Search results

The absence of the reasoning stage in the analysis of long-term investment decision creates a serious gap in this classic topic in management accounting literature. The purpose of this paper is to fill this gap. The traditional analysis focuses on the evaluation stage using capital budgeting tools to rank alternative investment proposals. It tacitly assumes that the decision is to be made, thereby bypassing the reasoning stage. However, the reasoning stage may reveal that there is no sufficient justification (reasoning) to consider searching for and evaluating alternative proposals for this decision. Focusing on the reasoning component, the paper combines Fritz's (1989, 1990) “creative tension” and Janis and Mann's (1977) “challenges” as the driving forces for the problem-finding step. To demonstrate the significance of filling the reasoning gap in the long-term investment decision, the paper selects the modular manufacturing system and the complex investment decision required for its adoption. Using hypothetical data, the paper employs the Dempster-Shafer Theory of Evidence and Omer, et al's (1995) algorithm to compute the belief and plausibility values of the three reasoned actions: (1) maintain the status quo, (2) adopt Level 2 (assembly) modularity or (3) adopt Level 2 (design) modularity.

The contributions of the paper include (1) highlighting a critical gap currently existing in one of the classical decisions in the management accounting literature; (2) developing a framework for filling this gap and (3) applying this framework to the intricate nature of the modular manufacturing system and its complex investment decision.

It has long been recognised that humans draw from a large pool of processing aids to help manage the everyday challenges of life. It is not uncommon to observe individuals adopting simplifying strategies when faced with ever increasing amounts of information to process, and especially for decisions where the chosen outcome will have a very marginal impact on their well-being. The transactions costs associated with processing all new information often exceed the benefits from such a comprehensive review. The accumulating life experiences of individuals are also often brought to bear as reference points to assist in selectively evaluating information placed in front of them. These features of human processing and cognition are not new to the broad literature on judgment and decision-making, where heuristics are offered up as deliberative analytic procedures intentionally designed to simplify choice. What is surprising is the limited recognition of heuristics that individuals use to process the attributes in stated choice experiments. In this paper we present a case for a utility-based framework within which some appealing processing strategies are embedded (without the aid of supplementary self-stated intentions), as well as models conditioned on self-stated intentions represented as single items of process advice, and illustrate the implications on willingness to pay for travel time savings of embedding each heuristic in the choice process. Given the controversy surrounding the reliability of self-stated intentions, we introduce a framework in which mixtures of process advice embedded within a belief function might be used in future empirical studies to condition choice, as a way of increasingly judging the strength of the evidence.

Using belief functions, this paper develops a model of the situation of a management team trying to decide if a cost process is in control, or out of control and, thus, in need of investigation. Belief functions allow accounting for uncertainty and information about the cost processes, extending traditional probability theory approaches. The purpose of this paper is to build and investigate the ramifications of that model. In addition, an example is used to illustrate the process.

In the context of Saudi Arabia, this chapter investigates how clustering promotes knowledge sharing and transfer in an emerging, government-directed industry cluster. It is determined that lateral actors play a key facilitating role, and formal and informal mechanisms and interpersonal links among actors support that cluster knowledge exchange. Limited social capital strength and depth and a lack of trust that prevents knowledge sharing are partially explained by the cluster's limited vertical and horizontal actors.

Professionals who carry out the forensic accounting profession must have an extensive knowledge of accounting, as well as an effective knowledge of law, auditing, internal audit, business management, psychology, crime science, and, in particular, computer technologies. In today’s digital business environment, it has become difficult to identify fraudulent transactions with traditional methods. Developments in information (data) and information technology have helped increase anti-fraud control programs and fraud research opportunities. In particular, fraudulent financial reporting disrupts the reliability, accuracy, and efficiency of financial markets in terms of existence and continuity. The forensic accounting profession has been able to improve the effectiveness of inspections by using big data techniques, data analytics, and algorithms (Rezaee, Lo, Ha, & Suen, 2016; Seda & Kramer, 2014; Singleton & Singleton, 2010).

The aim of the author, in this chapter, is to evaluate the contribution of using big data techniques in forensic accounting applications and the skills that will be provided to students while integrating these techniques in forensic accounting trainings. For this purpose, studies on forensic accounting education and their applications were reviewed. In addition, opinions were evaluated by considering the relevant literature about the importance of big data, benefits of big data, use of big data techniques, and interest shown of them.