

concentrated investment portfolios than their low positive affectivity counterparts, whereas high

negative affectivity investors construct more diversified investment portfolios than their low

negative affectivity counterparts. Further, investors who rely on analytical decision making

display a weaker relationship between negative affectivity and investment diversification

whereas investors who rely on emotion-based decision making display a stronger relationship

between positive affectivity and investment concentration.

Key words: venture investment decisions, dispositional affect, cognition

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THETHETHETHE INFLUENCEINFLUENCEINFLUENCEINFLUENCE OFOFOFOF DISPOSITIONALDISPOSITIONALDISPOSITIONALDISPOSITIONAL AFFECTAFFECTAFFECTAFFECT ANDANDANDAND COGNITIONCOGNITIONCOGNITIONCOGNITIONONONONONVENTUREVENTUREVENTUREVENTURE INVESTMENTINVESTMENTINVESTMENTINVESTMENT PORTFOLIOPORTFOLIOPORTFOLIOPORTFOLIO CONCENTRATIONCONCENTRATIONCONCENTRATIONCONCENTRATION

2.2.2.2. IntroductionIntroductionIntroductionIntroduction

Prior studies on venture investment decisions have typically taken two approaches. A

first set of studies take venture opportunities as a unit of analysis and find that an investor’s use

of heuristics1, elicited by such factors as confidence level, personal background, and personal

relationships, influence venture evaluations (e.g., De Clercq and Sapienza, 2006; Zacharakis and

Shepherd, 2001). Despite their valuable insights, these studies overlook the inter-dependencies

among evaluations of concurrent opportunities. In real life, venture investors typically evaluate

multiple opportunities over a short period of time and allocate limited resources to a few selected

ventures (Zacharakis and Meyer, 2000; Zacharakis and Shepherd, 2001).

Another set of studies take an investment firm’s portfolio as a unit of analysis and find

that investment firm characteristics such as experience, expertise, and stage-focus influence the

composition of investment portfolios (Dimov et al., 2007; Gupta and Sapienza, 1992; Han, 2009).

A firm’s investment portfolio, however, is the result of complex social interactions whereby

multiple actors make collective decisions over a long period of time (e.g., Patzelt et al., 2009).

As a result, the findings from these studies cannot be easily applied to the formation of an

individual investor’s portfolio. In many contexts, it is the individuals (e.g., angel investors or

managers of venture investment firms) who make venture investment decisions.

In this study, we link individual investors to their investment behavior by exploring the

influence of an investor’s dispositional affect, an individual propensity to experience specific

types of mood or emotions (Watson and Tellegen, 1999), and cognitive underpinnings on

1 We define heuristics as simple mental shortcuts for making judgment and decisions that may lead to accurate orinaccurate outcomes (Kahneman and Klein, 2009; Tversky and Kahneman, 1974).

4

venture investment concentration. Based on the influence of dispositional affect on cognitive

process and information evaluation, we first illustrate that high positive affectivity investors tend

to concentrate their portfolio compared with low positive affectivity investors. We then show

that high negative affectivity investors tend to diversify their portfolio compared with low

negative affectivity investors.

Moreover, individuals differ in cognitive style, i.e., the way they process and utilize

information (e.g., Allinson and Hayes, 1996). Such a difference may influence investment

behavior and moderate the relationship between dispositional affect and investment

concentration. A well-documented cognitive style is the need for cognition, reflecting an

individual’s propensity to enjoy and engage in thinking and analytical decision making

(Cacioppo and Petty, 1982). Investors with a high need for cognition are more likely to evaluate

the full set of investment opportunities. Thus, we argue that such investors are more likely to

diversify their investment portfolio compared with investors with a low need for cognition.

Further, we argue varying propensities for employing cognition or emotion in decision making

(Barchard, 2001; Cacioppo and Petty, 1982) will moderate the relationship between dispositional

affect and investment concentration, such that the relationship becomes weaker for those with a

high need for cognition and stronger for those who rely on their emotions when making

decisions. Using data from a business plan competition at a major university in the U.S., our

results generally support our predictions.

This study provides several insights. First, it contributes to venture investment decision

research by exploring factors influencing an investor’s portfolio composition. Prior studies have

focused on factors that influence either independent evaluations of venture investment

opportunities (e.g., De Clercq and Sapienza, 2006; Shepherd, 1999) or a firm’s investment

5

portfolio composition (e.g., Dimov et al., 2007; Patzelt et al., 2009). However, because real life

venture investors often concurrently evaluate multiple investment opportunities (Zacharakis and

Shepherd, 2001), our study provides insights on factors influencing how individual investors

construct their investment portfolio.

Further, responding to the recent call for incorporating affect research in entrepreneurship

(Baron, 2008, Foo, 2011), our study illustrates how dispositional affect can influence investment

concentration and explain its boundary conditions. Our work also contributes to understanding

how investment decisions can be predicted using theoretically relevant personality traits such as

dispositional affect, need for cognition, and emotion-based decision making. Moreover, this

study sheds light on dispositional affect research by showing how dispositional affect can

influence investment behavior in a complex manner and that its impact is bounded by individual

propensity for using cognition or emotion when making decisions.

We first review prior studies on venture investment decisions and establish the role of

affect. Next, we present hypotheses on the influence of dispositional affect on venture

investment portfolio concentration. We describe the dual process perspective and propose a

relationship between need for cognition and portfolio concentration. We then illuminate how an

investor’s propensities for relying on either cognition or emotion moderates the relationship

between dispositional affect and portfolio concentration, and follow this with a description of our

methods and presentation of our results. We conclude by discussing the implications, limitations,

and future extensions of our work.

3.3.3.3. TheoryTheoryTheoryTheory andandandand hypothesishypothesishypothesishypothesis

3.1. Venture investment decisions

6

Earlier studies on venture investment decisions found that those decisions often result

from multiple, lengthy evaluations based on several criteria (e.g., Hall and Hofer, 1993;

Shepherd, 1999). More recent studies have applied cognitive psychology (e.g., Tversky and

Kahneman, 1973) to explore how heuristics influence venture investment evaluations.

Researchers have found that investor attributes such as overconfidence (Zacharakis and

Shepherd, 2001), similar training and work experience with venture team members (Franke et al.,

2006), and personal relationships with venture teams (De Clercq and Sapienza, 2006) influence

investment evaluations and decisions.

Despite their valuable insights, these studies have limited application for understanding

venture investment decision making, because they typically rely on independent investment

evaluations as a unit of analysis. The process of evaluating venture investment opportunities is

complex and iterative (e.g., Hall and Hofer, 1993; Petty and Gruber, 2011). Investors typically

evaluate multiple opportunities over a short period of time, allocating limited investment

resources to a few selected ventures (Zacharakis and Meyer, 2000; Zacharakis and Shepherd,

2001). However, these studies provide limited insights on how investments are allocated across

multiple opportunities.

Others explored how venture capital firms construct investment portfolios (e.g., Gupta

and Sapienza, 1992). These studies typically focus on the influence of the firm and fund

characteristics on firm-level portfolio composition. For example, venture capital firms with

expertise in finance or entrepreneurial experience generally prefer early stage investments

(Dimov et al., 2007; Patzelt et al., 2009), whereas firms that do not focus on early stage ventures

or manage larger funds tend toward diversification (Gupta and Sapienza, 1992; Han, 2009).

However, firm-level portfolio composition may be the result of complex social interactions,

7

where multiple actors make collective decisions over a long period of time (e.g., Dimov et al.,

2007). Thus, findings of these studies have limited usefulness to understand the formation of an

individual investor’s portfolio. Because many venture investment decisions are made by

individual investors (e.g., angel investors or managers of venture capital funds), understanding

what influences individual investment portfolio construction can provide insights into overall

venture investment decision making.

A traditional view in finance assumes investor rationality and suggests a two-stage

prescription for optimal investment allocation (e.g., Brealey et al., 2007). Investors are first

advised to evaluate each venture independently, using valuation methods such as discounted cash

flow methods to calculate the net present value for a particular venture (Crane and Reinbergs,

2001; Lerner and Willinge, 1996). They are then prescribed to allocate their investments

according to expected net present value of each investment and to diversify their portfolio to

mitigate risk (Brealey et al., 2007).

Although these prescriptions may appear rational, they are unrealistic given the limited

cognitive capacity for processing information and solving problems of human beings. Instead of

finding the best solution, people often settle for a “satisficing” solution, i.e., one that meets a

particular criterion adequately (Simon, 1957). In addition, they often rely on heuristics decision

making (e.g., Tversky and Kahneman, 1973), a tendency that has been documented in venture

investment decisions (De Clercq and Sapienza, 2006; Franke et al., 2006; Zacharakis and

Shepherd, 2001). Indeed, individuals substantially differ in the extent to which they rely on

heuristics decision making based on their traits such as dispositional affect and cognitive style

(e.g., Ambady and Gray, 2002; Epstein et al., 1996). We explore how such traits influence

investment decision making. Dispositional affect reflects an individual’s general propensity for

8

certain affective experiences that persist across time and context (Larsen and Ketelaar, 1989;

Watson et al., 1988). Because these affective experiences guide current and subsequent behavior

in a rapid and effective manner (Cohen, 2005; LeDoux, 1996; Lerner and Keltner, 2001; Slovic

et al., 2002), we first delineate how dispositional affect influences investment portfolio

construction.

3.2. Hypotheses development

3.2.1. Dispositional affect

Following prior studies (e.g., Barsade et al., 2003), we categorize dispositional affect by

using positive/negative activation approach suggesting that dispositional affect is often

characterized by a two-dimensional structural model (Watson and Tellegen, 1999). The first

dimension is pleasantness, ranging from high to low, whereas the second dimension is activation

energy, also ranging from high to low. Based on this model, dispositional affect can be

conceptualized as two dispositional affect: positive affectivity (or positively activated) and

negative affectivity (or negatively activated) (Watson et al., 1988; Watson and Tellegen, 1999).

Positive affectivity refers to an individual’s propensity to experience positive emotion or

mood (e.g., happiness) across situations and time (Watson et al., 1988). High positive affectivity

individuals tend to feel cheerful, experience positive self-concept, be more sensitive, and

overestimate the occurrence of positive stimuli (Hullett, 2005; Larsen, 1992). In the absence of

triggering events or stimuli, positive affectivity will predispose people toward a positive mood or

emotion (Watson, 2000). Negative affectivity refers to an individual’s propensity to experience a

negative emotion or mood (e.g., sadness) across situations and time (Watson, 2000; Watson et al.,

1988, 1999). High negative affectivity individuals tend to ruminate over their failures and

weaknesses, experience emotional distress, and harbor negative self-concepts (Watson and Slack,

9

1993). They are likely to be sensitive, overestimate the occurrence of negative stimuli, and

experience negative emotion or mood in the absence of any triggering event (Larsen, 1992;

Watson, 2000). These two traits are unique constructs and conceptually independent. For

instance, low positive affectivity does not suggest high negative affectivity or vice versa. They

are also factorially independent in that they range along the bipolar dimensions of pleasantness

and activation (e.g., Watson et al., 1999). Empirically, they are weakly correlated (Connolly and

Viswesvaran, 2000; Watson and Tellegen, 1999) and independently influence behavior (Ng and

Sorensen, 2009; Thoresen et al., 2003). Thus, we factor into our study the influence of these

traits on investment portfolio concentration.

3.2.2. The influence of positive affectivity

High positive affectivity investors are likely to experience positive mood and emotion

throughout the portfolio construction process compared with low positive affectivity investors.

Initially, high positive affectivity investors are more likely to experience a positive mood even in

the absence of stimuli (Watson, 2000). This affective experience influences subsequent judgment

and decision making (Lerner and Keltner, 2000, 2001; Tiedens and Linton, 2001), prompting

such investors to notice positive stimuli associated with their tasks (Larsen, 1992), resulting in a

more frequent experience of positive mood and emotion. As a result, individuals sharing a

particular dispositional affect display expressive and physiological profiles similar to those

having a momentary affective experience (e.g., Lazarus, 1991), often resulting in similar effects

of dispositional and state affect, e.g., emotion and mood in attitudes and behaviors across

situations (e.g., Baron, 2008; Foo, 2011; Lerner and Keltner, 2001; Thoresen et al., 2003). Thus,

high positive affectivity individuals are more likely to be influenced by positive mood and

emotion (e.g., Ambady and Gray, 2002; Forgas, 1994) when they make investment decisions.

10

Positive mood and emotion influence two specific behaviors, i.e., cognitive process and

information evaluation (e.g., Ambady and Gray, 2002; Forgas, 1994). First, individuals

experiencing positive mood and emotion mainly rely on heuristic processing because positive

mood and emotion often signal that their environment is safe. Hence those individuals do not

need to utilize cognitive processes (Schwarz, 1990; Schwarz and Bless, 1991). Empirically,

positive mood and emotion often reduce search effort on task-relevant information (Bless and

Schwarz, 1999; Martin et al., 1993) and the use of structural protocol in complex decisions (e.g.,

Elsbach and Barr, 1999). Further, positive mood and emotion trigger stereotypic thinking (e.g.,

Bodenhausen et al., 1994) and heuristic-based decision making (e.g., Gasper, 2003; Hullett, 2005;

Kaufmann and Vosburg, 1997). Second, because mood and emotion are often used consciously

and unconsciously to evaluate encountered objects, positive mood and emotion can influence the

valence of information that individuals approach, evaluate, and react to information in the

environment (e.g., Bower, 1981; Isen, 1984; Larsen, 1992; Watson et al., 1988). Individuals with

positive mood and emotion tend to approach and evaluate information positively (e.g., Hullet,

2005, Nygren et al., 1996; Wright and Bower, 1992), and make optimistic judgments across

various situations (Johnson and Tversky, 1983; Nygren et al., 1996; Wright and Bower, 1992).

Further, similar effects have been demonstrated for dispositional affect. Meta -analyses show

that high positive affectivity individuals report greater levels of satisfaction regarding different

aspects of life and work compared with low positive affectivity individuals (Ng, and Sorensen,

2009; Thoresen et al., 2003; Watson et al., 1988).

Both effects of positive mood and emotion experienced may jointly influence investment

concentration. Because high positive affectivity investors are more likely to experience positive

mood and emotion, triggering higher reliance on heuristic-based processing (e.g., Hullett, 2005;

11

Schwarz and Bless, 1991) and reduction of information search efforts (Martin et al., 1993), these

investors prefer to construct their portfolios in a less analytical manner compared with low

positive affectivity investors. Instead of the two-stage strategy prescribed by finance scholars,

high positive affectivity investors are more likely to simultaneously evaluate and allocate their

investments in terms of a particular opportunity. Because high positive affectivity investors are

more likely to experience positive mood and emotion, triggering higher tendency to respond to

positive information and evaluate any encountered opportunity more positively (e.g., Hullet,

2005, Nygren et al., 1996), they are likely to overestimate the profitability of that particular

opportunity. Their reduced information search efforts are likely to stop them from searching

additional opportunities once they are satisfied with their selected investments, whereas their

heuristic-based processing may make them to overlook the prescribed risk mitigation strategy,

i.e., portfolio diversification. As a result, high positive affectivity investors, compared with low

positive affectivity counterparts, evaluate fewer opportunities and allocate greater investments to

selected opportunities, resulting in a more concentrated portfolio.

Indeed, even if high positive affectivity investors followed the prescribed two-stage

strategy for constructing an investment portfolio, the joint influence of mood and emotion effects

is likely to result in more concentrated investment portfolios. In the first stage, high positive

affectivity investors are more likely to use heuristics, such as eliminating opportunities that is

diagnosed with a fatal flaw (Maxwell et al., 2011) or selecting opportunities with a higher value

in terms of a single criterion (Gigerenzer and Goldstein, 1996; Graefe and Armstrong, 2011), to

arrive at a manageable set of opportunities. These approaches are likely to result in a smaller set

of opportunities included for the later stage of analysis.

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In the second stage, high positive affectivity investors will pay greater attention to

positive cues, i.e., the strength of new ventures rather than negative ones, and make optimistic

judgments (e.g., Johnson and Tversky, 1983; Larsen, 1992). These investors are likely to

overestimate the profitability of their selected ventures among the smaller set opportunities

resulting from the first stage. Further, their optimistic tendency elicited by positive affectivity

prompts them to allocate larger amount to a few ventures and their heuristic-based processing

prompts them to ignore the prescribed risk mitigating strategy, i.e., diversification. Given the

combined effects of a smaller set of opportunities and a greater allocation to selected investments,

high positive affectivity investors will devote a larger amount of their investments to a smaller

set of opportunities compared with low positive affectivity counterparts, resulting in a greater

concentration in their portfolios. Thus,

Hypothesis 1: High positive affectivity investors are more likely to concentrate theirinvestments in a smaller number of ventures compared with low positive affectivityinvestors.

3.2.3. The influence of negative affectivity

High negative affectivity investors are likely to experience negative mood or emotion

even in the absence of stimuli compared with low negative affectivity investors (Watson, 2000).

Negative affectivity prompts these individuals to approach and attend to negative stimuli in the

environment (Larsen, 1992), resulting in more frequent experience of negative mood and

emotion. This experience of negative mood and emotion is also likely to influence cognitive

process and information evaluation. First, individuals with negative mood and emotion may feel

that the environment is dangerous and action or analysis is needed (Schwarz, 1980; Schwarz and

Bless, 1991). Empirically, individuals with negative mood and emotion tend to be more

analytical in processing information (e.g., Frijda, 1988; Gasper, 2003; Kaufmann and Vosburg,

13

1997), spend more time searching for task-relevant information (Martin et al., 1993), be less

likely to rely on stereotypes (Bodenhausen et al., 1994), focus on specific details of a persuasive

message and be influenced by strong arguments (Hullett, 2005), and follow a structured protocol

when making complex decisions (Elsbach and Barr, 1999). Second, negative mood and emotion

make individuals approach, evaluate, and react to information in the environment negatively (e.g.,

Larsen, 1992; Watson et al., 1988; Schwarz and Bless, 1991). Studies have found that

individuals with high negative mood and emotion, compared with those having low negative

mood and emotion, are pessimistic across various scenarios (e.g., Johnson and Tversky, 1983;

Wright and Bower, 1992). Meta analyses suggest that similar effect has been extend to

understand the influence of negative affectivity, i.e., high negative affectivity individuals report

lower levels of satisfaction regarding different aspects of life and work compared with their low

negative affectivity counterparts (Ng, and Sorensen, 2009; Thoresen et al., 2003).

Both effects of negative mood and emotion jointly influence how high negative

affectivity investors construct their portfolios. Because frequent experience of negative mood

and emotion triggers the use of analytical processes and structure protocols when making

decisions (e.g., Elsbach and Barr, 1999; Frijda, 1988), high negative affectivity investors are

more likely to follow the two-stage strategy prescribed by experts compared with low negative

affectivity investors. In the first stage, high negative affectivity investors are more likely to

systematically evaluate all investment opportunities using multiple criteria and evaluation

methods (e.g., discounted cash flow evaluation) compared with their low negative affectivity

counterparts. Because negative mood and emotion, experienced frequently by high negative

affectivity investors, increase search effort on task-relevant information (e.g., Martin et al., 1993),

these investors are likely to spend greater effort in searching for more investment opportuntities,

14

repeat the evaluation process for these opportunities, and carefully record all expected net

present values and associated analyses. As a result, they are more likely to comprehensively

evaluate all investment opportunities compared with their low negative affectivity counterparts.

In the second stage, the use of analytical processes by high negative affectivity investors

motivate them to compare expected net present values for all opportunities to determine how to

allocate their investments compared with low negative affectivity investors. Because high

negative affectivity individuals have pessimistic outlooks and attitudes (e.g., Johnson and

Tversky, 1983; Thoresen et al., 2003), those investors are more likely to underestimate the

probability of favorable outcomes and overestimate unfavorable outcomes for potential

investment opportunities. Further, their use of analytical processes motivates them to note and

implement the prescribed risk mitigating strategy, i.e., diversification. As a result, high negative

affectivity investors may hesitate to invest large amounts of money in a few new ventures but

rather spread their funds over a larger set of opportunities. Thus, high negative affectivitiy

investors will be more likely to diversify their investment portfolio compared with low negative

affectivity investors.

Hypothesis 2: High negative affectivity investors are more likely to diversify theirinvestments across a larger number of ventures compared with low negativeaffectivity investors.2

3.2.4. Influence of the need for cognition

Although dispositional affect can influence behavior, it is not the only factor shaping

behavior. Indeed, human behavior is co-determined by two types of cognitive process (Evans,

2 We derive H1 and H2 differently and independently. We use two scenarios, i.e., simultaneous evaluation ofand resource allocation on a particular venture and the finance prescribed two-stage strategy, to derive H1whereas we only use the two-stage strategy to delineate H2. Nevertheless, affect research has often found thatpositive affect and negative affect influences behavior in the opposite manners. As a result of using thesefindings, we may have appeared to derive our arguments for positive affectivity and negative affectivityoppositely.

15

2008; Kahneman, 2003; Kahneman and Klein, 2009). System 13 processing operates in a rapid,

implicit, automatic, and nearly effortless manner, whereas System 2 processing operates in a

slow, explicit, controlled, and effortful manner (Evans, 2008; Kahneman, 2003; Kahneman and

Klein, 2009). Because individuals have different cognitive style (Allinson and Hayes, 1996;

Epstein et al., 1996), some enjoy thinking (Cacioppo and Petty, 1982; Chatterjee et al., 2000;

Smith and Levin, 1996), whereas others may be cognitive "misers" preferring to use shortcuts in

order to make decisions (Taylor, 1981).

The need for cognition is a well-documented cognitive style construct that reflects an

individual’s propensity to engage in and enjoy thinking, which is a System 2 process (Cacioppo

and Petty, 1982; Epstein et al., 1996). This construct has been shown to be reliable and stable

across time and contexts (e.g., Cacioppo and Petty, 1982). For example, individuals with a high

need for cognition are better at retaining information (Boehm, 1994) and more likely to search

out new information (Cacioppo et al., 1996). In an argument, they are more likely to be

influenced by a relevant message (Cacioppo et al., 1983) and less likely to be influenced by

irrelevant information such as endorser appearance (Haugtvedt et al., 1992) or use of humor

(Zhang, 1996).

Because the need for cognition triggers the use of analytical process, it can directly shape

investment portfolio composition. Investors with a high need for cognition tend to engage in

thinking and attend to relevant facts for their decision tasks (e.g., Zhang, 1996). They will thus

more likely use the systematic approach prescribed by finance scholars when evaluating

investment opportunities and will keep a comprehensive record of expected net present values of

3Different terms, such as automatic evaluation versus conscious judgments (Bargh and Chartrand, 1999), heuristicversus analytic (Evans, 2006), and experiential versus rational (Pacini and Epstein, 1999), have been proposed todescribe these two processes; however, we have adopted the popular terminology of System 1 and System 2 used inrecent studies (Evans, 2008; Kahneman, 2003; Kahneman and Klein, 2009).

16

all investment opportunities. Thus, investors with a high need for cognition will generally assess

a larger set of opportunities to determine investment allocations, and are more likely to diversify

their investments compared with those with a low need for cognition.

Hypothesis 3: Investors with a high need for cognition are more likely to diversifytheir investments across a larger number of ventures compared with investors with alow need for cognition.

Further, the need for cognition may weaken the influence of affective experience on

behavior. There is some indirect evidence showing how the influence of mood and emotion on

behavior can be temporarily attenuated by increasing an individual’s motivation to deliberately

process information or by increasing their processing capability (e.g., Ambady and Gray, 2002;

Bless et al., 1996). The need for cognition can also reduce affect-induced changes in behavior,

such as the tendency to recall mood-congruent information (e.g., positive mood matched with

positive framing) (Kuvaas and Kaufmann, 2004).

We argue that the need for cognition attenuates the influence of dispositional affect on

investment portfolio composition. Because individuals with a high need for cognition tend to be

motivated to consider task-relevant informational cues and ignore task-irrelevant information

(Cacioppo et al., 1983; Haugtvedt et al., 1992; Zhang, 1996), they are less motivated to respond

emotionally to stimuli in the environment, resulting in diminishing the influence of dispositional

affect on frequent experience of certain type of mood and emotion. As a result, investors with a

high need for cognition are less likely influenced by the effects of their affective experience.

Such mechanisms attenuate the influence of dispositional affect on investment concentration.

Thus, we expect that the need for cognition will weaken the relationship between dispositional

affect and investment concentration.

Hypothesis 4: A need for cognition moderates the relationship between dispositionalaffect and investment decisions such that the greater the need for cognition, the

17

weaker will be the relationship between dispositional affect and investmentconcentration.

3.2.5. The influence of emotion-based decision making

When individuals are motivated toward affective events, they are more likely to be

influenced by their affective experience when making decisions (e.g., Maio and Esses, 2001).

Emotion-based decision making is a cognitive style variable that captures the degree to which an

individual relies on mood and emotion to process information and make decisions (Barchard,

2001; Evans and Barchard, 2005).4

We argue that emotion-based decision making strengthens the relationship between

dispositional affect and investment portfolio concentration. Because high emotion-based decision

making investors tend to rely on mood and emotion in making decisions, they are motivated to

attend and approach emotional stimuli in the environment compared with investors without such

a preference. As a result, high emotion-based decision making investors will frequently

experience the same types of mood and emotion, elicited by their dispositional affect. Further,

investors who make decisions based on their emotions will pay more attention to their affective

experiences and be influenced by associated effects. Taken together, we argue that high emotion-

based decision making investors will be more influenced by dispositional affect when

constructing investment portfolios compared with low emotion-based decision making investors.

Hypothesis 5: Emotion-based decision making moderates the relationship betweendispositional affect and investment decisions such that the greater the Emotion baseddecision making, the stronger will be the relationship between dispositional affectand investment concentration.

4.4.4.4. MethodsMethodsMethodsMethods

4.1. Setting and procedures

4Because this construct does not associate the underlying mechanisms we used to depict how dispositional affectinfluences investment portfolio construction. it has no direct influence on investment portfolio construction; Thus,we do not hypothesize any main effect of Emotion Based Decision Making on investment portfolio construction.

18

Each year the entrepreneurship center at a major university in the western United States

hosts a business plan competition. Venture teams comprised of student organized teams to actual

startup firms are evaluated through several rounds. Our study used the “road show” round; this is

held in a large room where venture teams are assigned to stations where they can display and

pitch their products or ideas to judges. Judging the competition are prominent members of the

local entrepreneurial community (e.g., entrepreneurs, venture capitalists, lawyers, etc.) recruited

months before the competition who have received the business plans of participating ventures a

few days before the event.

During the road show round, each judge is given an imaginary fund of $1,000 to invest in

ventures they consider the most viable. Judges have four hours to decide how to allocate their

funds. They typically walk from station to station and interact with team members before

deciding how to make their investments. Most judges complete their allocations within two and

half hours, spending three to seven minutes on each business opportunity.

In the round we observed, there were variations in how judges interacted with the venture

teams. Some took careful written notes, whereas others simply kept mental track. Some allocated

a specific value to each new venture after their visit, whereas others made all their allocation

decisions at the end. Once allocations were tallied at the end of the round, 16 teams that received

the most investment funds advanced to the next round.

To test our hypotheses, we designed a two-stage study. In Stage 1, two weeks prior to the

road show round, we asked the judges to complete an online survey about positive and negative

affectivities, emotion-based decision making, need for cognition, and other possible confounding

factors. This was done to control for effects of other variables. In Stage 2, following the road

show round, we collected each judge’s investment allocations.

19

This design combined features of a laboratory experiment and a field study, giving it

several advantages. First, compared with a laboratory experiment, this design produced more

generalizable results because the judges made investment decisions in a realistic and meaningful

context. Not only were they motivated to do their best but also they were less aware of being

studied (Sackett and Larson, 1990). Second, compared with a field survey, this design allowed us

to systematically control variables that may influence investment concentration. Specifically, all

participants encountered the same set of venture investment opportunities with the same number

of imaginary dollars, effectively controlling for opportunity and resource sets that may influence

judging behavior (Han, 2009) and allowing us to attenuate threats to internal validity that most

field surveys would have encountered. Most importantly, the design provides means to reduce

common method bias by temporally and methodologically separating collection of measurements

(Podsakoff et al., 2003). Specifically, data for our explanatory variables were collected two

weeks prior to those for our dependent variable. Surveys were used to capture our explanatory

variable data, whereas actual investment decisions were collected for our dependent variable.

Such temporal and methodological separation of measuring explanatory and dependent variables

can reduce common method bias caused by contextual effects, consistency motifs, and demand

characteristics (Podsakoff et al., 2003, p. 888).

4.2. Sample

Over a two-year period (2008–2009), survey invitations were emailed to the 331 judges

who participated in the business plan competition. In 2008, 33 ventures participated in this round

and another 34 ventures participated in 2009. In total, 148 judges (45%) completed the online

survey. We examined non-response bias by analyzing the mean and variance differences of the

dependent variable, Investment Concentration, between responding and non-responding groups.

20

We found that the variance of Investment Concentration between the responding group and the

non-responding group was similar (F = 1.506; p > 0.05). Thus, we assumed equal variances and

made independently sampled t-tests and found that the two groups did not have different means

for Investment Concentration (t = -1.272; p > 0.05). We also tested whether judges with different

types of work experience allocated investments differently, but found no significant differences

in Investment Concentration for the most common occupations, i.e., venture capitalist (t = 0.030;

p > 0.05) and attorney (t = 0.455; p > 0.05).

Of those who responded to our survey, 117 judges were male and 31 judges were female.

Ages ranged from 23 to 73 years, with a mean of 42.63 (SD = 10.68) years. The racial

breakdown for respondents was 4.7% Asian, 84.1% European American, 3.4% Hispanic

American, and 7.5% other ethnicities. Most planned to start a new business (79.1%), whereas

about half had founded a business that was still in operation (50.0%). This is consistent with

prior findings that many new venture investors, (e.g., angel investors) tend to have

entrepreneurial experience (e.g., Mason, 2006). There were 8 lawyers (5.4%) and 21 new venture

investors (14.2%). Out of 148 respondents, 128 completed all survey items of our study and were

included in the final analysis. Over the two-year period, 23 judges participated in the business

plan competition both years consecutively, whereas 82 judges participated in only one annual

competition.

4.3. Dependent variable: Investment concentration

We used the Herfindahl–Hirschman Index to measure degree of concentration of an

investment portfolio. This index was originally designed as a proxy to measure industry

concentration, but is often used to determine the concentration of investment portfolios (e.g., Seo

et al., 2010). It is considered a better way to measure investment portfolios than calculating the

21

number of investments, because the index takes into account both “unequal distribution and

fewness” (Hirschman, 1964, p. 761). We took several steps to arrive at the investment

concentration index. For every investor, we calculated the percentage of imaginary dollars

invested in each new venture ( from the total investment. We then obtained the sum of

squares of the percentage values for all investment ( as the investment concentration

index. The index ranged from 0 to 1. When the index was close to 1, an investor was considered

to have a concentrated investment portfolio, and when it was close to zero, the investor was

considered to have a diversified investment portfolio.

Further, we obtained two simpler measures of investment concentration for robustness

tests. We measured the number of invested firms by counting the number of firms an investor

has distributed his or her imaginary dollars. A higher value reflects a more diversified portfolio.

We also measured the standard deviation of an investor’s invested amounts. A higher value

reflects a more concentrated portfolio.

4.4. Explanatory variables

4.4.1. Positive affectivity and negative affectivity

We used the Positive Affect Negative Affect Schedule (PANAS) developed by Watson

and colleagues (1988) to measure investors’ positive and negative affectivities. A 20-item

version was used, with ten descriptors employed to measure positive affectivity and ten

descriptors used to measure negative affectivity. Some sample descriptors for positive affectivity

were: excited, enthusiastic, and inspired; sample descriptors for negative affectivity included

afraid, hostile, and nervous. Survey respondents were asked to use such descriptors to rate

22

themselves on how they generally felt, using a 5-point Likert scale with 1 being “very slightly or

not at all” and 5 being “extremely.” This scale has a demonstrated reliability and validity.

Similar to other studies (e.g., Watson et al., 1988), we obtained an alpha reliability coefficient of

0.85 for positive affectivity and 0.80 for negative affectivity.

4.4.2. Need for cognition

We used a 10-item Need for Cognition scale from the International Personality Item Pool

(IPIP). This tool was developed as part of an extensive effort to develop and refine personality

inventories (Goldberg et al., 2006). All items in the IPIP have been rigorously and systematically

validated, and appear in over 80 publications (Goldberg et al., 2006). The Need for Cognition

scale was originally developed to capture an individual’s propensity to engage in and enjoy

effortful cognitive activities (Cacioppo and Petty, 1982). Some sample items are “need things

explained only once” and “like to solve complex problems.” We used a 5-point Likert scale to

rate responses, with 1 being “very slightly or not at all” and 5 being “extremely.” Individuals

with a high need for cognition are considered highly and intrinsically motivated toward thinking,

and exhibit a strong propensity for enjoying complex cognitive tasks. The alpha reliability

coefficient for need for cognition was 0.70, consistent with that in other studies (e.g., Cacioppo

and Petty, 1982).

4.4.3. Emotion-based decision making

We used a 9-item Emotion-Based Decision Making scale originally developed to capture

an individual’s propensity to rely on emotion and "gut feelings" when making decisions

(Barchard, 2001; Evans and Barchard, 2005). Some sample items include “believe emotions give

direction to life” and “Listen to my feelings when making important decisions”. We used a 5-

point Likert response scale (1 being “very slightly or not at all” and 5 being “extremely”). This

23

measure has good content validity because the items measure what they were intended to

measure. In the validation study (Evans and Barchard, 2005), this measure showed good internal

consistency (alpha reliability coefficient was 0.77) and test-retest reliability (0.70). Further, this

measure is highly correlated to the Intuition vs. Reason Scale (r = 0.59, p < 0.001), a similar

measure designed to capture the degree to which "gut reactions" dictate decision making (Tett et

al., 1997), and not correlated with an unrelated measure (r = -0.15, p = 0.076), IPIP Activity

Level Scale that captures activity level (Goldberg et al., 2006). Further, our study indicates that

this measure is not correlated with unrelated measures such as need for cognition scales (r = -

0.10, p = 0.229), positive affectivity (r = -0.05, p = 0.326), and negative affectivity (r = 0.01, p =

0.463). We obtained an alpha reliability coefficient of 0.86.

4.5. Control variables

Because each judge had the same amount of imaginary fund and opportunity sets, we

were able to control the effect of fund size and opportunity sets on investment behavior (e.g.,

Han, 2009). We controlled for age, gender, and ethnicity because they may be related to one’s

risk taking propensities and investment decisions (Erb et al., 1997; Powell and Ansic, 1997,

Suden and Surette, 1998; Weber et al., 1998), which may in turn influence how an investor

constructs portfolio. We also controlled for finance knowledge, using the number of finance-

related courses participants had taken and business founding experience by taking into account

whether they planned to start a business and whether they had a business still in operation,

because financial knowledge and prior entrepreneurial experience may bias investment decision

making (e.g., Graham et al., 2009). Further, we controlled for participants’ risk-taking

propensities measured by a 10-item Risk Taking scale from the IPIP because low risk propensity

can lead to a more concentrated investment portfolio. Sample items include “enjoy being

24

reckless” and “take risks.” Its alpha reliability coefficient was 0.76. Finally, we controlled for

whether participants had judged both years of the business plan competition. We coded 1 for

those who participated in both competitions and zero if they had not.

5.5.5.5. ResultsResultsResultsResults

Table 1 reports the means, standard deviations, and correlations of the variables. A

number of explanatory variables are significantly correlated with one another. In order to detect

possible multicollinearity issues, we examined the variance inflation factor (VIF) using the full

model. Specifically, our VIF ranges from 1.108 to 2.599; these results do not violate the typical

rule of thumb; i.e., a VIF of 5 or 10 and above indicates a multicollinearity problem (Kennedy,

2003). Nevertheless, in order to reduce any multicollinearity associated with interaction terms

and easily compare our effect sizes, we centered our explanatory variables (positive affectivity,

negative affectivity, need for cognition, emotion based decision making, and risk-seeking

propensity), and used the centered variables to calculate the associated interaction terms (Aiken

and West, 1991; Edwards, 2008).

- Insert Tables 1 and 2 here -

To test our hypotheses, we used standard hierarchical regression. We present the ordinary

least square results with investment concentration as the dependent variable in Table 2. We

included all control variables in Model 1, which was statistical significant (R²=0.119, p < 0.05).

Investors with greater finance knowledge (β = 0.009; p < 0.05) and those whose business was

still operating (β = 0.053; p < 0.01) were more likely to construct more concentrated investment

portfolios. However, we did not find a relationship between risk taking preference and

investment concentration, perhaps because our risk taking preference measure only captures risk

attitudes in a general manner. Risk taking preference can be further categorized, based on the

25

task types (e.g., financial risk taking, safety risk taking, recreational risk taking). Although each

risk preference type is uniquely correlated to behavioral frequencies in different task types

(Weber et al., 2002), our generic measure may not have captured such subtleties.

We introduced main effects of positive affectivity, negative affectivity, and need for

cognition influencing investment concentration in order to test Hypotheses 1, 2, and 3,

controlling for the main effect of emotion-based decision making in Model 2. The addition of the

predictor variables made significant contributions over and above Model 1 (ΔR²=0.059, p <

0.05). Hypothesis 1 (H1) predicted that positive affectivity would lead to a more concentrated

investment portfolio, whereas Hypothesis 2 (H2) predicted that negative affectivity would lead to

a less concentrated investment portfolio. We obtained a positive coefficient for positive

affectivity (β = 0.046; p < 0.05) and a negative coefficient for negative affectivity (β = -0.054; p

< 0.05). Thus, both H1 and H2 were supported. However, the negative coefficient for need for

cognition was not significant (β = -0.029; p > 0.05). Thus, Hypothesis 3 (H3) was not supported.

Finally, we introduced the interaction effects of an investor’s need for cognition and

emotion-based decision making on investment concentration in Model 3. The addition of the

predictor variables made marginally significant contributions over and above Model 2

(ΔR²=0.054, p < 0.10). Hypothesis 4 (H4) predicted that investors with a higher need for

cognition would be less likely to be influenced by dispositional affect than would investors with

a lower need for cognition. We obtained partial support for H4. Although the coefficient for the

interaction between need for cognition and positive affectivity was not significant (β = -0.034; p

> 0.05), the coefficient for the interaction of need for cognition and negative affectivity was

positive and significant (β = 0.096; p < 0.05). The coefficients for our main variables remained

the same as in Model 2. Figure 1 graphically illustrates this relationship. Investors with a low

26

need for cognition had a strong negative relationship between negative affectivity and investment

concentration, whereas investors with a high need for cognition were not influenced by their

dispositional affect in constructing their investment portfolios.

Hypothesis 5 (H5) predicted that investors who relied on emotion would be more likely

to be influenced by dispositional affect than would their counterparts who did not rely on

emotion in making decisions. We obtained partial support for H5. In Model 3, the coefficient for

the interaction term of emotion-based decision making and positive affectivity was positive and

significant (β = 0.061; p < 0.05), but the coefficient for the interaction term of emotion-based

decision making and negative affectivity was not significant (β = 0.003; p > 0.05). Figure 2

graphically illustrates how individuals with a higher propensity to rely on emotion when making

decisions were more likely to have a stronger relationship between positive affectivity and

investment concentration compared with investors who had a lower propensity to rely on

emotion to make decisions.

Our empirical analysis revealed that need for cognition is a suppressor (MacKinnon et al.,

2000; Pandey and Elliott, 2010) because it is related to our explanatory variable, positive

affectivity, but unrelated to our dependent variable and its inclusion boosted the coefficient of

positive affectivity. However, we did not eliminate this suppressor variable because doing so

would produce a wrong estimation of key parameters, weaken the predictive power of the model,

lead to sample specific biases, and ignore its theoretical relevance (Pandey and Elliott, 2010).

The effect sizes of our study may appear to be small at a first glance, but they are

qualitative meaningful. Based on Model 3, one standard deviation increase in positive (negative)

affectivity leads an increase of 0.01 (decrease of 0.02) in investment concentration. However,

small differences in the investment concentration index may lead to large qualitative differences

27

in investment behavior. For instance, an average investor in our setting invested in 7.8 new

ventures with an investment concentration index of 0.19. In Table 4, we use this information and

assume an investor has $1,000 to construct a plausible baseline portfolio for investors with

average positive affectivity and average negative affectivity. We then depict plausible portfolios

for high positive affectivity investors with an investment concentration index of 0.20 in six

ventures and a plausible portfolio for high negative affectivity investors with an investment

concentration index of 0.17 in eight ventures. Although our example is only for illustrative

purpose, small differences in this index may result in relatively different portfolios.

- Insert Tables 3 and 4, and Figures 1 and 2 here -

We performed a number of robustness tests to rule out alternative explanations based on

missing occupation variables, sample selection choice, other non-tested, nonlinear relationships,

or the operationalization of our dependent variables (Table 3). Model 4 includes occupational

variables, i.e., legal profession and venture investment experience, which were added to our

analysis because it is possible that work experience might alter an investor’s cognitive process.

In Model 5, we excluded participants who had repetitive involvement in the business plan

competition, including only first-time participants. Model 6 excluded second evaluations by

participants who had repetitive involvement in the business plan competition and included the

occupation variable. In Model 7, we included square terms of key variables involved in the

interaction terms to control for plausible confounding effects of other nonlinear terms (Edwards,

2008). To test whether our findings are consistent with alternative operationalizations of our

dependent variable, we included simpler measures of investment concentration, i.e., the number

of invested firms and the standard deviation of invested amounts, as our dependent variables in

28

Model 8 and Model 9 respectively. Overall, we found relatively consistent results throughout our

robustness tests.

6.6.6.6. DiscussionDiscussionDiscussionDiscussion andandandand conclusionsconclusionsconclusionsconclusions

This study explored how dispositional affect and cognitive style of new venture investors

influence their portfolio concentration. We found that high positive affectivity investors

constructed more concentrated investment portfolios compared with their low positive affectivity

counterparts. In addition, high negative affectivity investors constructed more diversified

investment portfolios compared with their low negative affectivity counterparts. We also found

that the impact of dispositional affect on investment portfolio concentration was moderated by an

investor’s cognitive style. Specifically, investors with a high need for cognition to make

decisions demonstrated a weaker relationship between negative affectivity and investment

diversification compared with their counterparts with a low need for cognition. Likewise, high

emotion-based decision making investors rely on affective experience to make decisions showed

a stronger relationship between positive affectivity and investment concentration compared with

their counterparts with a low emotion-based decision making (see Figure 3).

Our study showed that positive affectivity leads to a more concentrated investment

portfolio (H1), whereas negative affectivity leads to a more diversified investment portfolio (H2).

However, we failed to find a direct influence of need for cognition on investment concentration

(H3). This suggests that an investor’s propensity to rely on analytical process might not be

sufficient in shaping that investor’s portfolio concentration. We suspect that because constructing

an investment portfolio is such a complex process, mere tendency to engage in analytical

decision making does not influence how an investor diversify investment portfolio. Further, we

only obtained partial support for the moderating hypotheses (H4 and H5). Perhaps our small

29

sample size did not provide enough statistical power to detect moderating hypotheses (Aguinis

and Stone-Romero, 1997). Further, because the moderating effects of cognitive style were

significant only when dispositional affect and cognitive style have similar effects on cognitive

process, i.e., both cognitive style and dispositional affect leading to either heuristic/emotion

based processing or analytical processing, it is plausible that when independent effects of

dispositional affect and cognitive style on cognitive process are in the opposite direction,

cognitive style is not strong enough to influence the effect of dispositional affect on cognitive

process. Future experimental research could provide further explanations on these underlying

mechanisms.

6.1. Theoretical contribution

This study contributes to literature related to venture investment decision. Prior studies

have either studied investors’ independent decision of evaluating single ventures or firms’

portfolio construction. Yet, a firm’s investment portfolio is constructed by individuals who often

concurrently evaluate multiple opportunities. This study fulfills the intermediary gap by

delineating and testing factors influencing the composition of an individual’s investment

portfolio. Further, our work responds to the recent call to introduce affect-related research into

the study of entrepreneurship (e.g., Baron, 2008, Foo, 2011) by delineating how dispositional

affect influence investment concentration and how these relationships are moderated by an

investor’s cognitive style.

Our work also contributes to the study of personality traits related to investment

decisions. It suggests that investment concentration can be captured by personality variables, i.e.,

dispositional affect, need for cognition and emotion-based decision making. Investment portfolio

construction is the result of a complex set of judgments and decision making tasks. Investors

30

often took hours to evaluate multiple ventures and determine how to allocate their investments

among multiple opportunities. Their investment portfolios reflected important behavioral

regularities that can be predicted using personality traits (Epstein and O'Brien, 1985). Our study

shows that the direct and interaction effects of our personality variables are theoretically relevant,

empirically meaningful, and captured important regularities of portfolio construction.

- Insert Figure 3 here -

Further, our study contributes to dispositional affect research in two ways. First, it

provides further evidence on criterion validation for the Emotion-Based Decision Making scale.

This scale has only been demonstrated to be reliable, highly correlated with similar construct and

uncorrelated to unrelated constructs (Evans and Barchard, 2005). To our knowledge, this study is

the first one to demonstrate how this scale could be used to predict behavior. Second, it shows

how the influence of dispositional affect on simple cognitive tasks and job attitudes (e.g. Lerner

and Keltner, 2001; Thoresen et al., 2003) can be extended to understand complex behavior, such

as the portfolio composition of a new venture investor. Further, this study illustrates that the

influence of dispositional affect on behavior is moderated not only by an individual’s reliance to

engage in analytical process, but also by that individual’s reliance on affective experience to

make decisions.

6.2. Practical implications

Because the competition judges independently determined how to allocate their

investments, we refrain from applying our findings to group level decisions. Instead, we discuss

how our findings have practical implications for individual decision making. Although our task

may not perfectly capture investment portfolio composition, our findings may shed light on such

daily resource allocation decisions as screening business plans for first meetings or selecting new

31

ventures for due diligence. As components of a lengthy evaluation process (Hall and Hofer, 1993;

Zacharakis and Meyer, 2000; Zacharakis and Shepherd, 2001), such decisions are hardly

inconsequential. In fact, both new venture investors and entrepreneurs may benefit from the

findings of this study.

Entrepreneurs can use our findings to improve their resource acquisition strategies. First,

entrepreneurs could estimate a potential investor’s dispositional affect by talking with others who

know their investors. Based on this information, entrepreneurs could better determine how to

best present information to potential investors. For example, entrepreneurs could provide a

comprehensive analysis of a firm’s net present values to ease the use of analytical process to

investors with high negative affectivity, whereas they could highlight key points for investors

with high positive affectivity to ease their use of heuristic processing. Moreover, entrepreneurs

could also selectively approach a particular type of investors depending on their funding needs.

For example, if entrepreneurs seek large amount of funding quickly, they might consider looking

for high positive affectivity investors because these investors are more likely to make large

amount of investment compared with high negative affectivity investors.

Venture investment firms could use our findings to strategize how to construct

investment portfolios that reflect an investor's risk profile and distribution of expected returns

(e.g., Dimov and De Clercq, 2006; Han, 2009). They could select investment managers by

matching their dispositional affect, need for cognition, and emotion-based decision making with

a firm’s investment strategy. The literature of person-organization fit suggests that a better match

between organization and individual can lead to greater job satisfaction, retention, and even

performance (e.g., Kristof-Brown et al., 2005). Investment firms could use our findings to design

training and development programs to teach employees methods that could attenuate personality

32

bias by changing motivation or cognitive capability (e.g., Bless et al., 1990; Bodenhausen et al.,

1994; Worth and Mackie, 1987). Likewise, entrepreneurs could use our findings to understand

how their investors allocate investment resources among multiple ventures.

6.3. Limitations and future direction

The limitations of this study indicate areas for possible research directions. First, the

business plan competition setting may lack realism in understanding the actual composition of a

venture investment portfolio. Our study limits the extent we can generalize findings to realistic

investment settings. Our sample represents a relatively small group of investors who took part in

a business plan competition, which may have limited resemblance to actual investment practices.

Moreover, although many judges were active members of the local community of

entrepreneurship-related professionals (venture capitalists, lawyers, entrepreneurs, etc.),

relatively small proportion of them have actual investment experience. Thus, it is important to

test our framework with actual venture investors making real decisions.

Second, our study design did not allow us to track how the effects of dispositional affect

on cognitive process and information evaluation could influence investment concentration on an

ongoing basis, because we could only use it as a proxy for the combined effects of dispositional

affect. Future researchers could unpack these effects by using a real-time research design, i.e., a

verbal protocol such as thinking aloud to capture investor thought processes when evaluating

multiple ventures (Hall and Hofer, 1993), radio frequency identification techniques and

experience sampling methodology to track and capture judges' feelings, movements and search

activities at an investment roadshow (Welbourne et al., 2009; Uy et al., 2010).

Third, although this study investigated how two main types of dispositional affect can

influence investment behavior, scholars advocate that dispositional affect be refined into more

33

discrete categories such as trait fear and trait anger, along with positive and negative affectivities

(Foo, 2011; Lerner and Keltner, 2001. They have found that discrete forms of dispositional affect

can influence behavior using different mechanisms. Thus, ongoing research could explore how

other types of dispositional affect may influence portfolio composition.

Fourth, this study does not distinguish the impact of dispositional affect and emotional

states such as mood or emotion. An investor’s affective experience can easily change during the

portfolio construction period. Therefore, it is possible that the relationship between dispositional

affect and venture investment allocation may be mediated by a given state affect (Foo et al.,

2009). The interplay between dispositional and other types of affect is an important possible

direction for future research. Because the activation role of an individual’s dispositional affect

partly influences the impact on venture efforts (Foo et al., 2009), future research should

investigate these relationships within a similar context. Although ample evidence shows that the

influences of dispositional affect and state affect are quite similar, the underlying mechanisms

could be quite different. It is plausible that state affect serves as a mediator for the influence of

dispositional affect on attitudes and behaviors (e.g., Lyubomirsky et al., 2005; Foo et al., 2009).

Future research could further distinguish these two constructs and the underlying mechanism.

Investment portfolio construction is the result of a complex set of judgments and decision

making tasks involving lengthy processes. Their investment portfolios represent important

behavioral regularities that can be predicted using theoretically relevant personality traits. Our

theoretical framework and empirical results provided insights on how dispositional affect, need

for cognition, and emotion-based decision making influenced venture investment behavior using

a real-life business plan competition. We hope that our theoretical framework could open up

exciting future research opportunities.

34

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Table 1Means, Standard Deviations, Reliabilities and Correlations

Variables Mean SD 1 2 3 4 5 6 7 8 9 10 11 12 131 Investment Concentration Index 0.19 0.10 –2 Number of Invested Firms 7.82 3.51 -0.53 –3 Standard Deviation of Invested

Amount 77.96 59.85 0.89 -0.36 –4 Age 42.47 10.66 -0.05 0.08 -0.04 –5 Gender 0.79 0.41 0.04 -0.10 0.05 -0.01 –6 Finance Knowledge 4.14 2.31 0.15 -0.02 0.14 -0.08 0.00 –7 Risk Taking Preference 3.26 0.62 0.13 -0.09 0.14 -0.48 0.02 0.09 (0.76)8 Plan to Start a Business 0.79 0.41 0.01 0.05 -0.01 -0.02 0.02 0.11 0.11 –9 Business Still in Operation 0.50 0.50 0.19 -0.11 0.18 -0.08 -0.05 -0.14 0.21 0.42 –

10 Emotion Based Decision Making 2.72 0.60 0.03 0.06 0.06 0.04 -0.07 -0.07 0.18 0.12 0.17 (0.86)11 Positive Affectivity 4.03 0.48 0.16 -0.11 0.22 0.13 0.08 0.20 0.05 0.12 -0.01 -0.05 (0.85)12 Negative Affectivity 1.64 0.42 -0.09 0.12 -0.05 -0.22 0.20 -0.07 0.28 -0.12 -0.02 0.01 -0.03 (0.80)13 Need for Cognition 4.17 0.45 0.06 -0.10 0.03 -0.05 -0.10 0.25 0.13 0.18 0.01 -0.10 0.47 -0.21 (0.70)

Internal reliabilities are in parentheses. Correlations greater than 0.16 or less than -0.16 are significant at p < 0.05.

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Table 2Multiple Regression Analysis of the Impact of Dispositional Affect and Cognitive Style on Investment Concentration

** p<0.01, * p<0.05, & p<0.10; N=128Note: One tailed tests were performed whenour hypotheses contained directionality. Twotailed tests were performed otherwise.Standardized regression coefficients arereported in the table.

Model 1 Model 2 Model 3Duplication 0.032 0.006 0.036Age 0.016 -0.067 -0.073Gender 0.105 0.114 0.118 &Asian American 0.045 0.061 0.072European American 0.017 0.100 0.084Hispanic American 0.116 0.186 * 0.157 &Finance Knowledge 0.207 * 0.175 * 0.185 *Risk Taking Preference 0.091 0.122 0.106Plan to start -0.118 -0.145 -0.175 *Business Still in Operation 0.252 ** 0.246 ** 0.263 **Emotion Based Decision Making 0.043 0.050 0.086Positive Affectivity (H1) 0.220 * 0.267 **Negative Affectivity (H2) -0.203 * -0.178 *Need for Cognition (H3) -0.118 -0.131Need for Cognition X Positive

Affectivity (H4) -0.065Need for Cognition X Negative

Affectivity (H4) 0.157 *Emotion Based Decision Making

X Positive Affectivity (H5) 0.177 *Emotion Based Decision Making

X Negative Affectivity (H5) 0.007F-Statistic 1.423& 1.751* 1.829*R Square 0.119 0.178 0.232Adjusted R Square 0.035 0.076 0.105Change in R Square 0.059 * 0.054 &

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Table 3Robustness Tests

Model 4 Model 5 Model 6 Model 7 Model 8 Model 9Duplication 0.043 0.054 -0.049 0.034Age -0.066 -0.074 -0.065 -0.066 0.129 -0.034Gender 0.124 & 0.146 & 0.153 & 0.130 & -0.114 0.100Asian American 0.074 0.063 0.064 0.069 0.244 * 0.202 *European American 0.084 0.134 0.132 0.083 0.072 0.214 *Hispanic American 0.157 & 0.111 0.111 0.148 & -0.066 0. 284 *Finance Knowledge 0.185 * 0.193 * 0.192 * 0.182 * -0.005 0.165 *Risk Taking Preference 0.111 0.152 0.159 & 0.104 -0.073 0.127Plan to start -0.172 * -0.224 * -0.221 * -0.184 * 0.163 & -0.209 *Business Still in Operation 0.274 ** 0.296 ** 0.307 ** 0.270 ** -0.136 & 0.260 **Emotion Based Decision Making 0.084 0.027 0.026 0.091 0.041 0.091Law Profession 0.037 0.033Investment Experience 0.023 0.026(Emotion Based Decision Making ) ^ 2 -0.038(Positive Affectivity) ^ 2 0.081(Negative Affectivity) ^ 2 0.083(Need for Cognition) ^ 2 0.003Positive Affectivity (H1) 0.265 ** 0.248 * 0.247 * 0.295 ** -0.152 & 0.362 **Negative Affectivity (H2) -0.180 * -0.177 * -0.178 * -0.197 * 0.222 * -0.136 &Need for Cognition (H3) -0.133 -0.160 & -0.162 & -0.135 -0.020 -0.244 *Need for Cognition X Positive

Affectivity (H4)-0.066 -0.036 -0.035 -0.128 0.046 -0.057

Need for Cognition X NegativeAffectivity (H4)

0.158 * 0.150 & 0.150 & 0.141 & -0.150 & 0.141 &

Emotion Based Decision Making XPositive Affectivity (H5)

0.181 * 0.179 * 0.182 * 0.183 * -0.160 * 0.166 *

Emotion Based Decision Making XNegative Affectivity (H5)

0.003 -0.014 -0.018 -0.004 -0.111 -0.081

F-Statistic 1.629* 1.589* 1.408& 1.525 * 1.545 * 2.262 **R Square 0.233 0.250 0.251 0.242 0.203 0.272Adjusted R Square 0.090 0.093 0.073 0.083 0.072 0.152** p<0.01, * p<0.05, & p<0.10; N=105 for Model 5 and Model 6, N=128 for other modelsNote: One tailed tests were performed when our hypotheses contained directionality. Two tailed tests were performed otherwise. Standardized regressioncoefficients are reported in the table.

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Table 4Illustration of the Effect of Investment Concentration Difference on Portfolio Composition

Venture 1 Venture 2 Venture 3 Venture 4 Venture 5 Venture 6 Venture 7 Venture 8Investment

ConcentrationIndex

Investors withaverage PositiveAffectivity andNegative Affectivity

$ 280(28%)

$ 220(22%)

$ 160(16%)

$ 160(16%)

$ 80(8%)

$ 80(8%)

$ 20(2%)

$ 0(0%) 0.19

Investors with +1 SDPositive Affectivity

$ 310(31%)

$ 210(21%)

$ 160(16%)

$ 160(16%)

$ 80(8%)

$ 80(8%)

$ 0(0.0%)

$ 0(0.0%) 0.20

Investors with +1 SDNegative Affectivity

$ 240(24%)

$ 240(24%)

$ 160(16%)

$ 160(16%)

$ 80(8%)

$ 80(8%)

$ 30(3%)

$ 30(3%) 0.17