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This paper analyzes citations from the first 20 volumes of Advances in Management Accounting using Google Scholar in April and May, 2013.

This study assesses the success of the first 20 volumes of Advances in Management Accounting using citation analysis. Four citation metrics are used. The four citation metrics are: (1) total citations since year of publication until April and May, 2013, (2) citations per author since year of publication until April and May, 2013, (3) citations per year since year of publication until April and May, 2013, and (4) citations per author per year since year of publication until April and May, 2013.

The top 20 authors for each citation metric, the top 20 faculties for each citation metric, and the top 20 doctoral programs for each citation metric are determined. Furthermore, the top 20 articles are determined using two citation metrics and the H-index for Advances in Management Accounting is computed.

Potential doctoral students, current doctoral students, “new” Ph.D.s with an interest in management accounting, current management accounting faculty, department chairs, deans, other administrators, journal editors, and journal publishers will find these results informative.

This chapter provides evidence that legislation affecting litigation risk has an influence on the financial reporting behavior of corporate management, we address the following research questions: (1) Do firms react to changes in litigation risk that result from the passage of new legislation at the federal level by adjusting their level of conservatism with regard to reporting earnings? (2) How do firms’ levels of conservatism react to changes in litigation risk over time? We analyze the level and trend in conditional conservatism to evaluate the efficacy of legislation in altering managerial reporting choice. Our examination takes place in the context of two distinct pieces of legislation intended to alter the legal environment faced by corporate managers: (1) the PSLRA (1995), and (2) Sarbanes–Oxley Act of 2002. Our findings indicate that the passage of legislation that increases litigation risk is associated with increased timeliness (conservatism) in financial reporting by managers. The increased timeliness, however, begins to subside shortly after the initial effect. While the initial effect of a reduction in litigation risk is negligible, subsequent periods exhibit declining timeliness (conservatism) in financial reporting. Our results indicate that legislative actions can be successful in altering management reporting choice through changes in legal regime. However, our results also demonstrate that the desired influence of these legislative policies may be transient.

A strong, useful theoretical foundation for performance assessment and prediction relies on four components: preliminary observation of a system, identification of key or dominating variables in the system, synthetic and vertical thinking, and successive refinement.

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

A fundamental problem in articulating the societal benefits of technology transfer is the lack of hard empirical evidence on the economic gains associated with this activity. To fill this gap, we apply the framework and methods developed by Griliches and Mansfield et al. to assess the social returns to university-based inventions. This methodology can be used to derive explicit measures of key metrics, such as social rates of return and benefit-to-cost ratios characteristic of specific new technologies. A case study is used to illustrate the application of this method.

Content marketing is a crucial aspect of digital marketing in modern firms. By generating content that is interesting and engaging, companies have the two-fold advantage of promoting their products in a relatable way, while increasing familiarity and engagement with the brand. As data scientists at Credit Suisse, we value our content teams because their voice is the bank's voice. We strive to provide them with the best tools to increase their articles' success. With the help of machine learning, we have created digital products that allow them to improve articles before publication, recommend them to the most interested readers, and track their performance. The chapter begins with a brief introduction to content marketing, followed by an overview of our data, a review of the business challenges we have encountered, and the machine learning solutions we have developed in order to provide the best data insights to our internal and external stakeholders. We close the chapter with a brief summary of our work.

Discrete choice models are widely used for estimating the effects of changes in attributes on a given product's likely market share. These models can be applied directly to situations in which the choice set is constant across the market of interest or in which the choice set varies systematically across the market. In both of these applications, the models are used to determine the effects of different attribute levels on market shares among the available alternatives, given predetermined choice sets, or of varying the choice set in a straightforward way.

Discrete choice models can also be used to identify the “optimal” configuration of a product or service in a given market. This can be computationally challenging when preferences vary with respect to the ordering of levels within an attribute as well the strengths of preferences across attributes. However, this type of optimization can be a relatively straightforward extension of the typical discrete choice model application.

In this paper, we describe two applications that use discrete choice methods to provide a more robust metric for use in Total Unduplicated Reach and Frequency (TURF) applications: apparel and food products. Both applications involve products for which there is a high degree of heterogeneity in preferences among consumers.

We further discuss a significant challenge in using TURF — that with multi-attributed products the method can become computationally intractable — and describe a heuristic approach to support food and apparel applications. We conclude with a summary of the challenges in these applications, which are yet to be addressed.

The application of network theory and social network analysis (SNA) to tourism and hospitality is recent. Nonetheless, several authors have been applying the method contributing to regional planning, local-level tourism networks, tourism policy and governance, innovation, entrepreneurship, knowledge transfer, and learning. This chapter aims to characterize the use of SNA in tourism and hospitality research. Specifically, it intends to: (i) present the framework of SNA in a methodological perspective; (ii) perform a bibliometric analysis of SNA use in tourism and hospitality research; (iii) systematize the dimensions and metrics that researchers can use to apply SNA, namely the relevance for tourism; and (iv) present a case study analyzing tourism innovation networks. This chapter brings important contributions to tourism and hospitality research and practice, by focusing on the theoretical framework and practical application of SNA, providing relevant conceptual and practical knowledge that will empower researchers to use this method in tourism and hospitality studies.

We develop and test a model of how a software firm's business strategy (product scope and market scope) interacts with the firm's network position (alliance degree and structural holes) to impact performance. We test the joint-effects hypotheses on a sample 359 packaged software firms that have entered into 5,489 alliances involving 2,849 distinct firms during the time period, 1990–2002. While prior studies have demonstrated the importance of network positions as a determinant of firm strategy and performance, this chapter begins to examine the performance effects of how a firm's business strategy and network positions interact. We find support for three of the four hypotheses lending empirical support for our theoretical model. We develop implications for network-based perspectives of strategy and outline areas for further research.