On accounting’s twenty-first century challenge: evidence on the relation between intangible assets and audit fees

Review of Quantitative Finance and Accounting - We inform the policy debate arising from the dramatic shift in US firms’ balance sheets towards intangible assets that has created a challenge...     

Material weakness disclosures and restatement announcements: The joint and order effects

We examine differences in stock price, option volatility, and litigation reactions to restatement announcements that are associated with a material weakness (MW) disclosure. Contrasted with restatements that are not associated with any MW disclosure, our analyses reveal that firms that announce both a restatement and an associated MW experience significantly more negative market returns, greater implied volatility, and higher likelihood of class action lawsuits. Separating the restatements into timely reporters, where the MW precedes the restatement, and non‐timely reporters, where the MW is concurrent with or follows the restatement, we find that timely reporters experience more negative returns at the time of the restatement, relative to non‐timely reporters, suggesting that investors perceive the early MW disclosure to signal more pervasive control‐related problems. Interestingly, we find that timely and non‐timely reporters are equally likely to be sued, consistent with the argument that wrongdoing (through either a timely or non‐timely MW disclosure) provides stronger grounds for establishing scienter. However, timely reporters appear to secure more favorable litigation outcomes: they face higher likelihood of lawsuit dismissals and pay much lower settlements, compared to non‐timely reporters. Overall, our evidence provides new insights into how market participants incorporate information about internal control weaknesses into their perceptions regarding the economic implications of financial restatements, and financial reporting quality.     

State enforceability of noncompete agreements: Regulations that stifle productivity!

Noncompete agreements (also known as covenants not to compete [CNCs]) are frequently used by many businesses in an attempt to maintain their competitive advantage by safeguarding their human capital and the associated business secrets. Although the choice of whether to include CNCs in employment contracts is made by firms, the real extent of their restrictiveness is determined by the state laws. In this article, we explore the effect of state‐level CNC enforceability on firm productivity. We assert that an increase in state level CNC enforceability is detrimental to firm productivity, and this relationship becomes stronger as comparable job opportunities become more concentrated in a firm's home state. On the other hand, this negative relationship is weakened as employee compensation tends to become more long‐term oriented. Results based on hierarchical linear modeling analysis of 21,134 firm‐year observations for 3,027 unique firms supported all three hypotheses.     

Education Dashboards for Enhanced Learning: A Singapore Experience

Educators continue to utilize add-ons, extensions, or replacements to traditional Learning Management Systems (LMSs) to supplement capabilities and improve the classroom experience. This work resulted in the design and development of the Education Dashboards for Enhanced Learning (EDEL) template. The EDEL template enables faculty members to create dynamic web spaces called Knowledge Portals (KPortals). A semester-long introduction of KPortals proved to be positive and useful. Students tended to view the KPortal as a “go-to” webspace. The authors hope to refine the template in future studies and examine its usefulness in other countries.     

Targeted advertising as a signal

This study presents a signaling model of advertising for horizontally differentiated products. The central ingredients of the model are two important characteristics of advertising—targeting, and noisy information content. The theory yields interesting results about the informational role of targeted advertising, and its consequences. First, targeting can itself serve as a signal on product attributes. Second, the effectiveness of targeting depends not only on firms knowing consumer preferences, but on consumers knowing that firms know this. This creates a distinction between strategies of targeting and personalization. Third, the effectiveness of targeting in equilibrium may (far) exceed the information contained directly in the targeted message. Fourth, information content is not, however, superfluous. Specifically, when ads contain no information, a targeting equilibrium does not exist. Together, these results reveal how advertising conveys information both through the content of the message and the firm’s choice of advertising medium. Furthermore, the model is robust to the various critiques of prior work on ads-as-signals: namely, that ad content is irrelevant, ad exposure is unnecessary, and the choice of ads as signals is inherently arbitrary.

The Global Market for Agricultural Machinery and Equipment

Growth in the current-dollar sales of agricultural machinery and equipment was almost six percent per annum during 2000-2005.1 This was much faster than the 2.6 percent growth for agricultural output and the 1.2 percent rate for population in the same period. JEL Classification L64     

KALDORIAN DEMAND FUNCTIONS AND THE RETURN TO CAPITAL: AN ANALYSIS IN A TRADE THEORETIC FRAMEWORK

Abstract.  This paper shows that under certain plausible conditions capital accumulation raises the return to capital. A three good trade theoretic model with Kaldorian demand functions is used to establish this result. This proposition is also independent of the assumption of diminishing return to capital a key feature of endogenous growth theory. Our result sheds light on the high rates of investment and growth that many East Asian economies have achieved.     

Comparing the resource‐based and relational views: knowledge transfer and spillover in vertical alliances

We compare resource‐based and relational perspectives to examine competitive advantages within the context of vertical learning alliances. Previous research has shown that through such alliances suppliers acquire knowledge to forge new capabilities and attain performance improvements. We ask whether such improvements are exclusive to the learning partnership, or are available in other average partnerships of this supplier. We posit that the extent to which such performance improvements are partnership exclusive depends on whether the newly forged capabilities lie entirely within the supplier firm's boundaries, or at the learning dyad level. As such, we untie two forms of performance improvements arising from learning dyads. While the resource‐based view helps explain the performance gains learning suppliers deploy across average partners, the relational view reveals the additional performance edge that remains exclusive to the learning partnership. Based on empirical evidence from a survey of 253 suppliers to the equipment industry, we find that partnership exclusive performance (i.e., ‘relational performance’), the true source of learning dyads' competitive advantage, is a function of suppliers acquiring know‐how within the dyad, developing dyad‐specific assets and capabilities, and structuring buyer‐supplier relational governance mechanisms. We discuss implications for research and practice. Copyright © 2008 John Wiley & Sons, Ltd.     

Challenges and opportunities in high-dimensional choice data analyses

Modern businesses routinely capture data on millions of observations across subjects, brand SKUs, time periods, predictor variables, and store locations, thereby generating massive high-dimensional datasets. For example, Netflix has choice data on billions of movies selected, user ratings, and geodemographic characteristics. Similar datasets emerge in retailing with potential use of RFIDs, online auctions (e.g., eBay), social networking sites (e.g., mySpace), product reviews (e.g., ePinion), customer relationship marketing, internet commerce, and mobile marketing. We envision massive databases as four-way VAST matrix arrays of Variables?×?Alternatives?×?Subjects?×?Time where at least one dimension is very large. Predictive choice modeling of such massive databases poses novel computational and modeling issues, and the negligence of academic research to address them will result in a disconnect from the marketing practice and an impoverishment of marketing theory. To address these issues, we discuss and identify the challenges and opportunities for both practicing and academic marketers. Thus, we offer an impetus for advancing research in this nascent area and fostering collaboration across scientific disciplines to improve the practice of marketing in information-rich environment.