...marginal impact decision policy contingencies and that specific human capital had significant influence on the probability of loan approval. However, we did find that the similarity between the loan officers' human capital and the applicants' human capital was a significant indicator of loan approval. The findings offer interesting insight into the heterogeneity of loan decision processes and outcomes and future research opportunities are suggested.

Introduction

Debt represents 50% of the capital structure in small firms, with commercial bank loans the most common source of debt funding. Even among the most prestigious venture capital-backed firms that make it to Initial Public Offerings (IPOs), bank financing is a major source of funding (Berger & Udell, 2003). Despite this need, many small businesses have difficulties obtaining bank loans, which constrain their ability to grow (e.g., Observatory of European SMEs, 2003). When considering whether to grant a loan, the natural response for banks is to seek more information in order to reduce uncertainty about the likelihood of loan repayment. However, it can be particularly challenging to collect information about small businesses. It is not unusual for small businesses to have a short history, a lack of formal or public records, or a deficiency of formal control systems. This lack of credible information can put the banks at a disadvantage, by making it difficult to differentiate between high-risk and low-risk borrowers, leading to an adverse selection problem (Stiglitz & Weiss, 1981).

Loan officers are charged with the task of gathering and evaluating information regarding a prospective borrower. In an attempt to reduce the inherent risk in providing loans to small entrepreneurial businesses, banks seek to formalize both the information gathering process and the loan officer's decision process. Application forms are developed to standardize information gathering. Banks provide training to the loan officers conveying explicit criteria that should be used to determine the creditworthiness of the borrower. Loan decision criteria are traditionally based on industry established principles such as those delineated in the well-known "five Cs of lending"; i.e., Capacity (the ability of a firm to service the debt in terms of financial status and management experience), Conditions (environmental conditions affecting the ability of the borrower to service the debt, covering areas such as: recession, growth, business cycle, interest rate, and competitive pressure), Capital (the funds available to operate the company), Collateral (collateral represents an alternative source of repayment for the bank that could be liquidated in case the borrower defaults), and Character (management's integrity, stability, and overall willingness to repay the loan) (Beaulieu, 1996; Jankowicz & Hisrich, 1987; Riding, Haines, & Thomas, 1994). By prescribing decision policies based on the five Cs or on similar frameworks, banks would conceivably expect any two loan officers to reach the same conclusion regarding a loan for any given applicant.

Despite banks' efforts to homogenize the loan decision-making process across loan officers, research suggests that the decisions made by loan officers actually vary according to the loan officers' level of experience (Andersson, 2001). The loan officers' experience is a facet of human capital which includes the knowledge, skills, and experience used by an individual to accomplish organizational goals (Becker, 1964). The finding that the level of human capital provides an explanation for variance in loan officers' decisions is consistent with previous empirical work on decision making under uncertainty (Chase & Simon, 1973; Choo & Trotman, 1991). However, there has been little research that explores how human capital influences decision-making processes.

We extend previous research by implementing a conjoint experiment to observe the utilization of decision criteria in real-time decisions by loan officers. Using the five-Cs framework as a starting point, we explore how loan officers' human capital influences the use of interactions or contingencies among the criteria employed in the loan decision-making process. We also consider how human capital may act as a moderator in the decision-making process. In other words, we examine the influence of specific human capital on the relationship between the decision process and the outcome of the loan decision, i.e., the likelihood of approving loans. Furthermore, we apply the similarity-attraction paradigm to consider the impact of the relationship between the loan officers' human capital and the entrepreneurs' human capital on the level of emphasis placed on the applicant's human capital in the credit evaluation process.

The article proceeds as follows. First, we discuss human capital and its impact on decision-making processes. We develop hypotheses regarding human capital and the use of contingencies in decision policies, the effect of specific human capital on the probability of loan approval, and the intersection of human capital and similarity attraction. Next, we describe the research design, present an overview of the conjoint experiment, and detail the variables used in the study. Finally, we present the analysis and results and discuss the implications of this research.

Human Capital and Loan Officers' Decision Policies

Human capital is the knowledge, skills, and experience used by an individual to provide value to the firm (Becker, 1964; Schultz, 1961). The merits of human capital as an antecedent to firm performance in a variety of contexts have been of considerable interest to scholars over the last couple of decades (Pfeffer, 1998). Hitt, Bierman, Shimizu, and Kochhar (2001) showed that human capital has direct and moderating effects on the performance of professional service firms. Human capital capabilities also positively influenced new venture performance (Chandler & Hanks, 1994). Multinational enterprises performed better when the top managers possessed international human capital qualities (Carpenter, Sanders, & Gregersen, 2001). Dimov and Shepherd (2005) found a relationship between venture capitalists' human capital and the performance of portfolio firms. Similarly, we expect that loan officers with a greater level of human capital would provide increased value (i.e., better performance) to the bank through more accurate assessments of the likelihood of loan repayment by potential borrowers.

Human capital varies in its degree of specificity and can be broadly classified as general or specific. Some aspects of human capital are very general and provide the individual with all-purpose skills and broad problem-solving capabilities that are relevant across multiple contexts. A general facet of human capital is formal education (Becker, 1964; Fisher & Govindarajan, 1992). Loan officers with a greater level of education in an applicable field of study are assumed to have a broader base of articulable knowledge and increased communication, problem-solving, and social skills. The knowledge obtained from an education also enhances future learning capacity (Cohen & Cohen, 1983).

Specific human capital is developed through training or experience which results in skills that are limited in applicability to a specific firm, job, or task (Gimeno, Folta, Cooper, & Woo, 1997). On-the-job experience can facilitate the development of some general human capital, but also leads to a greater understanding of products, processes, and services that are specific to the firm. Furthermore, tacit knowledge is developed regarding how to effectively perform within a particular job. Tacit knowledge cannot be codified and therefore cannot be easily learned or shared through verbal communication or written texts. Tacit knowledge must be learned through effort, discovery, and experience (Polanyi, 1969). The concept of tacit knowledge is closely related to skill and experience (Berman, Down, & Hill, 2002), which uniquely define specific human capital. Tacit knowledge has been shown to be a source of competitive advantage and positive performance (Berman et al., 2002; Hitt et al., 2001). In the context of bank loans, on-the-job experience would enable the loan officer to cultivate tacit knowledge about the local business environment, the banks' peculiar strengths, weaknesses, and processes, or the interaction of various entrepreneurial/small-business characteristics which may increase the odds of a successful venture. This knowledge resource likely has a profound impact on the loan decision-making process.

Little research exists that explicitly addresses how differences in human capital influence the degree to which various decision cues are used, and the empirical findings that have been presented are inconclusive (e.g., Camerer & Johnson, 1991). Individuals that have a high level of expertise in their job, or specific human capital, are likely to view problems and solutions in a different way than those with less expertise (Gavetti & Levinthal, 2000). Camerer and Johnson (1991) showed that experts have greater ability to use contingent decision rules. That is, interactions of different decision cues are more common among decision makers with greater human capital. Experts are typically efficient in their decision making by focusing on those attributes that contribute most to the outcomes of decisions (Chase & Simon, 1973; Choo & Trotman, 1991). A greater level of knowledge can help decision makers more efficiently process and make sense of larger quantifies of information and consequently reach decisions quicker (Forbes, 2005; Logan, 1990; Wozniak, 1987). Although this speed is often attributed to mental shortcuts involved with the use of heuristics (Libby & Lewis, 1982; Ucbasaran, Wright, Westhead, & Busenitz, 2003), this does not necessarily mean that experts' decision policies are simple. There is evidence that expert decision makers have policies based on a deep structure with more, stronger, and richer links between attributes (Gobbo & Chi, 1986). This increased richness is reflected, in part, by the use of contingent relationships between attributes. For example, Kuusela, Spence, and Kanto (1998) found that mortgage loan experts used more compensatory decision rules in making mortgage loan decisions.

Loan officers are attempting to determine the likelihood of loan repayment. Based on explicit decision policies set forth by the bank, loan officers would likely sum up the pros (e.g., independent collateral), and subtract the cons (e.g., high-risk project) to determine an aggregate "score" in order to...

NOTE: All illustrations and photos have been removed from this article.



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