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This paper attempts to apply complex theory in futures studies and addresses prediction challenges when the system is complex. The purpose of the research is to design a framework to engineer the futures in complex systems where components are divers and inter-related. Relations cannot be interpreted by cause and effect concept.

First, the authors shaped a conceptual framework based on engineering, complex theory and uncertainty. To extract tacit knowledge of experts, an online questionnaire was developed. To validate the proposed framework, a workshop method was adapted with NetLogo simulation.

Opinion of participants in the workshop which is collected through quantitative questionnaire shows that the framework helps us in understanding and shaping scenarios. Harnessing the complexity in developing the futures was the main objective of this paper with the proposed framework which has been realized based on the experience gained from the workshop.

Iterative processes are very important to harness the complexity in systems with uncertainty. The novelty of the research is a combination of engineering achievements in terms of computation, simulation and applying tools with futures studies methods.

This paper aims to propose a new benchmarking framework that uses a series of existing intuitive and analytical methods to systematically capture both objective data and subjective beliefs and preferences from a group of decision makers (DMs).

The proposed framework combines the excellence model developed by the European Foundation for Quality Management with the Rembrandt method, the entropy concept, the weighted‐sum approach, and the theory of the displaced ideal. Hard data and personal judgments are synthesized to evaluate a set of business units (BUs) with two overall performance scores plotted in a four quadrant model.

The two performance scores are used to benchmark the performance of the BUs in accordance with their Euclidean distance from the “ideal” BU. Quadrants are used to classify the BUs as efficacious, productive ineffectual, proficient unproductive, and inefficacious. The efficacious BUs, referred to as “excellent”, fall in the competency zone and have the shortest Euclidean distance from the ideal BU relative to their peers.

The benchmarking framework presented in this study has some obvious attractive features. First, the generic nature of the framework allows for the subjective and objective evaluation of a finite number of BUs by a group of DMs. Second, the information requirements of the framework are stratified hierarchically allowing DMs to focus on a small area of the large problem. Third, the framework does not dispel subjectivity; it calibrates the subjective weights with the objective weights determined through the entropy concept.

Given the growing emphasis among scholars, practitioners and policymakers on financial sustainability, this study aims to explore the applicability of machine learning techniques in predicting the financial performance of microfinance institutions (MFIs).

This study gathered 9,059 firm-year observations spanning from 2003 to 2018 from the World Bank's Mix Market database. To predict the financial performance of MFIs, the authors applied a range of machine learning regression approaches to both training and testing data sets. These included linear regression, partial least squares, linear regression with stepwise selection, elastic net, random forest, quantile random forest, Bayesian ridge regression, K-Nearest Neighbors and support vector regression. All models were implemented using Python.

The findings revealed the random forest model as the most suitable choice, outperforming the other models considered. The effectiveness of the random forest model varied depending on specific scenarios, particularly the balance between training and testing data set proportions. More importantly, the results identified operational self-sufficiency as the most critical factor influencing the financial performance of MFIs.

This study leveraged machine learning on a well-defined data set to identify the factors predicting the financial performance of MFIs. These insights offer valuable guidance for MFIs aiming to predict their long-term financial sustainability. Investors and donors can also use these findings to make informed decisions when selecting their potential recipients. Furthermore, practitioners and policymakers can use these findings to identify potential financial performance vulnerabilities.

This study stands out by using a global data set to investigate the best model for predicting the financial performance of MFIs, a relatively scarce subject in the existing microfinance literature. Moreover, it uses advanced machine learning techniques to gain a deeper understanding of the factors affecting the financial performance of MFIs.

This study aims to examine organizational factors (i.e. top management support, training and education, enterprise‐wide communication) that may influence the enterprise resource planning system implementation success in Iran.

Empirical data were collected via a survey questionnaire. The questionnaires were distributed to selected managers of companies adopting ERP systems in Iran.

The results indicate that the companies' top management must provide full support and commitment to the project if the system is to be successful. In addition, management must also ensure the plans are communicated and understood by the entire company. Finally it is also illustrated that adequate training and education pertaining to the systems must be given to all users to ensure that they are able to use the system effectively and efficiently thus contributing to their satisfaction which will subsequently influence the implementation success.

The ERP implementation success dimensions were measured using subjective and perceptual measures. This was due to the difficulty in securing the related factual data from the participating organizations.

The findings may help companies planning to implement an ERP system to strategise their efforts and process to ensure successful implementation.

This study examines how organizational factors, namely top management support, training and education as well as enterprise wide communication among ERP users, affect ERP implementation success in Iran.

The purpose of this paper is to demonstrate a deeper understanding about the reasons behind difference in previous studies’ results in the field of audit quality determinants.

A meta-analysis method is employed in which 52 studies including 40 international studies from authentic scientific articles during the year 2000–2015 and 12 national studies out of authentic national scientific articles from 2001 to 2015 are taken to account as sample studies. Audit firm size, auditor tenure and auditor specialization are set as independent variables and audit quality is the only dependent variable in the current paper.

The results indicate that audit firm size and auditor specialization are positively associated with audit quality. In other words, contracting with larger audit firm and specialized auditor results in delivering higher quality audit services.

The current study is the first study to be conducted in the field of audit quality determinants. The results may be beneficial both for standard setters as well practitioners in a way that it provides evidence that contributes to basis policy and audit-standard makers about domination and determinants of audit quality.

The purpose of this paper is to identify factors that are crucial for the successful implementation of enterprise resource planning (ERP) systems. Although there are many factors that influence the success, this study focuses on factors related to the ERP project environment, namely, project management, team composition and competence, and business process reengineering.

The study was conducted using a survey questionnaire distributed to ERP users in Iranian organizations. In total, 384 responses were collected and analyzed.

A significant relationship was found between project management and team composition with ERP implementation success. The better the project management activities the more likely the implementation will be successful. Likewise, the possibility of successful implementation is higher when the ERP team is more coordinated and experienced.

ERP adopting organizations and managers could gain an understanding of the complexities inherent in ERP installations to avoid barriers and increase the likelihood of achieving desired results. The outcomes of this study are also useful to ERP vendors and consultants to prepare some strategies to overcome the misfit between their ERP products and ERP adopting organizations in developing countries.

This study is one of the few that examine the success of ERP implementation from the perspective of key stakeholders (operational/unit/functional managers). It has contributed to academic research by producing empirical evidence to support the theories of critical success factors and ERP implementation success. The findings may be useful to ERP vendors and other organizations in other countries, as they could be used as a guideline for future ERP adoption and implementation.