Data science, intelligence and future analysis
Abbas Bagherian Kasgari; Iman Raeesi Vanani; Maghsoud Amiri; Saeid Homayoun
Abstract
Most traditional fraud detection systems primarily focus on financial criteria to identify financial fraud, often overlooking the potential for fraudulent companies to engage in various types of non-financial misconduct. Recent studies have predominantly highlighted the significance of financial data ...
Read More
Most traditional fraud detection systems primarily focus on financial criteria to identify financial fraud, often overlooking the potential for fraudulent companies to engage in various types of non-financial misconduct. Recent studies have predominantly highlighted the significance of financial data as the sole indicator of fraud, neglecting the exploration of non-financial or Environmental, Social, and Governance (ESG) metrics as supplementary predictors. This research aims to enhance fraud prediction by integrating financial and ESG data through sophisticated machine learning and deep learning models. It examines the effectiveness of supervised machine learning and deep learning algorithms in detecting financial fraud over a 10-year period ending in 1401. This study innovatively demonstrates that a hybrid model, which combines financial and non-financial criteria, yields superior predictive accuracy for financial fraud than models based solely on financial data. The results of this study, addressing the first research question, indicate that among various machine learning and deep learning algorithms, the classification or bagging algorithm demonstrated superior efficiency. Furthermore, in response to the second research question, it was found that the dataset encompassing all features—integrating both financial and non-financial data—outperformed those datasets limited to either financial or non-financial data alone. The research results indicated that the bagging machine learning algorithms act the best with combined feature set including financial and ESG metrics combined. The adoption of the proposed model significantly improves the accuracy and effectiveness of fraud detection systems.
Management approaches in the field of smart
Mehdi Elyasi; Maghsoud Amiri; Seyed Soroush Ghazinoori; Neda Jomehri
Abstract
The current digital revolution has given rise to a new organizational form, the Platform company. Today, the most valuable companies in the world and the first ones with a market value of more than a trillion dollars are platform companies. The Platform Economy is developing at an exponential rate and ...
Read More
The current digital revolution has given rise to a new organizational form, the Platform company. Today, the most valuable companies in the world and the first ones with a market value of more than a trillion dollars are platform companies. The Platform Economy is developing at an exponential rate and has become a top priority for governments across the world. The present study aims to provide a framework for evaluating the Digital Platform Economy at the international level. Utilizing a systematic review and meta-synthesis approach, the Platform Economy dimensions are identified as Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment and by extracting relevant indicators from international reports, the Platform Economy Composite Index is developed. Using the Partial Least Squares-Path Modelling (PLS-PM) method and specifically the Higher-Order Construct model, the measurement model is validated, and by employing a non-compensatory aggregation method, the Platform Economy Composite Index ranks 128 countries. The study is concluded by scrutinizing Iran’s current status regarding the enabling factors of the platform economy and identifying its strengths and weaknesses and providing recommendations for improvement. The results indicate that although Iran’s current status in terms of demand-side enablers is relatively good, it faces serious issues in terms of supply-side enablers.IntroductionThe emergence and proliferation of the application of big data, cloud computing and new algorithms have led to the formation of a platform economy built around platform companies. This new generation of digital businesses has disrupted several industries and often are startups that have become new market leaders (Acs et al., 2021).Companies like Apple, Microsoft, Google, Amazon, and Meta are examples of such businesses. The market value of these five companies was close to 9 trillion dollars in December 2021 (Companiesmarketcap, 2021), equivalent to 9.5% of the global GDP (O'Neill, 2021).The immense value creation power of the platform economy has made its the key to inclusive economic growth for both advanced and developing economies, and a catalyst for economic and social leapfrogging opportunities in developing countries (Chakravorti et al., 2019). However, platform economy literature has neglected the assessment of the national factors that have given rise to the platforms and therefore, it is necessary to identify the national factors that enable the emergence and growth of digital platforms (Hermes et al., 2020).However, a review of the research literature indicates that the evaluation of platform economy at the national level has not made much progress and the few studies that have attempted this (Chakravorti et al., 2019; Morvan et al., 2016), have been primarily focused on the developed countries and therefore are more compatible with the conditions of these countries. Consequently, policymakers in developing countries, despite having different conditions, must refer to the experiences of developed countries for the development of platform economy policies. Since there is a limited understanding of the effectiveness of such policies on enhancing the efficiency of the platform economy, this approach can be challenging (Szerb et al., 2022).Against this background, this study aims to develop a comprehensive framework for evaluating the platform economy of countries at different levels of development. Utilizing a systematic review and meta-synthesis approach, the enabling dimensions of the platform economy are identified as Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment. Based on this framework and by extracting relevant indicators from international reports, the Platform Economy Composite Index is constructed. The study concludes by closely examining Iran's current situation in terms of the enabling factors of the platform economy. It identifies the country's strengths and weaknesses and offers recommendations for improvement. Research Question(s)The main question of this research is defined as follows:What are the dimensions and components of a comprehensive framework for evaluating the platform economy and how can a composite index be developed using this framework?Literature ReviewDigital platforms serve as intermediaries that facilitate interactions and exchange of values between at least two different and interdependent user groups in platform ecosystems (Drewel et al., 2021).There is no consensus on the definition of the platform economy, and different terms such as Sharing Economy, Collaborative Economy, Access Economy, and Gig Economy have been used to refer to this phenomenon in academic and policy research (Riso, 2019). However, the term Platform Economy has gained more prevalence due to its more inclusive connotations. Kenney and Zysman (2016) consider the term Platform Economy a “more neutral term as they believe it encompasses a growing number of digitally enabled activities in business, politics, and social interaction”. Here, the platform economy is defined as a value creation system consisting of platforms and platform ecosystems (Dufva et al., 2017).A review of the research literature indicates that the evaluation of platform economy at the national level has not made much progress (Szerb et al., 2022), and the few studies that have evaluated the platform economy at the national level, have been primarily focused on developed economies e.g., Morvan et al. (2016) developed Platform Readiness Index to evaluate readiness level of 16 countries of G20 countries in the development of platforms. Furthermore, to the best of our knowledge there is no systematic review focused on the identification of platform economy enablers at the national level. Therefore, utilizing a systematic review and meta-synthesis approach, this study aims to develop a comprehensive framework for evaluating the platform economy of countries at different levels of development.MethodologyThe main steps for developing a composite index include developing a conceptual framework, selecting individual indicators, imputation of missing data, multivariate analysis, normalization, aggregation, and composite index validation (OECD, 2008).The first step of constructing a composite index is the development of a conceptual framework that encompasses the dimensions and components of the phenomenon being measured. To this end, based on a meta-synthesis approach, a systematic review was conducted. The meta-synthesis approach was implemented using the Noblit and Hare (1988) seven-step method: 1. getting started; 2. deciding what is relevant; 3. reading the studies; 4. determining how the studies are related; 5. translating the studies into one another; 6. synthesizing translations; 7. expressing the synthesis. This resulted in the extraction of 6 dimensions and 16 components as platform economy enablers which are presented in the proposed conceptual framework for the platform economy evaluation.Based on this framework and by extracting relevant indicators from international reports, the Platform Economy Composite Index is constructed. Using the Partial Least Squares-Path Modelling (PLS-PM) method and specifically the Higher-Order Construct model, the measurement model is validated, and by employing a non-compensatory aggregation method, the Platform Economy Composite Index ranks 128 countries.ConclusionThis study attempted to develop a comprehensive framework for evaluating the efficiency of the platform economy of countries at different levels of development. Using a systematic review and meta-synthesis approach, Digital Users, Digital Entrepreneurs, Digital Platforms, Digital Infrastructure, Innovation Capacity, and Institutional Environment were identified as the evaluating dimensions of the platform economy.Furthermore, Iran's current situation in terms of the enabling factors of the platform economy was closely examined and country's strengths and weaknesses were identified. The results from the Platform Economy Composite Index indicate that while Iran is in a relatively good position regarding demand-side enablers, it is facing significant challenges with supply-side enablers.Keywords: Digital Platform, Platform Economy, Composite Index, International Ranking.
Management approaches in the field of smart
Fatemeh Mohammadnezhad Chari; Jahanyar Bamdadsoofi; Iman Raeisi Vanani; Maghsoud Amiri
Abstract
The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy ...
Read More
The present paper is conducted through exploratory and inductive approach in order to achieve a business model for data marketplaces. The research paper could be considered as a first attempt in the field of data marketplaces and their business models in Iran. The paper is based on two iterative taxonomy approach that is first introduced by Nickerson et al.(2013).Mixing of a systematic way on current literatures along with structured interviews by some experts who are involved in this area is applied to gain the main objectives.Our results provide the main bloc of the presented archetype with three sub blocs ،attributes and specifications that is titled value proposition. The sub blocs are named value creation، value capture and value delivery.
Introduction
Recently many online data trading platforms have emerged as a new business paradigm to respond to society’s fundamental needs and rights for specific data. On these data marketplaces, service providers buy raw data from device and application owners or collect it from contributors to offer enriched and value-added data to data consumers such as scientists, businesses, etc. The aim of this study is to develop an architecture of business model for data marketplaces in order to get better a understanding of their business logic.
Hence, the research questions are as follows:
1-What are the attributes of construct blocs of the data marketplace business model?
2- What are the specifications of each attribute in any construct bloc of data marketplace business model?
Literature Review
The concept of business model has evolved during recent years by refining its components. There are different types of business model constructs across the literature, from 9 blocs of Osterwalder and Pigneur (2010) to the business model with 3 blocs proposed by “Hautes Etudes Commerciales de Paris” called Odyssey 3.14. The most famous business model construct includes four components (blocs) with “value proposition” as a core component which refers to the benefits that customers receive and why the company is the best choice for them. (Magretta, 2002; Casadesus et al.,2010). The three sub-constructs include “value creation”, “value delivery”, and “value capture” (Teece, 2010). “Value creation” reflects the products and services offered by the company and also the key activities, resources and processes, and partners. “Value delivery” refers to the corporate interactions with the market and “Value capture” concerns the revenue streams and cost structures which make the profit equation.
Methodology:
The present study is conducted through exploratory and inductive approach to achieve an archetype of a business model for data marketplaces. To the best of our knowledge, this research paper could be considered a first attempt in the field of data marketplaces business model design in Iran.
The methodological orientation of this research is based on two iterative taxonomy approaches that is first introduced by Nickerson et., al (2013). Mixing of a systematic way on current works of literature along with structured interviews by some experts who are involved in this area is applied to gain the main objective and answer the research questions. Through this approach, three following steps are taken in a systematic and repetitive manner.
Systematic literature review of 43 scientific documents and their content analysis
Conducting structured interviews with 5 experts
Visiting 4 online data platforms and data marketplaces websites
Results and discussion:
Findings indicate that the data marketplace business model archetype consists of “value proposition” as a main component with 8 attributes including data goods, technological products, infrastructural services, brokery and curation services, operating services, supporting services, the domain of activities, and proprietary forms. The three sub-components’ attributes concerning the data marketplace business model are figured out as follows:
“Value creation” as a sub-construct with six attributes including key partners, key activities, key processes, key products and services, transaction orientations, data sourcing and data origin, and data time -frame.
“Value delivery” as a second sub-component includes five attributes such as data accessibility, output frames, target audiences, trustworthy mechanisms, and privacy preservation mechanisms.
“Value capture” with five attributes including price discovery mechanisms, payment mechanisms, revenue streams, costing mechanisms, and pricing models.
To sum up, these 24 attributes include more than 100 specifications. All of these specifications are profoundly described in detail across the article. Some attributes have more than 8 specifications such as key partners, key activities, or key processes while others have fewer. Most of the specifications are not exclusive, since a particular platform’s attributes may include one or multiple specifications. For example, a particular data platform could have multiple pricing models such as “pay-per-use”, “freemium” or “flat rate”.
Conclusion
Our taxonomy of the data marketplace business model could be extended by four major concerns of data platforms which are data quality evaluation, data pricing mechanisms, secure data trading and truthfulness, and privacy protection mechanisms. Some aspects of the data marketplace business model are inherently contradictory and a trade-off has to be applied between them. For example, European General Data Protection Regulation (GDPR) tries to make a trade-off between data trading transparency and individual privacy protection. Furthermore, participants’ conflicting interests in order to gain a win-win result have to be considered in all online data platform business models. We suggest future researchers in computer science and IT management science, and data scientists extend our archetype by using methods such as text mining techniques and web crawling.
Keywords: Data Marketplace, Business Model, Archetype, Taxonomy.
