Special Issue "Entrepreneurship and Business Models in the Digital Era"

A special issue of Journal of Risk and Financial Management (ISSN 1911-8074). This special issue belongs to the section "Business and Entrepreneurship".

Deadline for manuscript submissions: closed (20 September 2022) | Viewed by 7885

Special Issue Editor

Dr. Salvatore Ammirato
E-Mail Website
Guest Editor
Department of Mechanical, Energy and Management Engineering, University of Calabria, Via P. Bucci, 46\C, 87036 Rende, CS, Italy
Interests: business process management; sustainability; ICT; tourism; digital entrepreneurship
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The COVID-19 pandemic has produced more digital transformation in the past 10 months than we have seen in the last decade, “with every transformation effort already underway finding itself accelerated, and at scale”. (Newman, 2020). The accelerated diffusion of digital technologies is producing a deep impact on both socioeconomic environments worldwide and on the everyday life of individuals in an unprecedented way (Olanrewaju et al., 2020). From a business perspective, digital entrepreneurship is exploding around the globe, offering public and private investors opportunities to stimulate speedy growth of digital enterprises (DEs) (Nambisan, 2019). DEs capitalize on the internet by merging digital technologies, e.g., cloud computing, cybersecurity, blockchain, Internet of Things, big data, and artificial intelligence, in many different industries (finance, tourism, public administration, manufacturing, education, agribusiness, and others) (Ammirato et al., 2019). In fact, both DEs, which harness technology to improve their performances and customer service through digitalization of business, and more traditional enterprises are reinventing their business models in order to reduce financial risks and to gain more flexibility and agility in engaging with customers, stakeholders, and new strategic partners (e.g., universities, research centers, venture capitalists, public administrations) (Linzalone et al., 2020).

This Special Issue welcomes both research papers and case studies devoted to analyzing the evolution of entrepreneurship and business models in the digital era.

Suggested topics suitable and of interest include:

  • Digital revolution and entrepreneurial revolution;
  • Entrepreneurs behaviors and decision-making process;
  • Digital business model innovation;
  • New technologies;
  • Impact of new technologies at industry and/or firm level;
  • Business methods and business thinking;
  • Business processes management and innovation;
  • Emerging models of risk impacted by technology and finance;
  • Predictions of changes in behaviors and business over the next millennium.

References:

Ammirato, Salvatore, Francesco Sofo, Alberto Michele Felicetti, Nina Helander, and Heli Aramo-Immonen. 2019. A new typology to characterize Italian digital entrepreneurs. The International Journal of Entrepreneurial Behaviour and Research 26:224-245

Linzalone, Roberto, Giovanni Schiuma, and Salvatore Ammirato. 2020. Connecting Universities with Entrepreneurship through Digital Learning Platform: functional requirements and education-based knowledge exchange activities. The International Journal of Entrepreneurial Behaviour and Research 26: 1525-1545.

Nambisan, Satish, Mike Wright, and Maryann Feldman. 2019. The digital transformation of innovation and entrepreneurship: Progress, challenges and key themes. Research Policy 48: 1037-1073.

Newman, Daniel. 2020. Top 10 Digital Transformation Trends For 2021. Forbes. Available online: https://www.forbes.com/sites/danielnewman/2020/09/21/top-10-digital-transformation-trends-for-2021/?sh=7c7ba2a0c6f4 (access on 20 November 2020)

Olanrewaju, Abdus-Samad Temitope, Mohammad Alamgir Hossain, Naomi Whiteside, and Paul Mercieca. 2020. Social media and entrepreneurship research: A literature review. International Journal of Information Management 50: 90-110

Prof. Dr. Salvatore Ammirato
Guest Editor

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Keywords

  • Entrepreneurship
  • Digital platforms
  • Business models/processes
  • Knowledge/information management
  • Start-up/scale-up companies
  • Performance/risk management
  • Academic entrepreneurship
  • Fintech
  • eTourism
  • Industry 4.0
  • eAgribusiness
  • Digital education
  • Digital marketing
  • Social media
  • Internet of Things
  • Artificial intelligence/machine learning
  • Cybersecurity

Published Papers (5 papers)

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Research

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Article
Grocery Apps and Consumer Purchase Behavior: Application of Gaussian Mixture Model and Multi-Layer Perceptron Algorithm
J. Risk Financial Manag. 2022, 15(10), 424; https://doi.org/10.3390/jrfm15100424 - 23 Sep 2022
Cited by 2 | Viewed by 1103
Abstract
The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. [...] Read more.
The purpose of this study is to investigate and compare the popularity of common grocery apps in Hungary as well as Iran. The data were gathered from Iranian and Hungarian users who had at least one online purchase experience using a grocery app. A Gaussian mixture model (GMM) and multi-layer perceptron (MLP) are used as supervised and unsupervised machine learning algorithms with Python programming to cluster customers and predict consumer behavior. The results revealed that Wolt in Hungary and Snappfood in Iran are the most popular grocery apps. Users in Iran are divided into three groups of users of app services and the type of full covariance has higher accuracy compared to the other three types (96%). Meanwhile, we found that the five apps used in Hungary have provided 95% accuracy from the users’ point of view based on the diagonal covariance. The MSE value (overfitting and cross-validation) is less than 0.1 in the MLP algorithm, which shows an acceptable amount of error. The results of overfitting indicate the proper fit of the MLP model. The findings of this study could be important for managers of online businesses. In the clustering section, the accuracy and value of consumer demographic information have been emphasized. Additionally, in the classification and prediction section, a kind of “customization” has been performed with an emphasis on market segmentation. This research used GMM and MLP machine learning algorithms as a creative way to cluster and classify consumers. Full article
(This article belongs to the Special Issue Entrepreneurship and Business Models in the Digital Era)
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Article
Parsimonious AHP-DEA Integrated Approach for Efficiency Evaluation of Production Processes
J. Risk Financial Manag. 2022, 15(7), 293; https://doi.org/10.3390/jrfm15070293 - 30 Jun 2022
Cited by 2 | Viewed by 745
Abstract
This document proposes an innovative composite indicator to measure and control the performance of production processes. The aim is to provide a tool for controlling the efficiency of the processes, assessed in relation to the number and the impact of occurring “errors”, which [...] Read more.
This document proposes an innovative composite indicator to measure and control the performance of production processes. The aim is to provide a tool for controlling the efficiency of the processes, assessed in relation to the number and the impact of occurring “errors”, which can take into account the opinion of experts in the specific domain. This allows for the definition of a more realistic and effective decision support system. Our composite indicator is based on an integrated approach based on Data Envelopment Analysis (DEA), and a new multi-criteria method such as Parsimonious Analytical Hierarchy Process (PAHP). The results obtained on a real test case, based on the automotive production domain, show that the composite indicator built with PAHP-DEA allows us to have clear evidence of the efficiency level of each process and the overall impact of errors on all the processes under evaluation. From a methodological point of view, we have for the first time combined the new thrifty AHP with the DEA. From an application point of view, this work introduces a new tool capable of evaluating the efficiency of production processes in an extremely competitive sector, exploiting the knowledge of the experts in the domain of errors, internal processes and the dynamics that occur. Full article
(This article belongs to the Special Issue Entrepreneurship and Business Models in the Digital Era)
Article
Conceptual Framework—Artificial Intelligence and Better Entrepreneurial Decision-Making: The Influence of Customer Preference, Industry Benchmark, and Employee Involvement in an Emerging Market
J. Risk Financial Manag. 2021, 14(12), 604; https://doi.org/10.3390/jrfm14120604 - 13 Dec 2021
Cited by 4 | Viewed by 2475
Abstract
Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach [...] Read more.
Purpose: Technology initiatives are now incorporated into a wide range of business domains. The objective of this paper is to explore the possible effects that Artificial intelligence systems have on entrepreneurs’ decision-making, through the mediation of customer preference and industry benchmark. Design/methodology/approach: This is a non-empirical review of the literature and the development of a conceptual model. Searches were conducted in key academic databases, such as Emerald Online Journals, Taylor and Francis Online Journals, JSTOR Online Journals, Elsevier Online Journals, IEEE Xplore, and Directory of Open Access Journals (DOAJ) for papers which focused on Artificial intelligence (AI), Entrepreneurial decision-making, Customer preference, Industry benchmarks, and Employee involvement. In total, 25 articles met the predefined criteria and were used. Findings: The study proposes that Artificial intelligence systems can facilitate better decision-making from the entrepreneurial perspective. In addition, the study demonstrates that employees, as stakeholders, can moderate the relationship between Artificial intelligence systems and better decision-making for entrepreneurs with their involvement. Moreover, the study demonstrates that customer preference and industry benchmark can mediate the relationship between Artificial intelligence systems and better entrepreneur decision-making. Research limitations/implications: The study assumes a perfect ICT environment for the smooth operation of Artificial intelligence systems. However, this might not always be the case. The study does not consider the personal disposition of entrepreneurs in terms of ICT usage and adoption. Practical implications: This study proposes that entrepreneurial decision-making is enriched in an environment of Artificial intelligence systems, which is complemented by customer preference, industry benchmark, and employee involvement. This finding provides entrepreneurs with a possible technological tool for better decision-making, highlighting the endless options offered by Artificial intelligence systems. Social Implications: The introduction of AI in the business decision-making process comes with many social issues in relation to the impact machines have on humans and society. This paper suggests how this new technology should be used without destroying society. Originality/value: This conceptual framework serves as a valuable organizational spectrum for entrepreneurial development. In addition, this study makes a valuable contribution to entrepreneurial development through Artificial intelligence systems. Full article
(This article belongs to the Special Issue Entrepreneurship and Business Models in the Digital Era)
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Article
Family Business in the Digital Age: The State of the Art and the Impact of Change in the Estimate of Economic Value
J. Risk Financial Manag. 2021, 14(7), 301; https://doi.org/10.3390/jrfm14070301 - 02 Jul 2021
Cited by 2 | Viewed by 1692
Abstract
Throughout the review of the most relevant literature on family businesses and business valuation, this work pursues a twofold purpose: to explore the possible evolutionary scenarios of family businesses in the era of digitalisation, highlighting their role and purpose; and to determine the [...] Read more.
Throughout the review of the most relevant literature on family businesses and business valuation, this work pursues a twofold purpose: to explore the possible evolutionary scenarios of family businesses in the era of digitalisation, highlighting their role and purpose; and to determine the valuation approaches that may be applied to them, also in light of the different role that intangible assets deriving from their digitalisation may assume. Therefore, after a description of the most relevant changes related to the digital transformation of the FB, the focus will be set on their valuation, paying special attention to the choice of the most appropriate methodology for “grasping” the aforementioned changes. Family businesses, in fact, due to their distinctive traits and the various estimation opportunities, require a dynamic business valuation process that has to be projected into the future and suitable for estimating those intangible assets that strongly characterise these types of companies, inasmuch as they are related to the implicit components that are strongly connected to the ownership and are a result of knowledge, strategic adaptability, and product innovation, and their possible impact on the risks and their expected flows. Thus, throughout a systematic literature review, the study provides, on the one hand, a clearer representation of the state of the art of the FB valuation in the digital age; on the other hand, it highlights the characteristics and peculiarities of “novel” FBs whose valuation needs to be conducted with due care. Full article
(This article belongs to the Special Issue Entrepreneurship and Business Models in the Digital Era)

Review

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Review
Multi-Criteria Decision Making in Production Fields: A Structured Content Analysis and Implications for Practice
J. Risk Financial Manag. 2022, 15(10), 431; https://doi.org/10.3390/jrfm15100431 - 27 Sep 2022
Viewed by 668
Abstract
As the complexity of decision-making problems and the competitiveness in which companies find themselves carrying out their activities increase, the need to use tools that can help Decision-Makers (DM) make more informed and more effective choices increases. Multi-Criteria Decision Making (MCDM) represents a [...] Read more.
As the complexity of decision-making problems and the competitiveness in which companies find themselves carrying out their activities increase, the need to use tools that can help Decision-Makers (DM) make more informed and more effective choices increases. Multi-Criteria Decision Making (MCDM) represents a valid decision support tool capable of simplifying the process of choosing, ranking or sorting the alternatives that characterize the problem. This work aims to investigate with a structured content analysis if MCDMs are used in an extremely complex and competitive sector such as the automotive sector. The work also aims to describe and explore in the existing literature the role that entrepreneurs (our decision-makers) play in the construction of MCDM methods. The results show that MCDMs are widely used in different application areas in the domain of interest and that the decision maker is involved in several phases of construction of the MCDM methods. Full article
(This article belongs to the Special Issue Entrepreneurship and Business Models in the Digital Era)
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