Validation and Refinement of an Experience-Based Onboarding Model for the IT Industry Through Multivocal Literature Review ^†

Abstract

Aim: This review aims to validate the Experience-Based Onboarding Model (EBOM) and refine it into an improved adaptive onboarding model, OnMod. Methods: In this review, autoethnography is combined with a Multivocal Literature Review (MLR) that combines white and gray literature sources. Evidence is mapped to entities and semantic relations and assessed using predefined decision rules. Main findings: The validation of the model confirms the core EBOM entities and semantic relations. It also introduces several new or renamed entities or semantic relations that close the feedback loop and yield the refined OnMod model. Implications: The theoretical contribution is reflected in the application of autoethnography in combination with the MLR, where it represents a good basis for the development of an onboarding model. In industrial practice, the presented OnMod model can be used by mentors and managers as a guide for improving operational and daily activities, as well as for the development of onboarding strategies in IT and software companies.

1. Introduction

1.1. Research Background

Onboarding in the Information Technology (IT) sector is of particular importance due to the high intensity of knowledge, rapid staff turnover, team interactions that require a certain period of time to master, and hybrid/remote work [1,2,3]. Meta-analyses show that the increase in labor mobility accelerates the socialization of new employees, where the clarity and purpose of the role, efficiency in work, and acceptance by other employees are the key factors in mediating between adaptation at the beginning of the onboarding process and outcomes that include job satisfaction, commitment, and performance, as well as retention of the employee in the company [2,3,4]. Onboarding, which is presented as part of the organizational socialization process, has a long research tradition. Papers emphasizing the content and consequences of socialization [5] and Wanous and Reichers [6] highlight the importance of structured orientation programs [2]. In agile teams, studies describe a specific form of difficulty for a new employee to enter an already-formed onboarding and practice process. For this reason, it is very important to have a structured onboarding process in order to increase the probability of a successful integration [7,8,9]. Research in software engineering (SE) has shown that onboarding is an expensive and error-prone process and that many programming employees need up to three years to become fully productive in projects. Considering that the rotation in companies is less than two years, some employees never reach full productivity within a team [10].

The transition to remote work further emphasizes vulnerable places. The biggest challenge becomes how to build a strong social connection with the team, reliable and effective communication, with limited opportunities for direct contact. A study of 267 new hires at Microsoft documents a deficit in socialization and a need for practices that include one-on-one meetings, turning cameras on during meetings, and mentors to mitigate the effects of remote environments [10]. With that, the findings from the paper are that onboarding in a remote environment is qualitatively different from classic office work, where the context is emphasized that the remote/hybrid work environment changes the nature of work [1,10,11,12].

Specific onboarding practices (early defined resources to be used: software visualizations and onboarding visualizations [13,14]), clear terms of reference, mentor, etc.) and taxonomy of process practices are well documented [15], but the literature sources mostly present connections or contextual findings among concepts in the model [16,17]. Approaches to onboarding show that the way practices are designed changes the early outcomes of identification and performance in a new employee, but the precise mapping of “who affects what and in what way” is often implicit [8,18]. In addition, when new employees seek information or expand their network of acquaintances, they represent a strong step towards adaptation, but the effects are often indirect or conditional [19,20,21] through HR strategies and software tools.

Due to the large gap between academic findings and practice in industry, the MLR approach has been adopted in the SE community, which combines white literature (WL) and gray literature sources (GL) to reduce the pressure of publishing, incorporate the voice of people from practice, and identify aspects that are not sufficiently covered by scientific papers [22,23]. A critical review of GL in software engineering shows both benefits and risks with precise suggestions for assessing transparency and quality [4]. In management, there are clear guidelines on how GL should include the typology of sources and reliability criteria, and thus strengthen the methodological basis [24].

On that track, in the previous work of the first author [25], a model was developed that seeks to be further validated and refined in this research work by relying on a systematic search, theoretical validation with the help of WL and GL in order to confirm and refine the entities, and their semantic relations on the existing model. This model was created from notes, reflections, organizing notes, analysis, and visualization of entities and semantic relations. For the sake of terminological clarity, in this research paper, the same model will be labeled as “Experience-Based Onboarding Model” (EBOM).

1.2. Research Questions

Onboarding is a key step in the process of integrating employees into the company. At an organizational level, embedding onboarding in a digital HR strategy can increase consistency and measurement across the employee lifecycle [26]. The new employee adapts (the accent is in the IT environment) and is directly related to the clarity of a specific role, self-efficacy, and social acceptance. In addition, recent works on specific onboarding practices emphasize the need to identify “what exactly does this job” and under what conditions. This combination justifies checking which entities in the model [25] have stable support for multiple sources [3,16]. Based on the above, RQ1 is proposed.

(RQ1) Which entities from the Experience-based Onboarding Model have stable support in white and gray literature?

In accordance with the recommendations for a Systematic Literature Review (SLR) in software engineering [27], which can be found in Chapter 22, some of the procedures for searching WL and GL, as well as comparing entities and semantic relations originally derived from the EBOM, which was created through autoethnographic reflection with findings from the literature sources, were applied. In this way, a table of evidence based on the literature sources is formed, which provides a clear, precise, and repeatable framework for evaluation and is based on methodological soundness. Based on these findings, each entity and semantic relation will be categorized according to the rate scale: (1) Strongly confirmed (SC)—five or more independent sources, (2) Confirmed (CN)—two to four sources, (3) Provisional (PV)—one source, and (4) Experience-only (EO)—no confirmation from the literature sources, relying only on the personal experience of the first author.

The literature sources on socialization explicitly distinguish between indirect and conditional effects and emphasize that the choice of methodology affects whether the results can be interpreted as causal or only as an association. More recent research on the onboarding process carefully describes outcomes in the form of positive or negative associations and assigns them a score based on a specific context. In this way, relations can be classified according to a specific type [3,28]. Based on the items mentioned, the research question RQ2 arises.

(RQ2) Which relations between entities are confirmed in the literature, and what are their natures (causal effect, correlational effect, indirect effect, conditional effect)?

• Causal effect—it is presented in the form of a change in practice that is accompanied by an intervention and leads to a certain change in the outcome (structured training). In evaluations of formal onboarding, this was presented through RCTs and groups, where the programs increased competence and role clarity. Based on this application, they enabled a stronger conclusion about the effects of the intervention [29].
• Correlational effect—it is used when it is necessary to report a statistical association between elements, but without claiming causation. Within Socialization Resources Theory, resources such as support from a superior, feedback, and recognition are described as key factors that facilitate adaptation, can lead to different outcomes and can be called correlational [3,30].
• Indirect effect—mediation occurs when X influences Y through the variable Z. In socialization research, adjustment through role clarity, self-efficacy, and social acceptance has been shown to have socialization and information-seeking effects on outcomes such as job satisfaction, performance, and potential job retention. Such indirect relations were confirmed in the work [31].
• Conditional effect—when the direction of the relation changes depending on the conditions. The research paper [31] explicitly codes moderators such as longitudinal versus average design, transition from school to work versus transition from work to work, and measurement principle. In the software engineering literature, a study [10] indicates that remote onboarding makes socialization with the team very difficult, requiring additional steps in the integration of the new employee.

It is necessary to extract all relations in the form of S-P-O (Subject-Predicate-Object) from the existing model. For each relationship, appropriate WL and SL sources are targeted within the MLR steps that are already defined in Section 3. From each source, minimal statements about the relations between the entities and in which direction are derived. After that, the type of relation is assigned: (1) causal effect, (2) correlational effect, (3) indirect effect, and (4) conditional effect. For each existing relationship, independent sources from WL and GL are requested, and we assign the appropriate rating power (SC/CN/PV/EO). In this way, one can see how strong the relations between certain entities really are.

Onboarding in the IT/SE environment represents a risky and expensive phase, due to the complexity of knowledge and software tools, dependence on the team and other individuals, and the growing frequency of hybrid and remote work [32]. In this way, problems such as social integration, role clarity, and information flow increase. Based on a study at Microsoft, it can be concluded that there is a socialization deficit and the need for one-on-one meetings with cameras and mentoring [10]. In the previous work, a model [25] was developed through autoethnography, which presented a good basis, but with clear shortcomings: the border between mentors and managers is imprecise, the feedback loop of evaluation according to learning goals, methods, and resources is not explicit, and also, the types and directions of relationships are not uniform. For this reason, it is necessary to systematically establish and expand the model by relying on multiple sources: both WL and GL, with an explicit separation and evaluation of the types of relations listed in RQ2. In this paper, along with theoretical validation through WL and GL, the entities and semantic relations between them will be confirmed and supplemented, and the results will be translated into the OnMod model with transparent rules for including elements in the model. Based on the above, research question RQ3 is created.

(RQ3) Which new entities and semantic relations from the literature sources extend the EBOM?

In RQ3, the same sets of selected studies that are already included in the theoretical validation in RQ1 and RQ2 via WL and GL are used as a set of sources for extending the model. The second, targeted reading is performed on that set with an exclusive focus on new entities and relations. New statements about entities and relations that are not present in the previous tables are singled out, mapped in the form of S-P-O (if it is a relation between entities); they are assigned the type of relation as specified by the rules in RQ2 and are evaluated for their rating power (SC, CN, PV, EO) and records of WL and GL that include such an entity/relation by item. The results are placed in separate tables, especially the table for entities for semantic relations.

Research gap. Although there are numerous research papers describing the practices and outcomes of onboarding, there is a lack of models that: (1) integrate the white and gray literature that present a transparent validation framework, and (2) explicitly classify the nature and strength of the relations between the main onboarding entities. Also, existing studies rarely close the feedback loop by associating evaluation with adaptive updates of the state of goals, methods, and resources involved in the onboarding process. This paper addresses these shortcomings by validating an experience-based model (EBOM) through a multivocal literature review and refining it to an adaptive version of the model (OnMod).

Aim. This review aims to validate the Experience-Based Onboarding Model (EBOM) through a multivocal literature review (WL and GL) and to refine it into the adaptive OnMod model.

Paper outline. The outline of this paper is as follows: Section 2 presents previous research that is relevant to the entities and semantic relations that are covered by the model. Section 3 provides a detailed description of the methods and materials (autoethnography and a multivocal literature review), including the decision rules for assessing confirmation strength and classifying relation types between entities. Section 4 presents the validation results for entities and semantic relations, as well as the derived improvements that lead from the EBOM to the adaptive model OnMod. Section 5 provides answers to RQ1–RQ3 and synthesizes the theoretical and practical contributions of research. Section 6 summarizes the limitations that are present in the paper. Section 7 provides a conclusion and future research directions.

2. Literature Review

In this section, the relevant literature sources for the key parts of the model that are needed for confirmation and refinement will be briefly presented. These segments were originally derived from the EBOM that is proposed in [25], which represents a starting point for identifying and structuring the relevant literature. The content refers to the topics accompanying the onboarding process related to the relationship between the mentor and the manager, then the mechanisms for skill development and reflection, and the relation between goals and tasks with the presence of resources and methods, evaluation, and the contribution of the team and the community of practice.

2.1. Mentor and the Role of Manager

Research related to the agile context emphasizes that the presence of mentors, managers, and team support is crucial for the rapid inclusion of a new employee in the job. Gregory et al. [33] proposed a model for the onboarding process for agile teams that includes activities such as the following: (1) support and training, (2) adaptation to the new employee—self-efficacy, role, social integration, and (3) adaptation to the work environment—communication, composition, and number of the team. Based on the above, it is concluded that mentoring is recognized as a leading role in the onboarding process.

Organizational psychology states that managerial responses to seeking feedback, building relationships between employees, and general communicativeness in a new employee significantly encourage adaptation to the environment. This places the manager not only as an evaluator, but also as an active coordinator of organizational socialization and learning in the initial stages of the onboarding process [34]. Recent studies in the Open-Source-Software (OSS) and SE context further emphasize the importance of mentorship and task recommendations when new employees join a project [28,35].

2.2. Reflection and Skill Development

Empirical research on gamified sprint retrospectives shows that games encourage greater involvement, better communication, and greater creativity in teams. Also, this helps new employees in the team to develop skills faster, increase knowledge, and contribute more easily [36].

From the aspect of knowledge transfer, an in-depth analysis of pair programming identifies concrete and precise strategies for the transfer of expertise between experienced employees and new employees (beginners), e.g., verbalization, which offers a special mechanism for a designed mentoring practice that is based “on the code in the environment” [37], while complementary evidence shows that lightweight software visualization can support early comprehension and reflection [13].

2.3. Mapping Learning Objectives, Methods, Resources, and Tasks

“Good First Issue” (GFI) research shows that tasks assigned to new hires often go unsolved and take much longer than originally expected. This indicates a need for better alignment of tasks with specific competencies, better support, and more efficient use of resources [38]. This can be followed up by the ICSE work presented by RecCFI: more precisely, this is a practical approach to recommending initial tasks that is especially personalized for new hires. This works by directly addressing the association of goals, methods, resources, and appropriate tasks, thus relieving the employee who designed it, and increasing the likelihood of a successful first contribution of the new employee to the organization [39]. Recent studies introduce personalized GFI recommenders and compliance metrics that can predict acceptance of contributions [28,40].

2.4. Evaluation in the Onboarding Process

The meta-analysis shows that when new employees themselves seek to understand the work that is required of them, build relationships with colleagues, and look at the situation in a positive way, this then represents a positive impact on the socialization between them and other colleagues. Also, they come to understand the role, they have more self-confidence, they are satisfied with the work they do, and they want to stay with the company. Such results provide reliable indicators that can be used to measure the effectiveness of the onboarding process [19], and recent syntheses highlight role clarity, new employees’ self-efficacy, job satisfaction, and intention to remain as key outcomes [41].

In the context of software engineering, case studies in distributed teams suggest practical guidelines and indicators for how to monitor the progress of new hires, as well as the effects of strategies applied in onboarding processes in real-world conditions related to, e.g., quickly taking independent tasks, with recent work further operationalizing tool-supported monitoring of new employees’ progress and compliance indicators in SE [28,42,43].

An industry study suggests linking evaluation to a strategy that looks at the temporal dynamics of new employee contributions in distributed teams so that feedback can, over time, reshape the learning plan [44,45,46].

2.5. Team and Community of Practice

In agile environments, an interview study is presented that shows why organizations form Communities of Practice (CoP) in order to harmonize practices, transfer and spread knowledge, support roles, show what their structure would look like, and show what characteristics would affect its effectiveness. This would provide evidence that the CoP acts as a learning infrastructure that significantly accelerates the socialization of new employees [47,48].

In a reference case study from Ericsson, CoP types, their routines, and dynamics related to the rhythm of meetings and transparency are documented, showing how CoP reduces dependence on individuals and enables a high degree of continuous learning of the entire team with similar patterns that are reported in large-scale agile organizations [47,49].

3. Materials and Methods

The main objective of this study is to validate the EBOM through an MLR, encompassing the white and gray literature sources, and to perform its refinement, leading to the development of the OnMod model. Accordingly, the methodological approach in this research starts from the previously developed EBOM, aiming at its theoretical validation and refinement. In order to achieve the goals, an MLR is applied to integrate WL and GL, which identifies and addresses gaps and deficiencies, and confirms existing entities and semantic relations, as well as introduces new ones when recognized through the literature sources.

Figure 1 provides an overview of the methodological approach used in this research paper. The light gray block represents the starting point, the model that was developed in the previous autoethnographic work [25]. After that, the yellow blocks show steps in this research: gap analysis, theoretical validation of entities and semantic relations, and where WL and GL will be used (blue blocks), and ends with an improvement that will give an updated model called the OnMod model.

3.1. Research Design and Overview

This scientific paper uses a combination of autoethnography and a multivocal literature review (MLR) that includes WL and GL [50,51,52,53], intending to perform theoretical validation and thus, to expand the initial onboarding model that originated through personal reflection and experience of the first author [25]. Autoethnography will serve as a process and as a systematic analysis of the author’s personal experience in the IT environment to understand the organizational concept of honesty and responsibilities, as well as clearly presenting that methods are taken into account, which are used data sources, and which are present limits of reliability in order to reach epistemological transparency [54].

In this paper, an analytical type of autoethnography will be used that will follow Anderson’s specific approach to autoethnography that combines personal experience and theoretical analysis through five important features [55]: (1) Status of “Complete member researcher”—author is not only the observer in this case, but a member of a team or group that actively participates in a process with relevant experience. (2) Analytical reflexivity—the author consciously examines their role, position, and influence on overall research with particular attention to the subject. (3) Narrative visibility—the author is fully present in the content of the paper, making it very transparent to the reader who is conducting research, from which perspective it is written, and how the analysis of experience is presented. (4) Dialog with Actors—although “user story” begins from the angle of personal experience, the author supplements perspective through interaction with external types of sources that include interviews, conversations, and documents to reach a comprehensive conclusion. (5) Commitment to Theoretical Explanation—the goal is to draw a theoretical conclusion that will contribute to scientific understanding and greater application.

3.2. EBOM and Gaps

This research work is based on an actual period of an onboarding process and includes a data set from personal experience of the first author that includes segments like daily reflection, notes that are written both in digital and paper form on daily basis, interaction with mentor through a Slack channel, email, initial meeting with the manager, weekly team meetings, and internal materials such as internal tutorial, software, documentation, tasks and objectives, as well as a progress-tracking tool [25]. All types of notes were recorded at specific time events with the aim of preserving chronological accuracy and grounding.

The data set was processed through iterative reflection periods. The notes were analyzed and grouped thematically by a set of similar segments formed around concepts that were already repeated, including relevant concepts such as the first author’s personal reflection, mentor and manager’s support, resources, learning goals, evaluation, and interaction with the team. In this process, there was no formal analysis of the theory; instead, the focus was on grouping and examining patterns in a practical example of the onboarding process. Based on this data organization, the data set is represented through entities. The entities consisted of Mentor, Manager, Resources, Learning Objectives, Evaluation, Team, Role, Skills, etc., and the author’s assumptions about the relation through semantic relationships between the mentioned entities.

An entity represents a clearly defined, identified entity of the model that includes segments such as Mentor, Manager, and Resource, while a semantic relation represents a relationship between two entities with a specified predicate, such as mentoring supporting learning.

Through a thematic data set, a sketch of the initial skeleton of the onboarding process was made on paper, after which the initial model was transferred to a digital form for easier and better presentation and organization. During several rounds of organization and analysis of the notes, the following items were examined: (1) “Are entities clear enough?”, (2) “Is there a clear distinction between certain entities?”, and (3) “Are directions and types of relations adequately presented and connected?”. In these stages, narrative episodes are presented as key links that are connected to specific events in practice. After several iterations of refinement, analysis, organization, and visualization, the “Final onboarding model” [25] was created, which unites the main entities in the onboarding process as well as the semantic relations that emerged from personal experiences. This presentation of the model represents the starting point of this paper’s research, i.e., systematic theoretical validation with the goal of examining entities, and semantic relations, as well as possible extensibility of the model based on WL and GL.

Figure 2 presents the unified initial model that was created from the autoethnographic data set and the relationship between the key entities in the onboarding process. The model was presented through a specific process in the IT sector and serves as a starting segment on which theoretical validation was not performed.

Although the model already includes entities such as Mentor, Manager, Role, and Resources, there are also several methodological and logical gaps that make it difficult to clearly interpret and apply the process. Some of the gaps are as follows: (1) The boundary between the Mentor and Manager entities is imprecise in the sense that there are semantic relations directed towards Learning Objectives and the project; it is unclear who enables and supports the development of the knowledge level and who contributes to such decisions. (2) Evaluation is modeled as a simple entity without a specific emphasis on whether it can be performed during learning or only at the end of the process. (3) There is no feedback in the direction of the entities of Learning Objectives and method related to the outcome of the evaluation and its adjustment after insufficiently acquired knowledge and skills. (4) The entity Resources is presented semantically as too broad a term because certain assets, such as tools, documentation, and the principle of work (code review, mini tasks from mentor), are mixed, which can make it difficult to understand the roles of these entities. (5) Semantic relations between entities are not uniform; different verbs are used, and the type and direction of relations are not clear.

These gaps are quite expected for a reflexive approach because this model [25] was created from personal experience and motivates further theoretical validation in Section 3.3.

3.3. Validation Approach: Multivocal Literature Review

In this subsection, the theoretical validation procedure of the EBOM will be described. The validation process includes the following steps: (1) define the literature sources of evidence through WL and GL, (2) search query, (3) define inclusion and exclusion criteria, (4) define the search flow, (5) describe the extraction and mapping of findings to entities and semantic relations through subjects, predicates, and objects (S-P-O) with a certain type of relations, and (6) define and introduce a scale for the strength of evidence and decision rules according to which certain elements will be integrated into the new onboarding model OnMod.

For the validation approach, WL and GL are used to align the initial model with evidence according to the multivocal literature review (MLR) framework in software engineering. White literature (WL) refers to peer-reviewed scientific works of high quality and relevance (journals and peer-reviewed conferences) that have undergone peer review and rely on a more thorough grounding of the model and a more stable approach to research [24,56,57]. Gray literature (GL) includes diverse sources and documents created by various institutions such as state government, faculty, and industry, whether in print or digital format, but that are outside the control of commercial publishers and thus, offer a rich overview and recommendation for its critical integration [4,22,58,59]. Methodological works emphasize the advantages of using and including GL with the aim of reducing bias in the publication of scientific works, which would encourage greater up-to-dateness and the use of a wider database of the literature sources [23,59].

In order to clearly distinguish the literature sources, GL is separated from WL and grouped based on the type and degree of editorial control: (1) A higher degree of control includes government reports, business reports, white papers, theses, and dissertations. (2) A moderate level of control includes presentations that can be found on the Internet, videos within platforms, etc. (3) The lowest level of control includes blogs consisting of posts. This type of GL classification is based on the recommendation of the MLR in software engineering, where types must be defined first, and only then search, selection, and their use [23,58,59].

3.4. Research Question and Operationalization

This section presents how the theoretical validation of the initial model was performed. In accordance with the recommendations for a systematic literature review (SLR) in software engineering, certain procedures from the book [27] were applied in this paper. The author’s guidelines from the mentioned book are written specifically for the needs of the software engineering community, where methodological support is used for segments such as defining questions, searching papers, selection, and other steps [27]. Also, below, the research questions are elaborated on the verification of the model: (1) RQ1—refers to the verification of the entity FOM in the literature sources of WL and GL. (2) RQ2—refers to checking and determining the type of relations, such as causal, supportive, indirect, and contextual. (3) RQ3—refers to the finding of new entities and semantic relations after finding the “Strongly confirmed” or “Confirmed” confirmation rating of each entity and semantic relation into a new model called OnMod.

3.5. Search Strategy and Data Sources

As shown in Figure 3, the light gray blocks represent the stages of the literature search, while the light yellow blocks refer to the output of each stage.

For this research paper, three primary keywords were proposed:

• Keyword 1 (KW1)–“Onboarding” OR “Newcomer adjustment” OR “Employee orientation”.
• Keyword 2 (KW2)–(“Software Engineering” OR “SE”) OR (“Information Technology” OR “IT”) OR “Engineering teams”.
• Keyword 3 (KW3)–“Mentor” OR “Buddy program” OR “Knowledge transfer” OR “Training” OR “Code review” OR “Pair programming” OR “Evaluation”.

These three primary keywords represent the basic concepts that direct the search for relevant WL and GL sources. In order to find the literature sources more precisely, logical operators AND and OR were used to connect the mentioned terms. Combining the three keywords forms a search query such as the following: (KW1) AND (KW2) AND (KW3).

This WL search query has been applied to databases such as Google Scholar, IEEE Xplore, MDPI, ScienceDirect, and Wiley. As for the GL, the search was carried out by a general web search with identical keywords, where various types of blogs (engineering, Medium, Dev.to, etc.) would be found, white papers, a search on Google Scholar, a search for posts on the LinkedIn platform containing the keywords “Onboarding”, “Onboarding” and “Companies”, “Onboarding” and “Companies” and “Learning” in PDF format.

3.6. Inclusion and Exclusion Criteria

The selection of the literature sources for this research work is carried out in accordance with certain inclusion criteria. The proposed inclusion criteria are as follows: (I1) Literature sources that explicitly deal with onboarding or explicit socialization in the IT or SE sector or business practices related to the keywords mentorship, buddy program, documentation, knowledge transfer, code review, pair programming, evaluation outcomes. (I2) WL sources that are available in full text in PDF format. (I3) Web blogs, white papers, corporate documentation, non-reviewed conference papers, LinkedIn articles, internal documentation of the first author who is received in the onboarding process. (I4) Literature sources that are exclusively in English. (I5) Alignment of title, abstract, and keywords (T+A+KW) with the topic, with reading of the literature sources being necessary to ensure focus on successful literature search. (I6) Ability to map findings from the literature sources to model elements related to entity and/or semantic relation. (I7) Works published in the range of 2015 to 2025.

In order to ensure the relevance and quality of the selected literature sources, exclusion criteria were also applied: (E1) literature sources that are not in the domain of IT and SE or domains that are similar to the IT and SE domain, (E2) literature sources that do not contain enough information to compare or map the entities and semantic relations of the model, (E3) duplicates, (E4) literature sources that are not written in English, (E5) literature sources that do not contain the full text, and (E6) literature sources that were published before 2015.

3.7. Study Selection

A search of the literature sources was conducted for WL and GL in selected databases, and in a web search with defined keywords and detailed inclusion and exclusion criteria in accordance with the guidelines in the book [27] and the MLR access rules. A total of 92 records were identified in the period from 2015 to 2025. After checking the title, abstract, textual part of the source, 8 duplicates (E3), 20 works that are outside the domain of IT and SE (E1), 5 records without full text content (E5), 15 works that were published before 2015 (E6), and 7 sources that do not contain enough data to be considered for mapping to the existing model were excluded (E2). A total of 37 selected studies (SS) were selected for detailed analysis, as presented in

Table 1. Selected studies for model validation.

Selected Studies	Type	Authors Name	Title	Reference
SS1	WL	Britto, R.; Šmite, D.; Damm, L. O.; Börstler, J.	Evaluating and strategizing the onboarding of software developers in large-scale globally distributed projects	[44]
SS2	WL	Santos, I.; Felizardo, K. R.; Steinmacher, I.; Gerosa, M.	Software solutions for newcomers’ onboarding in software projects: A systematic literature review	[43]
SS3	WL	Pham, R.; Kiesling, S.; Singer, L.; Schneider, K.	Onboarding inexperienced developers: Struggles and perceptions regarding automated testing	[60]
SS4	WL	Buchan, J.; MacDonell, S. G.; Yang, J.	Effective team onboarding in agile software development: Techniques and goals	[8]
SS5	WL	Ju, A.; Sajnani, H.; Kelly, S.; Herzig, K.	A case study of onboarding in software teams: Tasks and strategies	[61]
SS6	WL	Rodeghero, P.; Zimmermann, T.; Houck, B.; Ford, D.	Please turn your cameras on: Remote onboarding of software developers during a pandemic	[10]
SS7	WL	Godinho, T.; Reis, I. P.; Carvalho, R.; Martinho, F.	Onboarding handbook: An indispensable tool for onboarding processes	[62]
SS8	WL	Pinco, O.; Crișan, E. L.	The first impression matters: A literature review on employee onboarding	[63]
SS9	WL	Pinco, O.; Salanță, I. I.; Beleiu, I. N.; Crișan, E. L.	The onboarding process: A review	[64]
SS10	WL	Jeske, D.; Olson, D.	Onboarding new hires: Recognizing mutual learning opportunities	[65]
SS11	WL	Azanza, M.; Irastorza, A.; Medeiros, R.; Díaz, O.	Onboarding in software product lines: Concept maps as welcome guides	[66]
SS12	WL	Pal, M.	Workplace challenges for new employees: An existing view	[67]
SS13	WL	Aamer, T.; Milani, F.	Improving digital onboarding processes for financial services—A multivocal literature review	[68]
SS14	WL	Petrilli, S.; Galuppo, L.; Ripamonti, S. C.	Digital onboarding: Facilitators and barriers to improve worker experience	[69]
SS15	WL	Tot, V.	Analysis of the onboarding and orientation process of employees in Central Serbian organizations	[70]
SS16	WL	Menon, S.; Narayanan, L.; Hampton, A. J.; Whitlock, D. W.; Kennedy, E.	Virtual onboarding effectiveness: a comparison of factors influencing perceptions	[71]
SS17	WL	Caldwell, C.; Peters, R.	New employee onboarding—Psychological contracts and ethical perspectives	[72]
SS18	WL	Subash, S.; Rani, J.	A study on onboarding process of new employees in Annalect	[73]
SS19	WL	Lemmen, C.; Sommer, P. S.	Good modeling software practices	[74]
SS20	WL	Turzo, A. K.; Sultana, S.; Bosu, A.	From first patch to long-term contributor: Evaluating onboarding recommendations for OSS newcomers	[28]
SS21	GL	HR Cloud	The Psychology of Onboarding: Understanding New Hire Anxiety and Expectations	[75]
SS22	GL	National Archives and Records Administration (NARA)	Agency Onboarding and Offboarding Processes: Assessment Report	[76]
SS23	GL	Oregon Department of Administrative Services, Chief Human Resources Office	Onboarding Guide for Oregon State Government. Salem, OR, USA	[77]
SS24	GL	Council of University of California Staff Assemblies (CUCSA)	Onboarding Workgroup Final Report	[78]
SS25	GL	Wilcox, L.	White paper: Onboarding fire service recruits can help change the fire service culture	[79]
SS26	GL	Boateng, M. O.	Examining the Relationship Between Onboarding Practices and Employee Turnover Within the First Year of Hire	[80]
SS27	GL	Bowers, J. C.	Investigating Onboarding Practices for New Principals	[81]
SS28	GL	ADP–DiStasio, C.	8 onboarding best practices to boost new-hire engagement and retention	[82]
SS29	GL	Mitrofanova, E.	Value in Onboarding: Designing the New Employee Onboarding Procedure for a Hybrid Working Environment	[83]
SS30	GL	Murphy, S.	Phenomenological Exploration of Newcomer Onboarding	[84]
SS31	GL	Geiger, E.	How to evaluate and improve onboarding	[85]
SS32	GL	Pavlina, K.	Assessing Best Practices for the Virtual Onboarding of New Hires in the Technology Industry	[86]
SS33	GL	GitLab	Onboarding buddies	[87]
SS34	GL	Banut, I.; Ragauskaitė, Ž.	The Relationship between the Onboarding Training Program and Employees’ Intentions to Leave an Organization	[88]
SS35	GL	Eduflow	5 psychological concepts to improve your onboarding training	[89]
SS36	GL	Workwize	New rules of employee onboarding. Practitioner guide	[90]
SS37	GL	BambooHR–Whitlock, C.	Ask an HR expert: Creating a successful onboarding program	[91]

Details on confirmation strength and visual notation are provided in Section 3.8.

.

3.8. Decision Rules and Notations

The criteria for evaluating the strength of confirmation of each entity and semantic relation are evaluated with a simple and transparent rating scale that will be consistently used in this paper and in the graphical representation of the model:

• Strongly confirmed (SC)—when there are five or more independent literature sources.
• Confirmed (CN)—when there are two to four independent literature sources.
• Provisional (PV)—when there is only one literature source.
• Experience-only (EO)—when there is no confirmation from a literature source and when the entity/semantic relation comes only from the author’s personal experience.

Only certain items that reach the SC or CN rating will enter the new model called OnMod. At the same time, the PV will be kept as a “potential candidate” that can enter the model once the additional literature sources are found to confirm the use of the entity/semantic relation. As for the items that contain EO, they will remain as items that arose from the author’s personal experience in the onboarding process. Each item that is evaluated through the criteria will be represented by a specific redundant coding as follows: (1) SC is displayed with a solid gray line if it is a semantic relation, i.e., a gray block if it is an entity, and will contain the label (SC). (2) CN is displayed with a solid green line if it is a semantic relation, i.e., a block of green color if it is an entity, and will contain the designation (CN). (3) PV is displayed with a dashed cyan line if it is a semantic relation, i.e., a block of cyan color if it is an entity, and will contain a mark (PV). (4) EO is shown with a dashed red line if it is a semantic relation, i.e., a block of red color if it is an entity, and will contain the mark (EO).

4. Results of Theoretical Validation

In the data set of 37 selected studies, the literature sources from WL and GL were carefully selected based on specific inclusion and exclusion criteria. Validation was carried out through WL and GL, which includes: (1) validation of entities from FOM, and (2) validation and confirmation of the type of semantic relations.

For each item, all findings from the literature sources from WL and GL will be collected and assigned rating power (SC, CN, PV, EO) with consistent follow-up of primary sources.

4.1. Entity Validation

In this section, the goal is to confirm whether the entities from the initial FOM model are found in the selected WL and GL sources, and it is necessary to assign them a confirmation strength rating according to the criteria (SC, CN, PV, EO). The multivocal literature used includes peer-reviewed works from the domains of SE and IT, management (teamwork, manager’s role, learning objectives, skills), and GL that exude rich content such as reports, government guidelines, dissertations, manuals, and others.

Only SC and CN entities will be imported into the OnMod model as noted in Section 3.8. Validated entities are shown in

Table 2. Entities of the EBOM and their validation through WL and GL.

Entity	WL (SSn)	GL (SSn)	Rate
New employee	SS4, SS5, SS6, SS7, SS8, SS10, SS12, SS17	SS21, SS23, SS28, SS30	SC
Mentor	SS1, SS2, SS4, SS5, SS6, SS7, SS10, SS20	SS23, SS24, SS28, SS33, SS37	SC
Manager	SS4, SS7, SS15, SS17	SS22,SS23, SS28, SS29, SS33, SS37	SC
Team	SS1, SS4, SS5, SS6, SS10, SS20	SS23, SS24, SS25, SS3, SS29	SC
Role	SS4, SS5, SS7, SS10, SS11, SS20	SS22, SS23, SS28, SS29, SS33	SC
Methods	SS2, SS3, SS4, SS5, SS6, SS11, SS13, SS14, SS16, SS18, SS20	SS23, SS24,SS27, SS28, SS32, SS33, SS36, SS37	SC
Resources	SS1, SS2, SS7, SS11, SS14, SS20	SS22, SS23, SS28, SS29, SS33, SS36, SS37	SC
Learning Objectives	SS1, SS4, SS7, SS10, SS11, SS16, SS20	SS23, SS28, SS35	SC
New skills	SS1, SS5, SS6, SS7, SS11, SS14	SS24, SS28, SS32, SS34	SC
Project	SS1, SS2, SS3, SS4, SS5, SS20	SS23, SS33	SC
Evaluation	SS1, SS2, SS3, SS7, SS9, SS20	SS22, SS24, SS26, SS31, SS33	SC
Successfully Integrated	SS1, SS7, SS14, SS20	SS23, SS26, SS28, SS29	SC

.

For the validation of the New Employee entity, studies [SS4], [SS5], [SS6], [SS7], [SS8], [SS10], [SS12], [SS17] from the WL source and studies [SS21], [SS23], [SS28], [SS30] from the GL source were selected. These literature sources confirm that the new employee is a central unit in the onboarding process and that, based on the orientation, the onboarding plan, and the supervision itself, they can speed up the process and increase its productivity.

Studies [SS1], [SS2], [SS4], [SS5], [SS6], [SS7], [SS10], [SS20] from the WL source and studies [SS23], [SS24], [SS28], [SS33], [SS37] from the GL source were selected for Mentor entity validation. These findings show that the mentor has an important role in learning, socialization, and overcoming barriers for the new employee.

To validate the Manager entity, studies [SS4], [SS7], [SS15], [SS17] from the WL source and studies [SS22], [SS23], [SS28], [SS29], [SS33], [SS37] from the GL were taken into account. Based on the literature sources, it can be said that the manager is the one who leads the process, sets goals and observations in the onboarding process, provides the necessary resources, and organizes meetings, and thus ensures the monitoring of the individual’s progress and daily learning.

For Team entity validation, studies [SS1], [SS4], [SS5], [SS6], [SS10], and [SS20] from the WL source, and studies [SS23], [SS24], [SS25], and [SS29] from the GL source were selected. The team within the company and the community (if it is a corporation) represents a type of environment where socialization, the exchange of information, and the sharing of knowledge occur through joint work, discussion, and the review of practices.

For the validation of the Role entity, studies [SS4], [SS5], [SS7], [SS10], [SS11], and [SS20] from the WL source and studies [SS22], [SS23], [SS28], [SS29], and [SS33] from the GL source were selected. Based on the review of these literature sources, it can be concluded that the role of the new employee must be clearly defined with a plan and certain expectations, because in this way it is easier to adopt the rules about the standards of the process and the achievement of the expected results.

For the validation of the Methods entity, studies [SS2], [SS3], [SS4], [SS5], [SS6], [SS11], [SS13], [SS14], [SS16], [SS18], and [SS20] from the WL source and studies [SS23], [SS24], [SS27], [SS28], [SS32], [SS33], [SS36], and [SS37] from GL sources were selected. Based on the selected literature sources, it can be concluded that initial tasks, work in pairs, code review, and revision of written code positively guide and quickly encourage a new employee to learn and join the team and the company.

For the validation of the Resources entity, studies [SS1], [SS2], [SS7], [SS11], [SS14], and [SS20] from WL sources and studies [SS22], [SS23], [SS28], [SS29], [SS33], [SS36], and [SS37] from GL sources were selected. Based on the selected literature, it can be concluded that the standard documentation found in the company (manuals, guides, etc.) greatly simplifies navigation, reduces the burden of the new employee, and thus ensures effective access to information and learning.

Studies [SS1], [SS4], [SS7], [SS10], [SS11], [SS16], and [SS20] from the WL source and studies [SS23], [SS28], and [SS35] from the GL source were selected for the validation of the Learning Objectives entity. Findings from the aforementioned literature sources can contribute to faster evaluation and integration into the company, if clear learning goals are defined that are aligned with the role and task/project.

For the validation of the New Skills entity, studies [SS1], [SS5], [SS6], [SS7], [SS11], and [SS14] from the WL source and studies [SS24], [SS28], [SS32], and [SS34] from the GL source were selected. These selected studies suggest that if the activities are structured and if there is some form of checking and feedback, it is possible to greatly improve the process of technical, soft, and team skills in the early stages of onboarding.

For Project entity validation, studies [SS1], [SS2], [SS3], [SS4], [SS5], and [SS20] from the WL source and studies [SS23] and [SS33] from the GL source were selected. Sources dictate that the context of the project is shaped through the initial strategy of creating the onboarding process, where it is necessary to devise previous items that will include tasks, resources, skills, and standards that are defined within the company with the aim of raising the quality and degree of learning, as well as the confidence of the new employee.

In validating the Evaluation entity, studies [SS1], [SS2], [SS3], [SS7], [SS9], and [SS20] from the WL source and studies [SS22], [SS24], [SS26], [SS31], and [SS33] from the GL source were selected. The literature sources talk about evaluating the progress of a new employee who will go through certain formal checks, such as a survey, test, or metrics.

For the validation of the successfully integrated entity, studies [SS1], [SS7], [SS14], and [SS20] from the WL source, and studies [SS23], [SS26], [SS28], and [SS29] from the GL source were selected. Based on the literature, the successful integration of a new employee can be established through achieving goals, successful project implementation, and independence in overcoming mini tasks defined by mentors and managers. Based on WL and GL, it can be said that if plans are clearly defined, mentoring steps, relevant resources, and support can shape a new employee’s time of adaptability and success.

Based on the entity validation results, it can be concluded that all the entities originally derived from the EBOM have received strong SC validation from the literature sources. This result emphasizes that the initial model, which is based on experience, is presented through a valid starting point and that the methodological approach in this research is completely transparent and reliable. Based on this, it can be concluded that the relevance of the model is confirmed and that a solid foundation is laid for further validation of semantic relations.

4.2. Semantic Relation Validation

In this subsection, Table 2 presents semantic relations in the form S-P-O (S—subject represents the source of the relation, P—represents the predicate, i.e., the form of the name of the verb between the source of the relation and the target of the relation, and O—object, which represents the goal or destination of the relation). The primary names of semantic relations in the FOM model contain descriptive tags such as has, uses, etc., which did not describe the functionality or influence between the two entities clearly enough, and for that reason, based on the selected sources WL and GL from Table 1, will be selected for each pair of entities and their predicate will be replaced by a substantiated version, in the columns WL(SSn) and GL(SSn) will be as listed the literature sources that contributed to the validation of the semantic relations as well as the change in the predicate between the two entities. Additionally, each relation will be marked with the appropriate type: (1) causal effect, (2) correlational, (3) indirect effect, and (4) conditional effect and evaluated on a scale of strength according to the same principle as in entity validation; (1) Strongly confirmed (SC), (2) Confirmed (CN), (3) Provisional (PV), (4) Experience-only (EO).

Based on the review of the literature sources, in order to represent the mechanism arising from the cited literature, S and O cannot be identical, as they are represented in the FOM model. The literature sources clearly point to a different course of action that requires a redirection of the arrow between the two entities. Additionally, the arrows that contained short verbs were replaced with functional and meaningful names, so instead of has, joins, etc., there are now verbs like integrates, clarifies, develops, etc. This type of change in line guidelines and predicate names between two entities, which is performed with the help of WL and GL sources, is also listed in

Table 3. Semantic relations of the EBOM and their validation through WL and GL.

Subject (S)	Predicate (P)	Object (O)	Type	WL (SSn)	GL (SSn)	Rate
Mentor	Facilitates	New employee	correlational	SS1, SS4, SS5, SS6, SS7, SS20	SS23, SS24, SS28, SS37	SC
Mentor	Collaborates	Team	correlational	SS1, SS4, SS5, SS10, SS20	SS23, SS24, SS28, SS33	SC
Team	Integrates	New employee	correlational	SS4, SS5, SS10, SS20	SS23, SS24, SS29	SC
Team	Applies	Methods	correlational	SS4, SS5	SS23, SS24	CN
New employee	performs the assigned task	Project	correlational	SS1, SS4, SS5, SS20	SS23, SS28, SS33	SC
New Employee	Adopts	Role	correlational	SS4	SS23	CN
Manager	Clarifies	Role	correlational	SS4, SS7, SS17	SS22, SS23, SS28, SS33	SC
Evaluation	Verifies	Successfully integrated	causal/ correlational	SS1, SS2, SS7, SS9, SS20	SS22, SS24, SS26, SS28, SS31, SS33	SC
Evaluation	Assesses progress	Project	correlational	SS1, SS2, SS7, SS9, SS20	SS22, SS24, SS26, SS28, SS31, SS33	SC
Resource	Mediate	New skills	indirect	SS2,SS7, SS11	SS23, SS37	SC
Resource	Support	Learning objective	correlational	SS2, SS4, SS7, SS11, SS14	SS23	SC
New Skills	Enable contribution	Project	causal/ correlational	SS1, SS5, SS20, SS3, SS4	SS23, SS32	SC
Methods	Leverage	Resources	correlational	SS2, SS7, SS11, SS14	SS23, SS37	SC
Role	Defines	Learning objectives	correlational	SS4, SS7, SS11, SS20	SS23	SC
Role	Situates	Team	conditional	SS4, SS5	SS23, SS29	CN
Learning objective	guide development of	New skills	correlational	SS4, SS7, SS11, SS20	SS23	SC

.

The validation of the semantic relation Mentor–facilitates–New Employee is confirmed by works [SS1], [SS4], [SS5], [SS6], [SS7], and [SS20] from WL and sources [SS23], [SS24], [SS28], and [SS37] which consistently present that mentoring support greatly facilitates the progress of a new employee, and accelerates his learning and socialization with other employees.

The validation of the Mentor–collaborates–Team semantic relation is confirmed by works [SS1], [SS4], [SS5], [SS10], and [SS20] from WL and sources [SS23], [SS24], [SS28], and [SS33] from GL, which suggest that the mentor functions so that he is from the same team as the new employee, participates in organizing and performing onboarding, and in this way supports the new employee.

The validation of the semantic relation Team–integrates–New Employee is confirmed by works [SS4], [SS5], [SS10], and [SS20] from WL and sources [SS23], [SS24], and [SS29], which prove that the team, as part of onboarding, can influence better socialization and motivation of the new employee through joint work and review practices.

The validation of the semantic relation New Employee–performs assigned task–Project is confirmed by works [SS4] and [SS5] from WL and sources [SS23] and [SS24] from GL, where it is indicated that if the task/project is well designed, the new employee can improve their knowledge and speed up their productivity.

The validation of the semantic relation New Employee–adopts–Role is confirmed by work [SS4] from WL and source [SS23] from GL, where it is indicated that the new employee assumes his role during the onboarding process, which was previously defined through the plan.

The validation of the semantic relation Manager–clarifies–Role is confirmed by works [SS4], [SS7], and [SS17] from WL and sources [SS22], [SS23], [SS28], and [SS33], where it is stated that the manager expresses the expectations of onboarding, as well as his responsibilities, through the created plan.

The validation of the semantic relation Evaluation–assesses progress–Project is confirmed by works [SS1], [SS2], [SS7], [SS9], and [SS20] from WL and sources [SS22], [SS24], [SS26], [SS28], [SS31], and [SS33] from GL, which confirm that a new employee can progress well if there are systematic measurements on the project that will be used for checking and guiding in the further process onboarding.

The validation of the semantic relation Resources–Support–Learning Objective is confirmed by works [SS2], [SS4], [SS7], [SS11], and [SS14] from WL and source [SS23] from GL, that talks about how resources can elaborate on learning objectives and enable effective implementation.

The validation of the semantic relation New Skills–enable contribution–Project is confirmed by works [SS1], [SS3], [SS4], [SS5], and [SS20] from WL and sources [SS23] and [SS32], which agree that when acquiring new skills, it is possible to have a more independent and better contribution to the task/project.

The validation of the semantic relation Methods–leverage–Resources is confirmed by works [SS2], [SS7], [SS11], and [SS14] from WL and sources [SS23] and [SS37], which emphasize that manuals and guides are created during the creation of the onboarding process, and thus new employees can fully rely on them.

The validation of the semantic relation Role–defines–Learning Objectives is confirmed by works [SS4], [SS7], [SS11], and [SS20] from WL and source [SS23], which emphasize that for each role there is an adequate responsibility and learning objective.

The validation of the semantic relation Role–situations–Team is confirmed by works [SS4] and [SS5] from WL and sources [SS23] and [SS29], which indicate that a role matches in a team based on its engagement.

The validation of the semantic relation Learning Objectives–guide development of New Skills is confirmed by works [SS4], [SS7], [SS11], and [SS20] from WL and source [SS23], where researchers confirm that clearly defined objectives are aimed at skills that will be specifically developed, in what way, and in what order.

Based on the validation of semantic relations, it can be concluded that all relations between EBOM entities are confirmed through WL and GL and are distributed according to the confirmation strength criteria. Most of the routes received instances of SC, while a smaller number of instances with CN were confirmed. This validation result confirms that the identified relationships had a good basis in the EBOM, while the MLR provided clear categorization and additional confirmation through the literature sources. In this way, it can be concluded that the relations did not arise by chance, but that they resulted from the autoethnographic approach and that they are consistent with the statements found in the literature sources.

4.3. Refined Onboarding Model

The EBOM, as a starting point, served as a good basis for an onboarding process, but several shortcomings were observed in: (1) the methodological field—in this field, the EBOM shows insufficiently concrete design, choice and description of methods, data presentation, types of validation [92], and (2) the logical field—in this field, deficiencies of clear representations of the internal structure of the model related to whether relations and entities are meaningfully derived [93] were observed.

Some of the disadvantages are unclear semantic relations, ambiguity in entity functionality, and unclear boundaries between certain entity roles, such as Mentor and Manager. Based on such weaknesses, it was necessary to improve the model through multivocal validation (using WL and GL) and introduce explicit functions in order to close the learning cycle during onboarding and to make the process path clear.

In order to address the shortcomings, three new entities were identified through the literature that were strongly confirmed through WL and GL, namely: (1) buddy program, (2) onboarding plan, and (3) feedback, as shown in

Table 4. Description of new entities in the model.

Entity	Reason	WL (SSn)	GL (SSn)
Buddy program	SC	SS1, SS5, SS20	SS23, SS24, SS28, SS37
Onboarding plan	SC	SS4, SS7, SS20	SS23, SS24, SS33
Feedback	SC	SS3, SS5, SS6, SS20	SS24, SS28

. Inserting these entities remains consistent with the rule for visual coding (color of lines and blocks) from Section 3.2; only those items rated SC and CN will be listed in the model.

As shown in Table 4, based on the literature sources of WL [SS1], [SS5], and [SS20] and [SS23], [SS24], [SS28], and [SS37] from GL, it can be concluded that the buddy program represents a type of role that can complement the support of mentors and managers in the initial stages of onboarding. This person represents the first “contact person” who will help with orientation regarding other employees, defined processes in the company, and the tools used, and networking with colleagues, and thus reduces uncertainty. Based on that, a difference would be made between a mentor who would deal with technical teaching and a buddy who would serve for social and organizational navigation.

For the entity Onboarding Plan, based on the literature sources WL [SS4], [SS7], and [SS20] and [SS23], [SS24], and [SS33] from GL, this type of document/plan aligns learning objectives, initial tasks/project, and evaluation points in real time. In this way, a mechanism is provided that is missing in the EBOM, which is precisely the clear relation of “what to learn”, “how to learn”, “what is checked”, and “where is checked”, with full transparent responsibility of other employees in the company.

Based on the literature sources of WL [SS3], [SS5], [SS6], and [SS20] and [SS24], and [SS28] from GL, the Feedback entity concretizes the adaptive direction in onboarding: the results after the evaluation (test, and feedback from the mentor, team, or manager) are returned to the system to update the methods, the learning objectives, and the onboarding plan, thus shaping the onboarding as an iterative process, not a linear one.

In addition to entity validation, new semantic relations were identified from the literature sources and are presented in

Table 5. Description of new semantic relations in the model.

Subject (S)	Predicate (P)	Object (O)	Arrow Type	Type	Rate	WL (SSn)	GL (SSn)
Manager	Collaborates	Mentor	↔	causal/ conditional	CN	SS4, SS7	SS23, SS28
Manager	Authorizes	Resources	→	correlational	SC	SS4	SS22, SS23, SS33, SS28
Manager	Sets	Learning objectives	→	correlational	SC	SS4, SS7, SS17	SS22, SS23
Team	Informs	Evaluation	→	correlational	CN	SS4	SS24
Evaluation	Yields	Feedback	→	causal/ correlational	SC	SS1, SS7, SS20	SS22, SS26, SS31, SS33
Evaluation	Verifies	New skills	→	causal/ correlational	SC	SS1, SS3, SS7, SS16, SS20	SS22, SS24, SS26, SS31, SS33
Evaluation	Verifies attainment of	Learning objective	→	causal/ correlational	SC	SS1, SS2, SS7, SS9, SS20	SS22, SS24, SS26, SS31, SS33
Feedback	Adjusts	Learning objectives	→	correlational	SC	SS3, SS5, SS7	SS24, SS28
Feedback	Refines	Methods	→	correlational	CN	SS3, SS5	SS24, SS28
Feedback	Informs	Manager	→	correlational	SC	SS4, SS7, SS17	SS22, SS23, SS28, SS33
Feedback	Informs	Mentor	→	correlational	CN	SS5, SS20	SS23, SS24
Feedback	Updates	Onboarding plan	→	causal	CN	SS4, SS7, SS20	SS23
Feedback	Clarifies	Role	→	correlational	CN	SS4, SS7	SS23
Onboarding plan	Assigns	Project	→	causal	CN	SS4, SS20	SS23, SS28
Buddy program	Bridges	Team	→	correlational	CN	SS5, SS20	SS23, SS28
Buddy program	Facilitates	Methods	→	causal	CN	SS3, SS4	/
Buddy program	Reduces uncertainty	New employee	→	causal	CN	SS4, SS6	SS23
Mentor	Informs	Evaluation	↔	correlational	CN	SS5	SS23, SS24, SS28
Mentor	Provides	Resources	→	correlational	CN	SS5, SS7	SS23, SS37
Mentor	Aligns	Learning objectives	→	correlational	CN	SS5, SS7, SS11	SS23
Successfully integrated	Contributes to	Team	→	correlational	CN	SS1, SS5	SS28
Methods	Supports	Learning objectives	→	correlational	CN	SS4, SS5, SS11	SS23
New employee	Seeks guidance	Mentor	→	correlational	CN	SS5, SS6, SS20	SS23
Learning objective	Defines criteria	Evaluation	→	correlational	CN	SS4, SS7, SS20	SS23

.

Based on the literature sources of WL [SS4], and [SS7], and [SS23] and [SS28] from GL, the entity Manager, through cooperation with Mentor, coordinates roles in onboarding and aligns tasks.

According to the literature sources WL [SS4] and [SS22], [SS23], [SS28], and [SS33] from GL, the Manager and Resource relation describes the practice of granting access to resources in the form of documents, tools, software packages, etc.

Evidence from the literature sources of WL [SS4], [SS7], and [SS17] and [SS22] and [SS23] from GL, the Manager and Learning Objectives relation states that the manager defines learning objectives that are aligned with a specific role and project in the onboarding process, and thereby determines the priority and sequence of skills.

As reported in the literature sources WL [SS4] and [SS24] from GL, the entity Team informs Evaluation—the team performs records and findings that enter the evaluation process of a certain segment.

Referring to the literature sources of WL [SS1], [SS7], and [SS20] and [SS22], [SS26], [SS31], and [SS33] from GL, Evaluation yields Feedback—the evaluation process does not end only with “gathering results” but produces feedback that can be used for further decisions, improvements, corrections, and the like.

According to the literature sources WL [SS1], [SS3], [SS7], [SS16], and [SS20] and [SS22], [SS24], [SS26], [SS31], and [SS33] from GL, Evaluation verifies New skills, i.e., evaluation measures, and confirms the level of adoption of acquired skills.

Evidence from the literature sources WL [SS1], [SS2], [SS7], [SS9], and [SS20] and [SS22], [SS24], [SS26], [SS31], and [SS33] from GL shows that evaluation confirms the achievement of learning objectives, i.e., with a certain type of evaluation, the fulfillment of the learning objectives is checked.

As reported in the literature sources of WL [SS3], [SS5], and [SS7], and [SS24] and [SS28] from GL, Feedback adapts Learning Objectives through the mechanism of adaptive learning.

Referring to the literature sources of WL [SS3] and [SS5], and [SS24] and [SS28] from GL, Feedback refines the form of methods because feedback drives iterative correction of methods.

Based on the literature sources WL [SS4], [SS7], and [SS17] and [SS22], [SS23], [SS28], and [SS33] from GL, the manager can correct expectations and priorities based on feedback.

According to the literature sources of WL [SS5] and [SS20], and [SS23] and [SS24] from GL, the mentor adjusts the intensity and content of training and support based on feedback.

Referring to the literature sources of WL [SS4], [SS7], and [SS20], and [SS23] from GL, the onboarding plan changes based on the progress or based on the obstacles encountered by the new employee.

Evidence from the literature sources of WL [SS4] and [SS7], and [SS23] from GL shows that feedback removes the present ambiguities about the role and expectations of the new employee.

Based on the literature sources of WL [SS4] and [SS20], and [SS23] and [SS28] from GL, the onboarding plan is created based on the initial task assigned to the new employee.

According to the literature sources of WL [SS5] and [SS20], and [SS23] and [SS28] from GL, the buddy program serves as a channel of socialization between the new employee and other employees, and in this way, the team is joined.

The literature sources indicate that in WL [SS3] and [SS4], the buddy program facilitates the application of recommended methods through practical guidance and thus accelerates the clarification of doubts.

According to the literature sources of WL [SS4] and [SS6], and [SS23] from GL, the buddy program at the beginning of the onboarding reduces the uncertainty and accelerates the orientation of the new employee.

Referring to the literature sources of WL [SS5] and [SS23], [SS24], and [SS28] from GL, the mentor directly contributes to the evaluation of the new employee’s progress with his observations and notes.

Based on the literature sources of WL [SS5] and [SS7], and [SS23] and [SS37] from GL, the mentor influences the choice and priority of the resources that will be present in the new employee.

As reported in the literature sources of WL [SS5], [SS7], and [SS11], and [SS23] from GL, the mentor can adjust the learning objectives based on the current situation.

Based on the literature sources of WL [SS1], [SS5], and [SS28] from GL, the involvement of a new employee contributes to the growth of team performance.

From the literature sources of WL [SS4], [SS5], [SS11], and [SS23] from GL, the choice of methods is aligned with the learning objectives, and thus, a certain framework is assigned for its achievement.

Based on the literature sources of WL [SS5], [SS6], and [SS20], and [SS23] from GL, a new employee turns to a mentor for help in understanding certain functionalities and practices, and in this way, the task is solved.

The literature sources indicate that in WL [SS4], [SS7], and [SS20], and [SS23] from GL, the learning objectives determine what is measured and with what it is measured, and in this way, the criteria for knowledge verification are presented.

Figure 4 presents the entities and semantic relations in the form of S-P-O that are validated through WL and GL (MLR). This new model, OnMod, includes relations with rating power SC and CN, where the direction of the arrows represents the validated predicates. The model also explicitly closes the learning loop, and thus, this onboarding model can be seen as an iterative process. Blocks in the figure represent entities, oriented arrows represent relations, and arrow marks represent violated predicates.

5. Discussion

In this section, the results obtained from the theoretical validation of the model will be analyzed through answers to research questions (RQs). This provides an overview of the model that is confirmed by sources from WL and GL, and where new entities and semantic relations appear.

5.1. Answer to RQ1: Which Entities from the Experience-Based Onboarding Model Have Stable Support in White and Gray Literature?

The results from the WL findings confirm the key actors and items that already existed in the model: New Employee as a central unit, Mentor as a person who provides support during onboarding, Manager and Team as an environment that create the conditions for involvement in further work, Methods as a type of task execution, and Resource as a learning support and tools for task realization and conducting Evaluation with predetermined criteria for success.

5.2. Answer to RQ2: Which Relations Between Entities Are Confirmed in the Literature, and What Are Their Natures (Causal Effect, Correlational Effect, Indirect Effect, Conditional Effect)?

On the basis of the second research question, the relations between the entities were identified and confirmed in the literature sources. The analysis shows that these relations differ according to the nature of the effect, which allows a more precise understanding of the mechanisms of the onboarding process in companies.

• Causal effect—when suggestions are given through the review, the task given to the new employee can be adjusted. If such a process is repeated, the probability of such an assignment being accepted increases. In this way, positive feedback will be obtained, the onboarding plan can be corrected in a qualitative way, and in this way, a successful evaluation can be achieved.
• Example: Evaluation–verifies–Successfully Integrated—the evaluation formally confirms the integration of the new employee and directly affects the success of the onboarding process.
• Correlational—resources systematically support the learning and preparation of the task assigned to the new employee.
• Example: Mentor–facilitates–New Employee—the importance of the mentor’s support in order to adapt the new employee is confirmed.
• Indirect effect—based on the evaluation results, certain feedback is formed that leads to the adjustment of learning objectives or the correction of methods. Based on this, there is an increase in the mastery level of new skills.
• Example: Resource–mediate–New Skills—resources indirectly contribute to the acquisition of new skills through the levels of the learning process.
• Conditional effect—based on the literature sources, the Manager–Resources–Methods relationship is contextual. In large companies, the manager must formally approve access to certain resources (software licenses, development tools). Once usage is approved, the new employee can apply the methods provided in their onboarding plan.
• Example: Role–situates–Team–team fit depends on the context of the role assigned to the new employee, as well as the context of the organization.

5.3. Answer to RQ3: (RQ3) Which New Entities and Semantic Relations from Literature Sources Extend the EBOM?

Based on validation, entities were introduced and specified more precisely:

• Feedback—represents a feedback loop that can adjust methods and goals, and update the onboarding plan, thus losing the literature flow of the process.
• Onboarding plan—it is implicitly present in WL through step-by-step guides and formalized procedures, and in this way, it is fully justified to create a model and connect the plan with the task and evaluation in the model.
• Buddy program—although the buddy program is not popular and dominant in the literature, such a function is also described through mentoring.

In addition to the listed entities, new semantic relations were identified through theoretical validation that expand and close the learning cycle in the OnMod model. The introduction of new semantic relations adds depth and clarity to the functioning of the onboarding process. Semantic relations that were identified are as follows:

• Manager–collaborates–Mentor—the joint role of manager and mentor in the onboarding process is emphasized.
• Manager–authorizes–Resources—the formal role of the manager in ensuring access to resources is confirmed.
• Manager–sets–Learning Objectives—the manager sets learning objectives according to the type of project and the role assigned to the new employee.
• Team–informs–Evaluation—team contributes with their observations to the evaluation of the new employee’s progress.
• Evaluation–yields–Feedback—shows that the evaluation generates feedback that leads to further adaptation of the onboarding process.
• Evaluation–verifies–New skills—formally checks acquired skills during the onboarding process.
• Evaluation–verifies attainment of–Learning Objectives—it is checked whether the learning objectives have been achieved.
• Feedback–adjusts–Learning Objectives—learning goals are adapted based on feedback.
• Feedback–refines–Methods—the methodology of work is corrected according to the evaluation results.
• Feedback–informs–Manager—the manager is given guidelines for further organization of the process.
• Feedback–informs–Mentor—the mentor is given guidelines to tailor the training to the new employee.
• Feedback–updates–Onboarding plan—the onboarding plan process is updated.
• Feedback–clarifies–Role—clarifies expectations regarding the employee’s role.
• Onboarding plan–assigns–Project—connects the plan with concrete tasks and the project.
• Buddy program–bridges–Team—represents a channel of socialization between the new employee and the team.
• Buddy program–facilitates–Methods—makes it easier to understand the methods recommended through the tips.
• Buddy program–reduces uncertainty–New Employee—counseling reduces uncertainty and thereby contributes to faster orientation.
• Mentor–informs–Evaluation—the mentor contributes his observations to the evaluation process.
• Mentor–provides–Resources—the mentor delivers resources that are relevant to learning, work, and advancement.
• Mentor–aligns–Learning Objectives—classifies and helps align learning objectives with assignments and projects.
• Successfully integrated–contributes to–Team—confirmation that the successfully integrated new employee contributes to the team.
• Methods–support–Learning Objectives—provided methods help the new employee to achieve the learning objectives.
• New employee–seeks guidance–Mentor—the new employee actively seeks advice and help from the mentor.
• Learning Objective–defines criteria–Evaluation—evaluation criteria are set based on the learning objectives.

5.4. Theoretical Contribution

The OnMod model advances the existing theory of the onboarding process by introducing an adaptive OnMod model that contains a closed loop between evaluation, feedback, and adapted goals and methods. In relation to linear flows, this model is presented as an iterative cycle of planning, execution, and implementation of the evaluation. In this way, a modern approach to learning and socialization of employees in the IT sector is achieved. Becker [94] emphasizes that the process of adaptive mechanisms is very important in the development of competences, which theoretically indicates the need for models that allow correction of learning goals and methods through a certain number of iterations.

Additionally, the OnMod model contributes to the theory by expanding the framework with new entities, which emphasizes the persistence of support and formalization of the learning process. The authors Pinco and Crisan [63], in their review of the onboarding literature, state that the first impression is very important when a new employee is included in the team, as is the completion of formal procedures when starting the onboarding process.

The special contribution of this research work is based on a methodological approach, based on autoethnography and theoretical validation through the MLR. In relation to most onboarding models that were created through theory, and only then through practice, the process in this work is reversed. The first model was developed on the basis of personal autoethnographic experience, reflection, and practical application [25], which is based on the real process of onboarding. Such a model was then subjected to theoretical validation using WL and GL. Such a procedure confirms that autoethnography can be a starting point for creating a theoretical model in the IT and SE sector, while subsequent validation can only strengthen such a model.

5.5. Practical Contribution

When it comes to the practical contribution of this paper, it is reflected in the possibility of applying this developed model in the context of the organization. The OnMod model can be used by people employed in the HR sector, managers, and mentors when designing and implementing the onboarding process. The entity Onboarding Plan provides a structured form for defining the initial tasks and learning goals, while the entity Feedback introduces a form of feedback where this process is shaped in the direction of adaptability. As for the entity Buddy Program, it expresses the importance of socialization and a form of informal support for a new employee, which is very important when the onboarding process is remote. Frogeli et al. [29] showed that formal and structured programs can accelerate socialization within the company and improve employees’ sense of self-confidence. Dickson and Isaiah [95] showed that the presence of a mentor and social support during the onboarding process can significantly increase the engagement of a new employee.

In IT-dominated environments, the OnMod model can provide guidance on how to connect resources with work methods, how to define clear learning goals, and thus increase the likelihood of mastering new knowledge, skills, and successful evaluation at the end of the process. Ju et al. [61] pointed out in their study of Microsoft Teams that initial tasks and the presence of mentor support contributed to the development of self-confidence, while Buchan et al. [8] successfully proved in the context of agile development that effective onboarding must rely on combining formal procedures and informal social techniques.

This model can also be applied in other sectors where it is important to quickly introduce new employees to the company, such as pharmaceuticals, education, or mechanical engineering. In this way, the model can serve as a practical guide for mentors and managers in reducing the integration time of a new employee, increasing engagement, and better organization, which would contribute to employee retention in the long term. Such a benefit would not only accrue for the organization but also to the employees during their onboarding process.

5.6. Comparison with Previous Studies

Compared to earlier research on onboarding that mostly relies on peer-reviewed sources, this review integrates both WL and GL to capture practices from real industrial environments and thereby extend the external validity of the findings [10]. Unlike earlier models that describe entities at a higher level of abstraction [8,33], this paper introduces semantic annotations of relations (S-P-O with typed connections), which are supported by confirmation ratings (SC or CN). In this way, a deeper and more precise articulation is obtained between the actions of mentors, managers, resources, learning goals, evaluation, and team dynamics during the onboarding process. Finally, the results confirm key findings from the literature while refining them into an OnMod model that offers clearer guidance for practice.

6. Limitations

This research paper relies on an MLR, i.e., the use of WL and GL studies, which in themselves contain methodological limitations. An additional limitation refers to the fact that the research itself did not assess the quality of the selected studies according to the recommendations [27], which may affect the quality of the research, whether the selected studies are methodologically clear, whether the results are clearly presented, and whether they are grounded in data. Frogelli et al. [29] indicate that most formal onboarding processes have been investigated under the conditions of a high risk of bias. Mosquera et al. [30] confirm that onboarding has a very positive effect on employee retention. However, the authors themselves indicated that other possible mechanisms that would additionally explain the connection within the team or in the direction of professional competence were not investigated. Although the OnMod model presents mechanisms such as a sense of meaning, team bonding, and professional competence, the model is still not empirically tested, so these factors are only implicitly present.

7. Conclusions

This review presents the development and validation of the onboarding model. The starting point was the EBOM, which was created through autoethnographic research and reflection of the first author. Through systematic validation using the MLR, the model was upgraded to an OnMod version of the model that includes new entities (see Table 3) and new semantic relations (see Table 4). Based on this method, the theoretical confirmation of the EBOM was provided, and the role of the feedback mechanism as a kind of regulator of the onboarding process, which has the possibility of fine-grained control of the onboarding process through the adjustment of learning goals, methods, and resources, was especially emphasized. In this way, the linearity of the process is completely interrupted, and adaptability and sustainable improvement are enabled.

In addition to the theoretical contribution, the application of the model has a practical value. The OnMod model can be used daily to improve the operational tasks of mentors and managers through the structured management of the onboarding process, iterative evaluation and feedback, but also can provide the basis for development and improvement of the onboarding process in the IT and SE sectors, as well as in other areas where it is important to carry out the onboarding process quickly, such as pharmacy, education or engineering.

Further research will be directed towards empirical testing of the OnMod model in companies and comparison with existing practices. An additional direction is the development of a software tool that would support the implementation of this model through the visualization of entities and relations, the automatic monitoring of the evaluation of a new employee, and the integration of feedback. In this way, the practical application of the model would be significantly increased, and the role of a tool for mentors and managers during the onboarding process would be strengthened.