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Scanning both the academic and popular business literature of the last 40 years puzzles the alert reader. The variety of prescriptions of how to be successful (effective, performing, etc.) 1 Organizational performance, organizational success and organizational effectiveness will be used interchangeably throughout this paper.1 in business is hardly comprehensible: “Being close to the customer,” Total Quality Management, corporate social responsibility, shareholder value maximization, efficient consumer response, management reward systems or employee involvement programs are but a few of the slogans introduced as means to increase organizational effectiveness. Management scholars have made little effort to integrate the various performance-enhancing strategies or to assess them in an orderly manner.

This study classifies organizational strategies by the importance each strategy attaches to different constituencies in the firm’s environment. A number of researchers divide an organization’s environment into various constituency groups and argue that these groups constitute – as providers and recipients of resources – the basis for organizational survival and well-being. Some theoretical schools argue for the foremost importance of responsiveness to certain constituencies while stakeholder theory calls for a – situation-contingent – balance in these responsiveness levels. Given that maximum responsiveness levels to different groups may be limited by an organization’s resource endowment or even counterbalanced, the need exists for a concurrent assessment of these competing claims by jointly evaluating the effect of the respective behaviors towards constituencies on performance. Thus, this study investigates the competing merits of implementing alternative business philosophies (e.g. balanced versus focused responsiveness to constituencies). Such a concurrent assessment provides a “critical test” of multiple, opposing theories rather than testing the merits of one theory (Carlsmith, Ellsworth & Aronson, 1976).

In the high tolerance level applied for this study (be among the top 80% of the industry) only a handful of organizations managed to sustain such a balanced strategy over the whole observation period. Continuously monitoring stakeholder demands and crafting suitable responsiveness strategies must therefore be a focus of successful business strategies. While such behavior may not be a sufficient explanation for organizational success, it certainly is a necessary one.

We examine a central tenet of real option theory – whether real options influence managerial thresholds for investment. In contrast to prior studies that have focused on whether real options influence discrete investment decisions, our focus is on empirically isolating real options’ effects on thresholds. In particular, we examine the real options inherent in acquisition decisions. Our model posits that there are good reasons why we might expect there to be information asymmetry around the value of real options. Accordingly, if managers have unique information about growth options we might expect to observe them lowering their thresholds, perhaps to the point where they are willing to accept negative market returns. We further expect that the degree of information asymmetry for firm-specific growth options should be higher than for industry-specific growth options. Finally, we believe that managerial thresholds will be more prone to influence from growth options than deferment options. While thresholds are unobservable, we are able to isolate the effects of real options on acquisition thresholds by borrowing a method used originally in labor economics to isolate the determinants of reservation wages. Using a sample of over 28,000 acquisitions in the U.S., we find strong support for the model. These findings suggest that firms with low thresholds may choose to acquire despite comparatively low expected performance.

I review the burgeoning literature on applications of Markov regime switching models in empirical finance. In particular, distinct attention is devoted to the ability of Markov Switching models to fit the data, filter unknown regimes and states on the basis of the data, to allow a powerful tool to test hypotheses formulated in light of financial theories, and to their forecasting performance with reference to both point and density predictions. The review covers papers concerning a multiplicity of sub-fields in financial economics, ranging from empirical analyses of stock returns, the term structure of default-free interest rates, the dynamics of exchange rates, as well as the joint process of stock and bond returns.

This paper analyzes the roles of idiosyncratic risk and firm-level conditional skewness in determining cross-sectional returns. It is shown that the traditional EGARCH estimates of conditional idiosyncratic volatility may bring significant finite sample estimation bias in the presence of non-Gaussianity. We propose a new estimator that has more robust sampling performance than the EGARCH MLE in the presence of heavy-tail or skewed innovations. Our cross-sectional portfolio analysis demonstrates that the idiosyncratic volatility puzzle documented by Ang, Hodrick, Xiang, and Zhang (2006) exists intertemporally. We conduct further analysis to solve the puzzle. We show that two factors idiosyncratic variance and individual conditional skewness play important roles in determining cross-sectional returns. A new concept, the “expected windfall,” is introduced as an alternate measure of conditional return skewness. After controlling for these two additional factors, we solve the major piece of this puzzle: Our cross-sectional regression tests identify a positive relationship between conditional idiosyncratic volatility and expected returns for over 99% of the total market capitalization of the NYSE, NASDAQ, and AMEX stock exchanges.

I survey applications of Markov switching models to the asset pricing and portfolio choice literatures. In particular, I discuss the potential that Markov switching models have to fit financial time series and at the same time provide powerful tools to test hypotheses formulated in the light of financial theories, and to generate positive economic value, as measured by risk-adjusted performances, in dynamic asset allocation applications. The chapter also reviews the role of Markov switching dynamics in modern asset pricing models in which the no-arbitrage principle is used to characterize the properties of the fundamental pricing measure in the presence of regimes.

From the supplier firm's perspective, a referral is a recommendation from A (the referrer) to B (the potential customer) that B should, or should not, purchase from C (the supplier firm). Thus, as referrals are for a specific supplier firm, they should be viewed as part of the supplier firm's marketing and sales activities. We recognize three types of referrals – customer-to-potential customer referrals, horizontal referrals, and supplier-initiated referrals – that have critical roles in a potential customer's purchase decision. We develop the concept of referral equity to capture the net effect of all referrals for a supplier firm in the market. We argue that supplier firms should view referral equity as a resource that has financial value to the firm as it affects the firm's cash flows and profits. We offer strategies firms can use to manage referrals and build their referral equity and suggest a research agenda.