Finite Element Analysis of Solids and Structures combines the theory of elasticity (advanced analytical treatment of stress analysis problems) and finite element methods (numerical details of finite element formulations) into one academic course derived from the author’s teaching, research, and applied work in automotive product development as well as in civil structural analysis. Features Gives equal weight to the theoretical details and FEA software use for problem solution by using finite element software packages Emphasizes understanding the deformation behavior of finite elements that directly affect the quality of actual analysis results Reduces the focus on hand calculation of property matrices, thus freeing up time to do more software experimentation with different FEA formulations Includes chapters dedicated to showing the use of FEA models in engineering assessment for strength, fatigue, and structural vibration properties Features an easy to follow format for guided learning and practice problems to be solved by using FEA software package, and with hand calculations for model validation This textbook contains 12 discrete chapters that can be covered in a single semester university graduate course on finite element analysis methods. It also serves as a reference for practicing engineers working on design assessment and analysis of solids and structures. Teaching ancillaries include a solutions manual (with data files) and lecture slides for adopting professors.

While accounting research has demonstrated the role of a decision maker's own emotions in various judgments, emerging psychology research proposes that others' emotions provide an informational signal that can be used to assess an opponent's limits and cooperativeness in a bargaining situation. We examine how a bargaining opponent's emotions can provide information signals that can be used by a selling division manager during transfer pricing decisions and whether organizational design choices by corporate management to foster cooperation can create a context that influences how managers react to these signals. In an experiment, under weak corporate management pressure to work internally (less collaborative environment), managers' selling price estimates were more conciliatory when the opponent was described as displaying negative emotions than when described as displaying positive emotions. However, when there was strong corporate management encouragement to work internally (more collaborative environment), managers' selling price estimates were more conciliatory when the opponent displayed positive rather than negative emotions. A multiple mediation analysis suggests that corporate policies for business unit collaboration can change which information signal (cooperativeness or others' limits) mediates the relationship between opponent's emotions and the transfer pricing decision. As such, not only do we extend both accounting and psychology research by lending insight into the context dependency of opponents' emotions under various organizational design structures, but we also lend insight into the causal mechanism by which the emotional displays influence transfer pricing judgments. These results have implications for unit profitability, divisional resource allocation, and management accounting practices.

Estimating the life-cycle or duration of a product can be an important input into a firm's decision-making related to production and marketing. In the music industry, online Peer-to-Peer (P2P) networks have attracted millions of potential music consumers and have had substantial impact on the music business. In this paper, we investigate the possible use of P2P information in estimating product shelf-life, in particular the duration of a music album on the Billboard 100 chart. We identify and track the music albums that appear on the Top 100 of the Billboard Charts, spanning a period of six months. We show that P2P sharing activity can be used to help predict the subsequent market performance of a music album.

Recent research indicates that context effects play an important role in accounting decision making, where information for a given decision task is evaluated differently depending on the nature of the prior contextual information (e.g., Tan and Jamal 2001; Bhattacharjee, et al 2007). While these studies have contributed significantly to the literature, this research has focused on decisions where only prior task information is available to contextualize current task information. Yet, many accounting decision tasks also contain relevant benchmark information (e.g., budget targets) that can and should be used as the basis to evaluate current task data. We extend the research in this area by conducting a series of experiments in which the robustness of context effects from prior task information are tested in a setting where managers are supposed to compare current performance metrics against relevant and available target information. Consistent with our theoretical arguments, experiment 1 reveals that managers' judgments using a basic scorecard are subject to context effects that result in assimilation such that an individual's current performance is rated more favorably (unfavorably) when the individual's prior performance was rated as favorable (unfavorable). This is the case even though in both conditions the same relevant benchmark information is provided and indicative of average performance for the individual. In experiment 2, we find that color status indicators, often used in practice and designed to make scorecard evaluations cognitively easier, are only partially successful at reducing assimilation effects in managers' judgments. In experiment 3, we take an approach that is different from what has been used in prior scorecard research by having managers perform analytics on the data. This approach was inspired by the attention-directing aspect of analytics, popularized in the audit domain. This attention directing approach was found to successfully reduce assimilation effects. Our results provide insight into a different approach to scorecard judgments that may be beneficial for certain biases. Our results also extend the accounting literature on comparative-based judgments by demonstrating that prior contextual information can have such a strong influence on judgments that relevant comparative benchmarks (budgeted data) are ignored.

This study examines the effectiveness of alternative training techniques on improving analytical procedures performance. To account for the sequential and iterative nature of complex analytical procedures, we used two different knowledge acquisition mechanisms (worked-out example and problem solving training) to develop specific training tools that have not been used in prior audit research. In addition, we combined these mechanisms with different levels of self-explanation (with self-explanation or without self-explanation). Participants were provided with no training (baseline group) or six training techniques that included outcome feedback, worked-out example, problem solving, self-explanation, worked-out example with self-explanation and problem solving with self-explanation. Thereafter, participants completed a final analytical procedures case that had an error seeded in the financial statements. Results indicate that "worked-out example" or "problem solving training" combined with self-explanation outperformed all other groups. Additional analyses provide insight on the performance gains of the various training techniques in the initial hypothesis generation and subsequent information search and evaluation phases during analytical procedures. Our findings contribute to the training literature in auditing and psychology by showing that combinations of techniques are required for designing effective training for complex diagnostic decision making like analytical procedures.

The music industry has repeatedly expressed concerns over online music sharing activity and its potentially devastating impact on revenues. Until recently, attempts to control online file-sharing have been primarily through consumer education and legal action against the operators of networks that facilitated file-sharing. Recent legal action against individual file-sharers marked an unprecedented shift in the industry's strategy in its latest anti-piracy campaign. The industry chose to focus on a relatively small group of individuals and maximize the publicity surrounding their legal action to discourage overall participation in file-sharing networks. However, the impact of these legal threats on individual file sharers is little known. In this research, we passively track the online file-sharing behavior of over 2000 individuals and examine the impact of these threats on their behavior. Our results suggest that individuals who share a substantial number of music files react to legal threats differently from those who share a lesser number of files. Importantly, our analysis indicates that even after these legal threats, overall availability of music files on these networks remains substantial.

Advances in online technologies and bandwidth availability have opened new vistas for online distribution of digital goods. But potential benefits for consumers are juxtaposed against challenges for retailers of such goods. Here we investigate one type of digital experience good - music - whose market environment includes the very real presence of online piracy options. While arguments abound for and against online distribution of such digital goods, little research exists in this area. We develop a model of consumer search for such an experience good, and study five different emerging market environments for retailers (from a traditional brick and mortar retailer to an online retailer) where consumers have opportunities of pirating from illegal networks. Retailer cost to publishers is modeled using a variety of licensing schema. Results from a survey we conducted, together with data from online sharing networks, are utilized to investigate the validity of a key assumption. Finally, computational analysis is used to develop insights related to conditions that cannot be solved for analytically. We find that online selling strategies for a traditional retailer can provide additional profits even in the face of existing piracy options. Our results indicate that decreasing piracy is not necessarily equivalent to increasing profit. We show that leading strategies for business in such goods should include the use of pricing options, provision of efficient search tools to consumers and new approaches to licensing structures with digital good publishers.

This paper examines the effect of generating and testing hypothesis sets of different sizes on the efficiency and accuracy of auditors? analytical review judgments. In particular, and in contrast to prior descriptive research by, e.g., Libby [1985], Koonce [1993], and Ismail and Trotman [1995], we investigate the accuracy and time efficiency of audit judgments if auditors are required to use a strategy that generates and tests a specific number of initial hypotheses. Our research is thus aimed at evaluating the relative benefits of different approaches to hypothesis testing as opposed to understanding the processes auditors use to generate and test hypotheses.

Product assortment and availability are important determinants of sales success for firms of industrial commodity products. Well-known pricing and promotion strategies for differentiated products do not translate well to such products where price is closely tied to the cost of the products. Consequently, firms with multiple stores of commodity products are faced with the problem of product assortment that incorporates varying geographic and demographic conditions of locations they serve. The paper presents a model for assortment planning and optimization for multiple stores of a company. The novelty of our approach is twofold: first, it deploys data mining techniques to identify sales pattern information across multiple stores through existing sales data across segments and across stores; second, it identifies the optimal product assortment for each store and permits analyses of assortment efficiency evaluation among all existing stores. Our model first finds frequent itemsets based on association rule analysis and prunes them using a novel conflict resolution method. It then incorporates the identified product combinations into the development of the optimization formulation. Our methodology offers solutions that have important implications on product assortment, including complements versus substitutes and product bundling, and sheds lights on product planning and assortment strategies in general. A data set from an industry leading plastics manufacturer and retailer in the United States is used to demonstrate our model.