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Enterprise Resource Planning (ERP) software which is a knowledge-based design on the interconnective communication of business units and information share, ensures that business processes such as finance, production, purchasing, sales, logistics and human resources, are integrated and gathered under one roof. This integrated system allows the company to make fast and accurate decisions and increases its competitiveness. Therefore, for an enterprise, choosing the suitable ERP software is extremely important. The aim of this study is to present new research on the ERP software selection process by clarifying the uncertainties and find suitable software in a computational way.

ERP selection problem design includes uncertainties on the expert opinions and the criteria values using intuitionistic fuzzy set theory and interval grey-numbers to MACBETH multi criteria decision making method. In this paper, a new interval grey MACBETH method approach is proposed, and the degree of greyness approach is used for clarifying the uncertainties. Using this new approach in which grey numbers are used, it is aimed to observe the changes in the importance of the alternatives. Moreover, the intuitionistic fuzzy set method is applied by considering the importance of expert opinions separately.

The proposed method is based on quantitative decision making derived from qualitative judgments. The results given under uncertain conditions are compared with the results obtained under crisp conditions of the same methods. With the qualitative levels of experts reflected in the decision process, it is clearly seen that ERP software selection problem area has more effective alternative decision solutions to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during ERP software selection process.

This study contributes to the relevant literature by (1) utilizing the MACBETH method in the selection of the ERP software by optimization, and (2) validating the importance of expert opinions with uncertainties on a proper ERP software selection procedure. So, the findings of this study can help the decision-makers to evaluate the ERP selection in uncertain conditions.

Considered the largest investment for most households, buying a house requires careful and transparent analysis by all parties involved in the transaction. The aim of this paper is to propose a methodological framework allowing for the readjustment of trade-offs among risk evaluation criteria, considered of extreme importance in the lending decision process of mortgage loans.

Multiple criteria decision analysis (MCDA) has proved over the years to be effective and versatile in handling compensations among criteria. Measuring attractiveness is applied by a categorical based evaluation technique (MACBETH) to a pre-established structure of credit-scoring criteria for mortgage lending risk evaluation. This pre-established structure is currently used by one of the largest banks in Portugal.

The framework allowed the authors to provide the credit experts who participated in the study with a more informed, transparent and accurate mortgage-lending risk-evaluation system. The sensitivity and robustness analyses carried out also helped in promoting discussion and supporting the readjustments made.

The study shows the usefulness of using the MACBETH approach to assist credit analysts in making better informed decisions, and opens avenues for further research. However, due to the dependence on the participants involved, extrapolations without proper caution are discouraged.

The credit analysts who participated in this study considered the framework more discerning in terms of Basel directives.

The integration of MACBETH and credit-scoring mechanisms holds great potential for risk assessment and decision support. No prior work reporting the application of MACBETH in terms of mortgage-lending risk-evaluation is known.

The prosthodontics sector is facing major challenges because of scientific and technological advances that imply a clearer definition of lines of action and decision making processes. Measuring quality of service in this sector is a complex decision problem since the perceptions of three main players need to be considered: patients, dentists and dental technicians. This study sought to develop an artificial-intelligence-based (AI-based) method for assessing service quality in the dental prosthesis sector.

Using strategic options development and analysis (SODA), which is grounded on cognitive mapping, and the measuring attractiveness by a categorical based evaluation technique (MACBETH), a constructivist decision support system was designed to facilitate the assessment of service quality in the dental prosthesis sector. The system was tested, and the results were validated both by the members of an expert panel and by the vice-president of the Portuguese association of dental prosthesis technicians.

The methodological process developed in this study is extremely versatile and its practical application facilitated the development of an empirically robust evaluation model in this study context. Specifically, the profile analyses carried out in actual clinics allowed the cases in which improvements are needed to be identified.

Although already applied in the fields of AI and decision making, no prior work reporting the use of SODA and MACBETH for assessing service quality in the prosthodontics sector has been found.

Prior studies use two measures of firm-specific return variation (FSRV): idiosyncratic volatility in absolute and relative terms, the latter of which is also termed stock price nonsynchronicity. Whereas most research focuses on investigating the idiosyncratic volatility puzzle, the authors carry out comparison of these two measures and further investigate which of the two constituents of nonsynchronicity explain the association between FSRV and stock returns, emphasising the importance of assessing which component drives stock returns.

The authors use the US individual stock returns from 1925 to 2016 and define the two measures of FRSV based on the Fama and French (1993) model. Specifically, the authors decompose the relative measure into two components: (i) absolute idiosyncratic volatility and (ii) systematic volatility. The authors conduct various tests based on high-minus-low, zero-investment quintile portfolio sorts and perform the Fama–MacBeth analysis by singling out each component.

The authors find a positive return on the portfolio sorted on relative idiosyncratic volatility or on systematic volatility, but find a negative return sorted on absolute idiosyncratic volatility. The results are robust after controlling for size, BM and other risk characteristics using a double-sorting approach. The Fama–MacBeth regression results show that a positive association between the relative measure and stock returns is driven primarily by the low-systematic-volatility anomaly across firms. The findings are robust to controlling for return residual momentum, skewness, jumps and information discreteness.

Extant research posits the idiosyncratic volatility puzzle and the low-volatility anomaly. The authors emphasize the importance of integrating these two streams of research. This study enhances the understanding of the driving force underlying the relationship between FSRV and cross-sectional stock returns.

When a fund manager leaves, the investment strategy of the fund changes or remains the same. The departing fund manager's resignation is either forced or voluntary. The study investigates the relationship between the portfolio manager's transition and the fund's investment strategy and how the change affects the mutual fund returns in the subsequent period.

The authors examine 148 fund manager changes in India between April 2005–March 2018 using three performance measures: abnormal return (fund return minus benchmark return), Jensen's alpha and Carhart four-factor alpha. The analysis includes an event study methodology, followed by a two-step Fama–MacBeth regression approach.

Contrary to the previous studies conducted in the developed markets, the authors find that fund performance improves irrespective of whether the fund manager change is forced or voluntary. The outperformance after the fund manager's exit is significant for funds belonging to the larger fund families.

In the context of investment management, the authors provide a conceptual framework to understand the effect of fund manager exit on mutual fund performance. The authors substantiate their arguments with empirical evidence. To the best of the authors' understanding, this is the first research to examine the effect of changing mutual fund managers in an emerging market setting.

Industrial companies are continuously looking to improve their business turn-over and choose improvement actions to achieve this objective. Legitimating this choice by only a strategic financial point of view currently has some limits, because additional ones such as quality, delay, resources needs have also to be taken into account. The multicriteria decision aid (MCDA) approaches have been developed to deal with problems such as ranking, sorting or quantification. The purpose of this paper is to describe the operational aspects in MCDA deployment through a case study submitted by the SME Fournier Company.

This paper concerns analytical overall performance models to allow the decision-makers (DMs) to select potential actions to launch regarding given decision criteria. The principles of the main MCDA approaches, outranking and aggregation, are presented. Then representative MCDA methods, ELECTREIII and MACBETH, are successively deployed to a tactical decision problem of business turn-over increase. From the building of their respective preference modelling, decision aid is provided in the form of preference relation or performance function.

A formal framework supplying information to the industrial DMs in order to legitimate the action selection is described. In addition, the DMs feedback highlights the required expertise for the preference modelling.

The article gives a guideline for industrial practitioners to carry out ELECTREIII and MACBETH for their improvement action selection.

SMEs can find an adapted framework for their improvement actions choice dealing with financial and non-financial criteria. It is also enriched by practical considerations concerning the methods deployment for tactical decision problems.

The purpose of this study is to extract factors enabling the digital car sharing enterprises' supply chain resilience (SCR), to interpret different factor prioritizations in terms of industry representatives’ assessments and specialties, and to discuss the results by applying and comparing different ranking techniques.

To achieve the purpose, the factors were identified via an in-depth systematic literature review, and next, these factors were examined by industry representatives to gather the decision matrices, then analytic hierarchy process (AHP) and measuring attractiveness by a categorical based evaluation technique (MACBETH) were applied separately to model the decision problem, and finally the findings were interpreted with different participants’ perspectives.

The findings revealed that the AHP and MACBETH provide nearly identical rankings in terms of main factors by implying the significance of the triple bottom line of sustainability. Therefore, the economic, social, and environmental dimensions of sustainability should be accomplished to obtain a resilient digital car sharing enterprise supply chain. In addition, readiness and agility are the other important factors affecting the enterprises’ resilience, and finally, although digitalization seemed to be the least important one, its sub-factor emerged at the top of the ranking list.

Up to the authors’ knowledge, this is the first study in the literature that focuses on the SCR of car sharing companies, a particular type of digital enterprise, and uses AHP and MACBETH to examine the important factors that might affect the SCR of these companies. Practitioners should take the findings of both methods into account when evaluating the results and determine the short- and long-term strategies accordingly.

Reliable operation of natural gas pipeline (NGP) is a critical factor in Iran’s economic development. NGP projects go through different ecosystems and considerably affect the environment. Environmental analysis is an essential step toward sustainable development. Tackling the challenges, this paper aims to develop a framework to systematically assess the environmental impact of NGPs.

This study proposes a comprehensive framework for environmental impact analysis of NGPs using Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH). MACBETH protocol is used to calculate the overall environmental scores of projects.

The results indicate that the impact of implementing NGPs on protected areas is of the highest priority, while the impact on vegetation covers is of least priority for assessing the environmental impact of NGP.

The practicality and validity of the model in the case of three candidate routes for the Polkale-Neizar project in Iran are examined. According to the results, the third alternative is selected based on its non-interference in protected areas and less environmental impacts. The proposed model can be modified and applied to perform environment appraisal of other linear projects such as energy, road and railway networks.

This model addresses a range of environmental impacts of implementing NGPs at two levels, with the second level being non-additive. The novelty of this study translates into considering the qualitative and quantitative features of each evaluation criterion applied to linear projects simultaneously using a multi-criteria value measurement.

Small- and medium-sized enterprises (SMEs) have become increasingly important in national and international markets because they contribute to the development of local and national economies. SMEs often face serious challenges when competing with multinational companies. The purpose of this paper is to develop a method for assessing SMEs’ competitiveness.

Based on a constructivist epistemology, this study makes an integrated use of cognitive mapping and the measuring attractiveness by a categorical-based evaluation technique (MACBETH). To this end, face-to-face sessions were conducted with a panel of entrepreneurs and senior managers who deal with the challenges of maintaining SME competitiveness every day. The proposed assessment system was tested and validated by the panel members.

The methodological processes adopted in this study provide promising results for decision makers seeking to identify the most competitive SMEs. Specifically, the results emphasize, among other points, the importance of innovation and the human dimension to gaining competitive advantages.

The evaluation system developed in this study is extremely versatile and confirms the usefulness of integrating cognitive mapping and MACBETH to facilitate evaluations of SME competitiveness. However, due to its idiosyncratic and process-oriented nature, generalizations need to be done with caution.

The proposed method can be valuable to researchers seeking to develop mechanisms for evaluating SMEs’ entrepreneurial performance and include specialized know-how and sensemaking in organizational decision-making processes.

The integrated use of cognitive maps and MACBETH contributes to a better understanding of how to assess SMEs’ competitiveness. No prior work reporting the use of this dual methodology in this study context has been found.

Barry and Brown find that returns are higher for securities that have been listed for shorter periods of time after controlling for firm size and a January effect. The purpose of this paper is to examine the robustness of this period of listing effect and test for a post‐listing effect.

By extending the sample and including additional risk measures using both the Fama and Macbeth approach and the Fama and French approach, the author is able to conduct a stronger test of the period of listing effect and jointly test for a post‐listing effect.

The results indicate that there is a period of listing effect but that the effect is driven not by the higher returns of the newly listed firms but by the lower returns of the longest listed firms. The paper also provides additional support for a post‐listing effect in the sample.

While the results support a period of listing effect, they invite the larger question of why longer listed firms underperform. While the Barry and Brown informational risk explanation for period of listing effect may suffice, the results indicate a greater need to understand why returns vary with firm listing age especially for the newly listed and longest listed firms.