Search results

This note presents a method of teaching accounting problems involving the use of the effective interest method such as bonds, notes, capital leases, and installment sales. The method is conceptually sound and simpler than the traditional method found in current textbooks and stimulates student interest by focusing on the economics of the transaction and relating it to real-life examples.

To assess its pedagogical efficacy, the method was tested in the introductory and intermediate accounting classes. In both courses, the results indicate that students' test scores are significantly higher under the new method than the traditional method. It is hoped that this evidence of the superiority of the new method in a classroom environment will spur its adoption by instructors and textbook writers.

While cost allocation decisions attract considerable attention in the management accounting literature, many studies are contradicting and inconclusive. They often seek to develop product or service weights in order to make operating decisions with the sole objective of maximizing the firm’s profitability. But before developing these weights, the studies must first rank these products – which is a complex endeavor that is often driven by many hierarchical financial and nonfinancial goals and objectives. Ranking is also difficult due to using such complex concepts as time, uncertainty, cost, and interdependencies between accounting systems and manufacturing systems and among the products of the product mix. These concepts are inherently fuzzy and coextensively applied often with a confluence of variables operating simultaneously.

This paper applies an advanced mathematical model to account for a hospital cost allocation decisions in treating spinal cord injuries (SCI). The model combines the powers of fuzzy set theory (Zadeh, 1965) and the analytic hierarchy process (Saaty, 1978). The precise ratings required in the conventional analytic hierarchy process but practically hard to obtain are replaced by naturally semantic variables by using the fuzzy set concept. de Korvin and Kleyle’s (1999) fuzzy-analytic-hierarchical process (FAHP) then develop these ambiguous variables. FAHP can help to optimize decisions involving ambiguous variables and the web of prioritized strategies and goals of cost leadership, product differentiation, financial objectives of earnings, cash flows, and market share and nonfinancial goals such as tradition and owners’ convictions and philosophies.

We use data from seven Michigan SCI facilities in applying the FAHP model to rank and otherwise develop more optimal strategies and goals and compare our results to the decisions of hospital management.

This research provides accounting-ethics authors and administrators with a benchmark for accounting-ethics research. While Bernardi and Bean (2010) considered publications in business-ethics and accounting’s top-40 journals this study considers research in eight accounting-ethics and public-interest journals, as well as, 34 business-ethics journals. We analyzed the contents of our 42 journals for the 25-year period between 1991 through 2015. This research documents the continued growth (Bernardi & Bean, 2007) of accounting-ethics research in both accounting-ethics and business-ethics journals. We provide data on the top-10 ethics authors in each doctoral year group, the top-50 ethics authors over the most recent 10, 20, and 25 years, and a distribution among ethics scholars for these periods. For the 25-year timeframe, our data indicate that only 665 (274) of the 5,125 accounting PhDs/DBAs (13.0% and 5.4% respectively) in Canada and the United States had authored or co-authored one (more than one) ethics article.

Although lean thinking is deemed to be a gold standard of modern production management, a lot of scepticism still remains regarding its applicability in small- and medium-sized enterprises (SMEs). The purpose of this paper is to understand the perception of lean in SMEs and establish the relationship between lean adoption and operational performance.

With the help of a survey, data were collected from 425 SMEs in India and analyzed using structural equation modeling.

Operational performance of the firms was found to be positively related to lean implementation.

This study also furnishes practitioners with a better understanding of lean thinking in SMEs and its impact on performance.

Effective inventory management is crucial for SMEs due to limited resources and higher risks like cash flow, storage space, and stockouts. Hence, the aim is to explore how technology and know-how can be integrated with inventory practices and impact operational performance.

The basis of the analysis was collecting papers from a wide range of databases, which included Scopus, Web of Science, and Google Scholar. In the first phase of the process, a search string with as many as nine related keywords was used to obtain 175 papers. It further filtered them based on their titles and abstracts to retain 95 papers that were included for thorough analysis.

The study introduced innovative methods of measuring inventory practices by exploring the impact of know-how. It is the first of its kind to identify and demonstrate how technical, technological, and behavioral know-how can influence inventory management practices and ultimately impact the performance of emerging SMEs. This study stands out for its comprehensive approach, which covers traditional and modern inventory management technologies in a single study.

The study provides valuable insights into the interplay between technical, technological, and behavioral know-how in inventory management practices and their effects on the performance of emerging SMEs in Industry 5.0 in the light of RBV theory.

The RBV theory and the Industry 5.0 paradigm are used in this study to explore how developing SMEs' inventory management practices influence their performance. This study investigates the effects of traditional and modern inventory management systems on business performance. Incorporating RBV theory with the Industry 5.0 framework investigates firm-specific resources and technological advances in the current industrial revolution. This unique technique advances the literature on inventory management and has industry implications.

One of the most decisive objectives of human resource management (HRM), essential for business performance, is to attract, maintain and engage a qualified workforce. This study aims to identify and prioritize the antecedents of employer branding, which are considered extremely important by potential applicants when they choose to join a particular employer.

The authors have adopted a popular multicriteria decision-making technique fuzzy analytical hierarchy process, to prioritize the identified antecedents. The modified Delphi techniques with the 22 experts have been conducted to validate the identified antecedents. Structured questionnaires were discussed, and their reliability was assessed using the expert’s opinion.

Results of this study indicate that potential applicants consider career advancement opportunities as the most important enabler that persuades them to join a particular employer. Future employees are also motivated to join if they are offered attractive compensation packages, which are distinct from competitors. Furthermore, corporate social responsibility is evolving as an antecedent of employer branding as potential candidates are attracted to employers who are socially responsible.

Employer branding can serve as a strategic HRM technique for any business seeking to attract, recruit, retain and involve outstanding employees, as the success of the organization is determined by how its image is built, communicated and promoted. This study provides insights for HR managers and practitioners who can think of developing an effective employer brand communication that offers a distinct and in-imitable image and reputation as an employer of choice.

This study is unique, as it offers meaningful visions to HR practitioners and experts for designing employer branding strategies for attracting potential applicants to join their organizations.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence.

During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first.

The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.