From Public E-Procurement 3.0 to E-Procurement 4.0; A Critical Literature Review

Abstract

Public procurement is an important part of public finances; therefore, its management is challenging for the quality of the citizen’s relationship with the public authorities. Existing electronic public procurement optimization tools are systematically attempting to standardize procedures by improving access to information and transparency in management. Nevertheless, the next day requires the definition of the transition to modern tools and technologies of the fourth industrial revolution. This study attempts to identify common and additional critical success factors from implementing e-procurement in the 3.0 and 4.0 eras. Identifying the key challenges will be the basis for the roadmap plan suitable for maximizing the achievement of new public management in Industry 4.0.

1. Introduction

Public procurement is one of the four key economic activities governments participate in [1,2]. In OECD member countries, public procurement accounts for one-third of all government spending and 13% of GDP [1,3]. One of the government functions most susceptible to corruption is this one. Even in EU nations with relatively strong procurement system integrity, estimates of losses from acquired spending range from 10 to 20 percent [4]. Due to poor infrastructure quality or degree, corruption can result in greater deficits and slower growth [5]. Some concepts and procedures used to prevent corruption and protect the public interest in the procurement process include integrity standards, openness, stakeholder involvement, and e-procurement [1,6,7,8]. The government may obtain the highest quality and price ratio in public procurement by boosting competition among bidders. Beyond the problem of the draining impact of corruption, there are also benefits to using e-procurement systems, including the ability to manage public resources efficiently [9]. Quality management as a public administration reform approach sought to develop strategies to ensure the modernization of public administration to maximize decision-making and the quality of public services [10]. A tool for achieving quality in public management objectives is e-procurement [11]. Numerous experts agree that enhancing quality necessitates a process-based perspective. E-procurement is essential for implementing a QMS because it supports the sharing of quality information and makes it possible for suppliers and purchasers to engage in quality procedures [11]. According to studies, e-procurement promoted the admission of better standard contractors and improved quality in India and Indonesia [12]. Implementing electronic (e-) procurement as a critical reform contributed to solutions through its application in the private and public sectors. In the public sector, in particular, the influence of e-procurement is a decisive factor in achieving efficiency and effectiveness in three fields [13,14,15,16]: (1) transparency and accountability in governance, (2) efficient use of public resources, and (3) conditions of balanced development of all regions and fair competition between companies. Reforming public procurement through electronic transition strengthens the citizens’ faith in public administration and democracy as it achieves its political goals rationally, effectively, and transparently [16,17,18]. Democratic accountability is the main component of public e-procurement systems that are constrained by laws, rules, regulations, and numerous institutional oversight mechanisms. Increasing competition between public vendors may greatly simplify procurement, decrease costs, and produce better results. With the advent of electronic government platforms, electronic public procurement (e-PP) began its journey toward digital transformation [16]. Procurement 2.0 (e-PP 2.0) transitioned from traditional price-based and siloed procurement to capturing data and transactions utilizing e-PP and digital process management in the early phases of digitalization [19]. Through procurement 3.0, e-PP 3.0 systems began working and interacting with information outside their data environment. Business choices were directed by cognitive capabilities and content management rather than transactions alone [15,16,20]. Industry 4.0 features machine-to-machine communication and integration of the physical, digital, and biological worlds using 4.0 technologies [21]. For instance, in Purchasing 3.0, an electronic catalogue, a digital tool that requires a human customer to register his desire is an example of buying. In Industry 4.0 (ID 4.0), sensors in a cyber-physical system detect demand by seeing that material is running low. Autonomy is the second of the constituting elements of I.D. 4.0 [22]. The decisions use pre-programmed algorithms (e.g., AI & ML). Finally, machine-to-machine communication is a legitimate need but necessary since it requires a secure connection to work. Instead of focusing on the human-machine interface as in I.D. 3.0, the innovation now is that networked robots may interact without human intervention [23].

This study attempts to lead by examining common and distinct challenges and problems in the transition from era 3.0 to era 4.0 of e-PP. To achieve this, it collects and categorizes problems and challenges of the 3.0 era, groups them with the method T.O.E., and attempts to classify them with the MIT90s model to have an understandable cognitive structure for those who make decisions. Previous studies underline that a digital transition plan from one era to another requires a deep understanding and accurate T.O.E. classification [24] of the challenges and barriers that interpret adoption readiness and effectiveness [25]. The next step is transforming these challenges and barriers to critical success factors (CSFs) for practical managerial implications. Numerous significant studies and reports have been conducted on the critical success factors for implementing e-procurement [2,13,15,16,20,25]. Likewise, there are studies on the potential of the fourth industrial revolution technologies in procurement in the private and public sectors [21,26,27,28,29,30,31,32].

The present electronic public procurement (e-P.P. 3.0) literature offers limited assistance for a compact transition strategy for companies and public organizations to electronic public procurement in ID 4.0. e-PP 4.0 studies have mostly focused on how emerging technologies may provide value to them [33,34,35]. To address this gap in the literature, our study gathered the new challenges and obstacles that public bodies and relevant shareholders face in applying emerging technologies in public procurement, and explored how these challenges and barriers could be classified to transform them into useful administrative actions, understandable to digital transition public or private decision-makers.

In Section 3.1, the study presents the definition and classification of the challenges and barriers of e-PP 3.0. The definition and classification of challenges and barriers of e-PP 4.0 were collected in current case studies of the 4.0 era to Section 3.2 and Appendix A. Section 4.1 presents the comparative analysis of e-PP. 3.0 and e-P.P. 4.0 challenges and barriers, resulting in two concluding tables analyzing the common problems and challenges of implementing e-procurement in the public sector and additional challenges and problems of the fourth industrial revolution. In Section 4.2, we classified the additional challenges and problems into understandable critical success factors based on the MIT90s framework of Scott Morton (1990) [36]. This could be the base of a future study to facilitate their conversion into management actions, maximizing the effectiveness of an e-public digital transition through a commonly accepted roadmap [37]. In the discussion, we proceed to theoretical implications to Section 5.1 by synthesizing managerial and research analysis tools to create a valuable input to a technology transition roadmap. Managerial implications in Section 5.2 included from the comparative evaluation reveal common elements and additional ones that concern technologies of the fourth industrial revolution exclusively, due to their special nature. In Section 5.3 we mention the main limitations of the study as they derive from the collection of the data, the ontological limits of the research tools, and the dynamics of the subject of the fourth industrial revolution, as it is ongoing. An overall digital transition strategy for public procurement in ID 4.0 is the point of a potential future study in Section 5.4. Also, the additional specific problems for public procurement in ID 4.0 could lead to new research questions and suggestions for the most efficient transition of public and private organizations. In the conclusion, the contribution of this study is highlighted as it will utilize our previous knowledge from the application of e-PP 3.0 during the digital transition and focuses the effort to identify, understand, and reveal the specific problems created by the technologies of the fourth industrial revolution in their future implementation in public procurement by implying a former framework (MIT90s) as a process of preparing a modern technology transition roadmap.

2. Research Methodology

We followed Stuart et al.’s research process model: first, we defined our research questions. The research design was therefore created to compare the theoretical view about e-P.P. 3.0 challenges and relevant cases in e-PP 4.0. Following that, we acquired, compared, and discussed the common and new challenges [38].

2.1. The Research Questions

The research questions were defined at the initial step of the research process.

The main research questions of this study are:

RQ1. Which are the common and new challenges and barriers between e-PP 3.0 and e-PP 4.0?

RQ2. How can we define and classify these new challenges and barriers to critical success factors to connect them to specific actions in a new digital transition roadmap?

2.2. Research Path

The databases chosen as the first step in the process for this literature review were Science Direct, IEEE Explore, Scopus, Google Scholar, and Web of Science.

The method of data collecting is shown in

Table 1. Data collection method.

Databases	Science Direct, IEEE Explore, Scopus, Google Scholar, and Web of Science
Keywords	Industry 4.0, e-public procurement, Public Procurement, blockchain, of Business Intelligence, Machine Learning, and Artificial Intelligence, 3D Printing, Robots, Challenges, Barriers, TOE, Critical Success Factors
Document type	Articles, Books, Cases
Date	2002>
Language	English
Initial search	352
Provisional collection	180 -abstract briefly reviewed
Selection criteria	Novelty, times cited, publication date, journal impact, relevance, pilot or full use
Selected articles	122

.

After gathering the appropriate theoretical data about e-PP 3.0 and case studies on e-PP 4.0, the study categorized barriers and challenges according to the technology evaluation methodology described as the TOE framework [13]. The TOE framework is a framework that proposes a basic set of factors that explain and predict the probability of innovation/technology adoption [13].

We used a qualitative method and performed a descriptive multiple case study about emerging technologies in e-PP 4.0 to give first insights and help create well-grounded, generalizable challenges with TOE analysis due to a lack of relevant literature [39]. According to Yin, case studies are most effective when the study topic is “why” observable phenomena occur and when the focus is on recent events [40]. We chose to identify and describe critical variables such as challenges and barriers in e-PP 4.0 where either a theory does not yet exist or cause and effect are in doubt in the case research methodology because it is both appropriate and essential. According to Handfield and Melynk, the proper research structure for finding key variables are focused, in-depth, multi-site, best-in-class case studies [41].

The primary sources of evidence in focused case studies are participant interviews, project reports, end user feedback documents, and meeting notes. Secondary sources are studies of international organizations concerning technology applications of the e-PP 4.0. The authors used a four-step approach: preparation, exploration, specification, and integration (PESI) for empirical material understanding [40]. Reliability and generalizability are the two main criteria for assessing the previous sourcing material but are risky in qualitative methods as described in the research limitation [42]. The research logic consideration is abduction, while it is sufficiently adaptable to allow for a less theory-driven research method than deduction. Abduction through case study analysis asserts that theoretical frameworks emerge with empirical observation. The researcher evaluates the empirical data and gives detailed descriptions based on the participants’ perspectives from the abovementioned sources [43]. The case study selection and the interpretation of challenges and barriers in e-PP 4.0 were focused on flexibility. Data-gathering methods were focused on public procurement cases, but different sources of case interpretation were gathered to make the data more flexible. To establish validity, we explored several sources of evidence for each essential aspect of challenges and barriers in e-PP.4.0, employing the crucial triangulation approach [44,45].

The authors have identified three distinct shortcomings in the data analysis section of case research paper submissions as reviewers for a range of journals: the failure to identify important patterns, the difficulty in simplifying from descriptive material, and the inability to think laterally. A good strategy for separating order from chaos is to structure the data in several patterns and present them in tables. Much of the data from case-based research is qualitative, making it challenging to present in a format suitable for a journal article. Yin describes writing a case study to avoid extensive tales. As a result, they decided to apply tables that categorize data by technology type and classify them in a TOE structure comparable to e-PP 3.0 data collection [40]. The Eisenhardt framework will be used to set the organize and control rules of the research, known as a case study protocol (CSP) [46], as it is adapted in IS research [46]. The Eisenhardt framework may be divided into three phases: model construction (Phase 1) by selecting the proper to research questions cases, model testing (Phase 2) by analyzing the data within the cases or cross-case, and model refining (Phase 3) by comparison with similar or conflicting literature. The data collection of the cases is presented in Appendix A.

There are also other commonly used theoretical models in I.S. adoption technology research, including the classical Technology Acceptance Model (TAM) [47], TAM II [48], the Theory of Planned Behavior (TPB) [49], and the Diffusion of Innovations (DOI) [50]. Nevertheless, the TOE framework appears relevant because it is a commonly used theoretical technique for examining contextual elements that impact IT innovation adoption with an empirical base [51]. The technological dimension of the TOE framework examines challenges, problems, and the degree of readiness about the necessary technology infrastructure in hardware and software and IT human resources capabilities [24]. The organizational dimension describes the administrative and contextual requirements for implementing new digital technology. Top management support, financial resources, and organizational structure were all identified by Kosmol et al. [25]. Change management and communicating the organization’s transitional goals and arrangements to address any resistance to change on the part of the staff, users, or other stakeholders is another element of the organizational challenges we had to address [52]. The environmental dimension refers to government regulation that public authorities and businesses should follow to preserve privacy, transparency, and accountability [36]. The sector’s structure, the national presence or absence of technical service providers, national policies, and public financing are all factors to consider also [53].

To classify the challenges and barriers to critical success factors (CSFs) [36] and then to an actionable technology roadmap (T.R.M) [37,54], we used the socio-technical MIT90s framework of Scott Morton [30]. The CSFs will be the subject of further future Delphi method analysis to determine actions and a strategic plan for adopting a digital transformation (TRM) by the shareholders of public procurement in the ID 4.0 era. A technological roadmap will assist policymakers in determining which technologies to advance and how to implement them. Figure 1 below summarizes the research path and the intended results of each step.

3. Comparing Public e-Procurement 3.0 and e-Procurement 4.0

3.1. The e-Public Procurement 3.0 Challenges by the TOE Framework Based on Literature Review

E-public procurement impact reduces administrative costs and reorganizes procurement procedures faster and more transparently by providing information about individual tender opportunities [55]. E-procurement improved monitoring because there is a digital recorded process under audit standards [56]. Similarly, the cross-border competition was expanded by removing barriers presented by paper-based procurement processes. The procurement administration is centralized, so applying economies of scale and reducing back-office costs is feasible [57].

Critical success factors, obstacles, and challenges arising from the implementation of e-procurement 3.0 contribute to their reliable benchmarking with those resulting from the implementation of e-procurement 4.0 cases. The idea is based on benchmarking between two products with common but different characteristics [58]. The TOE classification makes it easy for us to have a common benchmarking framework, understandable and easily translatable to management decisions [53].

Table 2. TOE framework e-public procurement 3.0 challenges/barriers.

Technological	Organizational	Environmental
The system does not meet public authorities needs [58,59]	Changing management issues such as organizational culture, a collaboration of different departments from the front office to the back office, and public institutions are inherently less flexible [33,60]	A competitive environment induces mimicking behavior between the public authorities and businesses [14]
The system is complicated and unable to attract suppliers [58,59]	Leadership and stakeholders lack engagement [20] due to sufficient funding and resources and the need for strong and committed leadership at the political level [33,61]	Economic health vs. budget constraints [14]
Disruptive innovation-a new set of resources, processes, and values [62]	Lack of project great pilots as an excellent paradigm [15]	Strong corruption [63]
Electronic signatures: costly, complex, lack of interoperability, legal barriers, and limited support from service providers [64]	Lack of well-trained and skilled personnel [15]	The compulsory and political character of the regulatory framework [18]
IT security and authentication (confidentiality, integrity, and availability) [59,65]	Users’ resistance to change because e-procurement will make their work more difficult and monotonous, and current roles will change [35,56,58]	Political engagement to guarantee enough funding [18]
Cyber security [20,66]	Various stakeholders’ different needs, goals, and interests, as well as the realization of benefits of information systems in public procurement stakeholders [56,58,59]	Social norms about innovations [56]
Interoperability [18,33]	Business process reengineering The inefficient and non-value-adding processes need to be cleaned up, while the mandatory procedures need to be optimized for e-use [35,58,59]	The size of the population and the high level of education [67]
Existing knowledge in IT [14]	Organizational adjustments to secure critical private and confidential information [59]	The involvement of small and medium enterprises in public procurement [68]
	Business model (in-house or third party) and lack of coordination and standardization of processes [35]	SMEs are responsible for innovation and flexible services [68]
	Bureaucratic culture and practices [26]	Availability of information to SME.s [69,70]
	Lack of skills and capabilities by the stakeholders and especially the employees [71]	The reduction of administrative burden and the increase of digitization [70]

below presents the TOE categorized barriers and challenges presented during the implementation of 3.0 technologies in electronic public procurement through the literature review.

3.2. The e-Public Procurement 4.0 Challenges by TOE Framework Based on Case Studies

The fourth industrial revolution is a wave that brings changes in the technology sector and all business structures and interfaces between the State, business, and citizens/customers. Industry 4.0 is the critical framework of the investigation, which offers solutions and creates research requirements in breadth and depth of all parameters that support or hinder the full realization of the benefits for business and civil society [72]. The 2019 Accenture report analyzes the DNA of the technological changes that are imminent in the context of the fourth industrial revolution through the acronym DARQ. It briefly refers to DTL technology [73], artificial intelligence (AI), augmented reality (AR), and quantum computing [74].

Public procurement 4.0 adopts technological tools which can offer several benefits, such as faster and more effective access to more cross-border opportunities, especially for the SMEs [70]. Persons, platforms, processes, and partnerships are the main categories of challenge transformation for e-procurement in ID 4.0 [75].

The study of the directorate-general for internal market, industry, entrepreneurship, and SMEs about emerging technologies in public procurement around the world is an important base for our research [28]. The following table summarizes the results of the impact of each technology per case study and the challenges, barriers, and critical issues connected to each case and generally in literature. Cloud computing and “XaaS” are included in SMAC technologies additional to social, mobile, and analytics technologies [74]. Big data analytics is an emerging disruptive technology as it is a bridge between 3.0 and 4.0 generations and combines the operations with the main disruptive technologies of business intelligence, machine learning, and artificial intelligence [74,75,76,77]. A number of the practical use-cases and technologies are mutually beneficial and operate in addition [74,75,78]. “AI Watch” is another research source about the european landscape on the use of artificial intelligence by the public sector and analyze national strategies about reforming public procurement to obtain AI for public use.

A crucial component of this digital transition in the ID 4.0 equation connects the digital, physical, and human spheres through networks, processes, and data that are then transformed into knowledge and action. Emerging technologies have recently switched their emphasis from the purely technical component of connecting devices and obtaining data to the interconnectedness of devices, data, business objectives, people, and processes [30,31,72].

Table 3. TOE framework e-public procurement 4.0 challenges/problems.

Technological	Organizational	Environmental
1. Simple technical implementation [28,79] 2. Integration with other digital technologies [80,81,82,83] 3. Cooperation with legacy systems [84,85,86,87] 4. Danger of lock-in [88,89] 5. Require existing digital procurement platforms to extract and transform data [90,91] ease of use, visual appeal analytic dashboards, workflow integration 6. Good data. Quality, accuracy, and trustworthiness [92] 7. Process challenges about data capturing, transformation and sharing integration [93] 8. Measuring corruption requires a proxy— a ‘corruption risk index’ (CRI) and proper patterns [7] 9. Quantum computing increases processing capacity [94] 10. Proper infrastructure: hardware, software, and license types must be adequately defined [95]. In-house production and support of AI systems [96] 11. Large, open, anonymized and standardized datasets are needed across the government [28,97,98]. 12. Advanced algorithm construction for better results [99] 13. Ability to use real-time structure and unstructured training data [76,100] 14. Add new A&Qs to the Chabot regularly [28] 15. Analyze the capabilities of data infrastructure Cyber security [101] Define differences between software-defined infrastructure (SDI) vs. AI-defined infrastructure [102] 16. Integrated data preprocessing [103] 17. Well-trained data scientists 18. Enriching the training data from the vendor market [104] 19.Technical transparency [105,106,107,108] 23. IT infrastructure must address security issues [109,110] 24.Managing heterogeneity: scalability, modularity, extensibility, and interoperability among heterogeneous things and their environments [109,111] 25.Endpoints/connected objects component (sensors–interfaces) [109] 26. Issues with the authenticity of the underlying data [100] 27. Robots need more clear information than humans [112] 28. Create algorithms that are adaptable enough to generate a simple and accurate 3D model for every type of building or construction [80] 29. Management of the continuous data volume increase [113,114,115] 30. Detailed technical information on components to be printed [116] 31. Delaminate parts-fused deposition modeling (FDM) [81,82,83,117] 32. Beware of inaccuracies in the product design process 33. Ensure post-processing requirements [118,119,120] 34.The raw material selection is not extensive [113,115,118] 35. Limit the size of the pieces that can be printed [28,83,121] 36.Deep analysis of the target process. Correctly identified use-cases for automation [122,123,124] 37. Give access to fresh data that can be studied and compared to current data to produce unique knowledge [125,126]	38. Need to build support for the project with internal stakeholders and users [84,127,128,129] 39. Overcome resistance to the use of emerging technologies [28,130] 40. Authorize access to data, and role distribution [131] 41. Organizations to own their data [90] 42. New roles need new training forms [94,96,132,133,134] 43. IT and non-IT users work properly together [135,136]. Non-technical public workers lack appropriate data and AI understanding [96] 44. Confirm the organizational structure’s quality, talents, experiences, programs, processes [137,138] 45. Project management focuses on data, not coding [94,138,139,140] 46. Lack of organization-wide adoption [138]. Creating and promoting a pro-innovative corporate culture among business/PA employees [141] 47. Compatible procurement policies and data expertise inside the public authority [133] 48. Check where the AI is the better solution [97] 49. Self-selection bias in data entry from the organizations [142] 50. Administrative processes building, contestability, human engagement, integration of values [143] 51. Update models regularly to reflect changing situations and behaviors of contractors and civil servants [100,144] 52. Procedural transparency provides information about the purpose of the algorithmic system [106,143,145] 53. Adapt public procurement to IoT model [109] 54. Networking vendors. Interoperability and compatibility between them [146,147,148] 55. New abilities required and new organizational forms and procedures [149] 56. Check suitability: high-volume processes, high standardization, rule-based tasks [150] 57. Manage physical interference and contact (PIFACT), humans with robots [151] 58. Create new positions and revise job descriptions [57] 59. Define and certify processes: protocols, standards, and procedures [83] 60. Define staff qualification and certification [118]	61. Overcoming legal regulations regarding barriers and conflicts [84,152,153,154,155] 62. Privacy issues about data stored [134,156,157,158] 63. Lack of support from the government [134,159] 64. Legal ethics issues about personal data [160] use, sovereignty safety, equity, accountability, and lack of fairness [97,161,162,163,164] 65. Explainability: clear documentation about the working algorithm according to bias or ethical issues [94,143,165] 66. Combine human intelligence with the best of machine intelligence (i.e., human in/on the loop, human in command) [166] 67. Integrating machine learning system design with administrative law [143,167]
		68. Lack of standards concerning, e.g., IoT technologies [110,168] 69. Expand the role of the public sector in governing data. Accessibility-flexibility accountability [134,169,170] 70. Manage thoughts and sentiments in opposition to automation, e.g., human workforce [123] 71. Liability for design errors, safety problems [112,141,171,172] 72. Intellectual property issues, copyright protection, patent protection, design rights [173]

presents the TOE framework e-public procurement 4.0 challenges/problems sourced from case studies (Appendix A).

Figure 2 below gathers, groups, and presents, more simply and understandably, in summary, the results of Table 3.

4. Comparative Analysis of Findings

4.1. Common and Additional Problems/Challenges e-Public Procurement 3.0–4.0

Regarding our first research question, the common problems and challenges between e-public procurement 3.0 and 4.0 are presented in

Table 4. Common problems/challenges in e-public procurement 3.0–4.0 (source: Table 2 and Table 3).

Common Problems/Challenges e-Public Procurement 3.0–4.0
Technological	Organizational	Environmental
Complicated platforms-tools. Usability and accessibility.	Change management strategy: overcome resistance overcome bureaucratic culture Involvement, engagement, motivation for stakeholders, and the realization of benefits	Expand the public sector’s data governing in data security, personal data protection intellectual property issues
Inaccurate IT design on users’ needs	Top management support Vision Resources Training Project management	Political decisions about providing Resources Infrastructures Administrative law adaption
Interoperability-cooperation with legacy and 3.0 systems/platforms/tools	Collaboration between departments. IT and non-IT staff
Security issues IT (i.e., authentication) Cyber Anonymize personal data	Adapting to new roles and job descriptions
Requirement of new technical skills
The quality of structured data requires Existing database Compatible formats
Disruptive innovation characteristics

.

The additional problems and challenges between e-public procurement 3.0 and 4.0 are presented in

Table 5. Additional problems/challenges in e-public procurement 3.0–4.0 (source: Table 2 and Table 3).

Additional Problems/Challenges in e Public Procurement 3.0–4.0
Technological	Organizational	Environmental
Avoid “lock-in danger” from 4.0 proprietary technologies	Need for a protocol to examine which technology is appropriate for each organization Procurement phase Type of procurement	The legal framework in ethical issues: Clear documentation about the working algorithm Combine human intelligence with the best of machine intelligence (i.e., human in/on the loop, human in command)
Manage unexpected-unwanted reactions (e.g., by the bots) Robots need more clear information than humans.	Business models are updated regularly to reflect the new learning inputs from the process (e.g., Machine Learning about fraud detection)	Introduction of standardization in devices (e.g., IoT) related to technologies 4.0
Find/create proper algorithms to discover patterns (e.g., fraud cases) (CRI).	Higher level of automation workflows becoming more transparent, decentralized, and less hierarchical	Data government policy issues
Limited access to critical databases (tax, insurance, business registries).	Reallocation of the workforce: Machine-to-machine communication (e.g., IoT, Bots). Work without human intervention. Self-monitoring systems
Interoperability, integration- between 4.0 technologies.	Business process-reengineering Discard unnecessary process Optimize Automate
Utilization of existing (i.e., EU’s) 3.0 tools as data sources	Compulsory to digitize the process from end-to-end Intra-organizational integration Inter-organizational integration
Managing and analyzing unstructured data requires skills (e.g., data science) computing infrastructure and high- processing capacity, data storage capacity
Availability of training data, e.g., vendor market, technical information and raw materials (e.g., 3D printing)
Manage difference between Software-defined infrastructure, SDI based on developer-defined rules and AI-defined infrastructure (AIDI) learning of its environment
Big datasets that cannot be gathered, stored, handled, or analyzed using standard software methods

.

4.2. Classify Challenges and Barriers to MIT90s Critical Success Factors

Regarding our second research question, the study’s results reveal that technologies such as artificial intelligence, big data, and the Internet of Things allow companies and public organizations to automate and optimize their procurement process only if they identify and solve critical challenges and problems.

The MIT90s framework is intended as an application tool for a particular organization or a business performance model that examines the environmental factors from the perspective of five (5) components, which are crucial organizational characteristics that emerge as highly valuable information and need to be aligned and formed together to achieve greater effectiveness [36]. The MIT90s model explains the coordination of several essential factors in aligning strategy and Information and Communication Technology (ICT) [174]. The MIT90s model has been examined as a fundamental framework that stimulates and enables companies to understand the dynamics of all transformations that are necessary for technology to ensure the success of business performance [175].

By developing a conceptual framework using the MIT90s framework, this study seeks to find the new challenges and barriers from the emerging technologies implementation in e-PP and classify them with a long-term purpose to a road mapping technology procedure. In order to better understand how CSFs interact with to implement emerging technologies in public procurement, the CSF concept and the MIT90s framework have been combined [176,177].

Table 6. Classify problems and challenges in C.S.F by MIT90s framework.

Scott Morton’s Element	CSF	Challenge Number Table 3
Project strategy	1.Performance legitimacy [178] Define clearly national transformation goals 2. Allocate sufficient funds to implement the transformation plan for infrastructures training and supportive resources	4,6,11,27,34,35,36,46,48,56,57,63,67,68,69,70,72
Structure	1. Task legitimacy [178] 2. Reengineer processes 3. Reform organizational structure	38,40,43,44,51,52,55,58,59,66
Information systems	1. Evaluate the interoperability, integration, security, data quality, and efficiency of legacy IT Systems (e.g., PP platforms) with 4.0 technologies 2. Data Governing	1,2,3,4,5,6,9,10,12,13,14,19,23,25,26,28,30,37,41,45,47,49,62,64,65,66,69,72
People	Value Legitimacy [178] Manage change resistance, relations, and deliver training Protect fundamental democratic rights and ethical values	17,38,39,42,55,57,60,64,65,66,70,71,72
Management processes	1. Manage legal reforms 2. Manage digital transformation tactics 3. Project management resources-requirement planning	6,7,8,13,15,16,17,23,24,29,31,32,33,37,43,46,47,50,51,52,53,54,61,64,65,66,67,70

presents the classification of problems and challenges in CSF by MIT90s framework.

5. Discussion

5.1. Theoretical Implications

The theoretical contribution of the work is to synthesize a research path with the harmonization of certified research tools and methods, which results in a common and additional challenges-and-problems summary of two different techno-economic paradigms. Comparative analysis with the collection of secondary data [179] from the literature in era 3.0 and the collection of qualitative data from case study analysis in era 4.0, is a challenge. A challenge affected two parameters: the difference in the maturity of the technologies of the two periods and the different nature of the collected data, as in the case of the secondary literature review data we have in the majority quantitative data while in the case of case study analysis, qualitative data. Nevertheless, the categorization of the challenges and problems with the TOE [180] and MIT90s framework could contribute to reliable benchmarking [181]. Thus, we can compare “apples to apples” and come to conclusions that will help future researchers or managers. In the case of common challenges, managers can adopt good past practices, and in the case of new challenges, the shareholders should compose new practices. Additionally, the study’s contribution to the theoretical part of the new challenges management, MIT90s method, could be utilized as a base for reform. The simplicity and fundamental nature of the MIT90s [53] framework can transform the heterogeneous information of challenges and problems into something more practical for managers. The familiarization of managers with the MIT90s method in IT and digital transition projects, and creating a series of proposed critical success factors by matching the challenges, creates the theoretical background for additional research that will be validated through a technology assessment. In addition, categorizing challenges and problems into groups helps identify the critical system requirements and their targets. In the case of the fourth industrial revolution, we have to assess whether the management processes or public procurement strategy will be at the center of the MIT90s framework [182] combined with the potential and weaknesses of emerging technologies. For this reason, in a future study, we have to research the interaction of the grouped critical success factors and focus on transforming public procurement strategy into technology-oriented drivers. In conclusion, serving the need to create a technology transition roadmap [37,54,183,184], we can rearrange the relationships of the elements of the MIT90s framework by setting in the center of the framework the public procurement strategic priorities and emerging technologies capabilities. In conclusion, it was studied and theoretically presented how management and data analysis tools can be combined to feed as an input a technology transition roadmap of electronic public procurement in the ID 4.0.

5.2. Managerial Implications

As a result, our study about emerging technologies in public procurement offers a viable answer to assist practitioners in implementing such technologies effectively and generating momentum by upscaling skills and applying methods accurately.

They are achieving the benefits of adopting ID 4.0 technologies require new skills, roles, and processes. A plan to increase the number of civil servants with emerging technologies 4.0-related skills is needed. Public managers should be informed of emerging technologies and what to expect from them, and their training should be adapted to these capabilities. Professionals should adopt the perspective that under certain circumstances, a public procurement system 4.0 can fail, be prejudiced, make incorrect decisions, or propose incorrect solutions.

Process automation frequently necessitates changes in organizational structures and cultures as tasks formerly conducted by people become automated, while previously unknown jobs and responsibilities become noticeable. There were always ethical issues to address through automation, and protective measures should be used to avoid the loss of control by humans. Regarding a reliable and human-centered use of technology, public administration is anticipated to be at the forefront.

The development and implementation of initiatives aimed at enhancing the public sector’s data quality, accessibility, and availability could be improved by access to data from the private sector through a secured method. The system, as well as the quality of the data, must be methodical and incorporated into legal frameworks. Regulatory sandboxes and pilot projects could allow the testing of innovative technologies in a real-life environment. The regulatory sandbox [185] aims to learn more about specific innovations’ opportunities and threats and develop an appropriate regulatory environment to respond to them. For instance, regulating the relationship between the public and private sectors is important to ensure successful collaboration and prevent unfavorable outcomes like vendor lock-in. The flagship pilots are useful for overcoming digital transformation challenges, but it is important to avoid pilot purgatory. Clear goals and integrated decisions by the shareholders are important to prove the value of the implemented technology in the long term. Using common metrics should review each emerging technology’s efficiency and document the key steps and success factors in developing a scaling roadmap checklist.

Public procurement departments, in particular, are being compelled to address governmental laws and connect their ID 4.0 programs with procurement legislation. As a result, we recommend starting with previous gain knowledge by e-PP3 to acquire firsthand experience and then evolve our transition to the specific elements that will meet the challenges of the technologies of ID 4.0.

Few infrastructure improvements will be made to encourage the adoption of emerging technologies in PP to improve the efficiency with which large amounts of data may be processed through the current data exchange portals. Furthermore, governments could encourage using PPP agreement. Supercomputer facilities could create novel emerging technology solutions for their public and private sectors. Also, reforming the procurement process to more agile methodologies could make adoption in technological and organizational aspects more likely to succeed. Technological interoperability is essential, particularly once the project has moved past the pilot stage.

5.3. Limitations

By gathering and interpreting qualitative data from focused case studies in e-PP 4.0 there is a reliability and generalizability risk. Qualitative case study research aims to analyze occurrences rather than generalize the findings. Credibility, reliability, transferability, and flexibility can all help to enhance qualitative empirical material interpretations [42]. The case study technique is frequently used to identify a relation or effect rather than to describe an average effect; hereafter, cases are frequently exemplary rather than typical [38]. Case-based research is sometimes misunderstood as a collection of “tales and war stories.” This critique can only be avoided by carefully designing and carrying out the research project using protocols. The cases are frequently described in a shot taken at a certain time. The case might be in pilot status or incomplete at this certain time. In addition, the data acquired from public sources was limited, so some obstacles or challenges might not be researched or covered [43]. The accessibility and interpretability of the information have a significant impact on the collection of use cases. Online, the information on particular use cases was frequently poor and, in some cases, unclear. Additionally, not all the information was readily available in English, leading to translation problems.

5.4. Implications for Future Studies

The classified critical success factors provide a method to establish guidelines for monitoring and controlling an organizational [186] and focus management attention on the critical area of business [187]. According to Leidecker and Bruno [188], using experts’ opinions is a technique used to identify CSFs. Delphi’s method could organize the expert opinion analysis more effectively because answers are tested repeatedly until a consensus is formed. The precise information produced is valuable for a well-focused decision-making policy [189]. TRM is an actionable time-specified strategic plan for realization among the various stakeholders/experts, developing a shared vision, defining rules for emerging technologies implementation, setting goals, evaluating the identified challenges and obstacles, and building strategy priorities. The proper tool for analysis and fulfilling of TRM in future research could be the Delphi method [37]. As we can record from previous studies, TRM [190] using as a designing basis, multi-criteria decision-making methodologies, the Delphi method, and HDM (hierarchical decision modeling) [54]. Consequently, future research based on classified challenges and barriers to CSF could suggest a TRM for a digital transition from e-public procurement 3.0 to 4.0 by building a consensus between experts about why, how, and when emerging technologies could impact and reform effectively the public procurement process.

6. Conclusions

By observing ongoing case studies, emerging technologies (Table 3) (Appendix A) provide a chance to handle the digital transition challenges for public procurement in ID 4.0. In the present study, by comparatively evaluating the challenges and problems between technologies 3.0 and 4.0 in the common context of a TOE. analysis, we found common and additional elements that can be our cognitive starting point during the digital transition from one technology to another.

Therefore, public and private sector managers could take advantage of the common and additional problems and challenges of the previous period and apply the following during the transition to the fourth industrial revolution.

In the technological field:

To ensure the resources for the necessary technological infrastructures.

Carefully plan the interface of legacy applications with emerging technologies by combining input sources and information outputs.

Design applications in a user-friendly manner that will ensure a user’s knowledge of how algorithms work when making decisions.

In the organizational field:

Public managers inspire with their vision and motivate staff to implement the transition by documenting the use of their choices and upgrading their skills.

Public authorities reorganize the framework of the management structure and update the job descriptions without barriers between departments and without exclusions for departments of employees or stakeholders.

PA and suppliers create an open model for innovation adoption by refining a culture of cooperation and knowledge exchange in the overall ecosystem.

In the environmental field:

Propose the institutional adaptation of administrative law to the modern technological challenges that require protecting personal data, cyber security, and preventing the risk of corruption and discrimination.

Ensure through certification protocols that the final responsibility for the control and operation of the electronic procurement tools will be with the end users and the citizens.

Calls for transparency as a path to a critical evaluation, democratic process, and accountability are inspired by images of the black-boxed algorithm or procurement without auditing.

Public administration in modern democracies serves the principles of democratic governance through the principles of accountability, transparency, political participation and equal opportunities.

The challenges found in the environment of emerging technologies define that the knowledge about the content of the algorithms and how they work must be clearly presented to citizens [191]. Although AI procurement systems can make decisions (for example, awarding procurements), they cannot be recipients of political responsibility towards the citizens. The citizens will request the responsibilities of the administrative staff of the public sector who choose the algorithms or train the robotic systems. Consequently, the public administration should, for reasons of strengthening the democratic feeling of the citizens, leave a window of participation and explanations for them. They should reveal the decision-making patterns at all stages of the procurement cycle by humans and machines and allow them to suggest changes.

The limitations of the present study are mainly found in the data collected from the case studies of emerging technologies in the field of electronic public procurement, as several of them are not mature or are in a trial stage or have not been sufficiently evaluated by the users.

Some future studies could be generated from the present one. A future study could concern the creation of a digital transition map through the consensus of experts (Delphi method). Another series of studies could isolate the emerging technology phase of the supply cycle or aim to investigate the challenges and problems concerning public procurement and propose solutions on a case-by-case basis.

It is important to speak generally of adoption rather than the implementation of emerging technologies in public procurement as the entire shareholder’s ecosystem should embrace the new solution, incorporate it into their processes, and as a result, become more fruitful. Generally, and especially for public processes like public procurement, it has become vital to create supervision and enforcement mechanisms that have a public-facing component, can demonstrate democratic responsibility, and are therefore also more representative of society as powerful technologies are deployed to the public in ways that are transparent to individuals. This legitimacy certification will empower the adoption culture through awareness training and continuously rationally reform the relationship between technologies, organizational structures, and people.