Knowledge Management Research & Practice

, Volume 15, Issue 3, pp 336–345 | Cite as

Explicit and tacit knowledge conversion effects, in software engineering undergraduate students

  • Maria Angelica Astorga-Vargas
  • Brenda L. Flores-Rios
  • Guillermo Licea-Sandoval
  • Felix F. Gonzalez-Navarro
Open Access
Original Article
  • 737 Downloads

Abstract

This study evaluates the effect of conversion between tacit and explicit knowledge, and its influence on Software engineering and Software Process Improvement in the context of a small school software company in which undergraduate students participate as personnel. A survey measurement instrument was applied to the last three generations of students. The effect was measured from an interaction of the four modes of the SECI model knowledge conversion using regression analysis associated with four hypotheses study. The findings show that students are able to generate tacit and explicit knowledge in a similar way to software organizations. This study is considered a contribution of both academia and software industry that encourages this type of experiences in undergraduate students and prepares them as intellectual capital with an organizational culture that shares knowledge.

Keywords

Tacit knowledge Explicit knowledge Software process improvement Software engineering Undergraduate students 

Introduction

A mechanism through which the information is converted into knowledge is the learning process; therefore, higher education institutions play an important role in the education of undergraduate students (Eid and Nuhu 2011). One of the strategies to build student’s competitivity as a professional, is to create environments similar to those in the industry during their training, such as university-based technology companies (Smilor et al. 1990). This type of school model Software Company was established in a computer systems undergraduate program at Autonomous University of Baja California’s (UABC) Faculty of Engineering, with the purpose to have students develop in an environment similar to an organization dedicated to the software development that solves real problems. This company is called Scholar because students associate more than one learning course during their participation (Astorga-Vargas et al. 2015). One of the challenges of the software organization is to identify content, locate, and use tacit and explicit knowledge, and maintain the level of competence in relation to their intellectual capital (Rus and Lindvall 2002). Knowledge management (KM) has been considered vital to improve professionalism in the areas or departments of software engineering (SE) (Edwards 2003), used as a strategy that provides a simple way to learn in depth about organizations of software (Humayoun and Qazi 2015). It enables a formal and informal exchange of knowledge among software developers (Rus and Lindvall 2002), contributing greatly to tacit and explicit knowledge-building practices that support the software process improvement (SPI) (Wan et al. 2011). It has been identified in previous work that the study population had been primarily staff of organizations of the software industry (Humayoun and Qazi 2015; Wan et al. 2011; Chikh 2011). In none of these works were only students involved as personnel of a company. For this reason, this study is considered a contribution with new perspectives of actor’s study.

Considering the advantages of the KM in SE and SPI projects, KM measurement techniques were implemented based on the work of (Flores-Ríos 2016; Pérez-Soltero 2007; Wan et al. 2011) taking into account that this type of companies also generate, store, and use knowledge. The objectives of this research are: (1) Measure the effect of conversion tacit to explicit knowledge and vice versa from the four processes of knowledge conversion during the education of undergraduate students, and (2) Characterize the findings to establish improvement actions in the SPI project; engaging students in a KM program associated to SPI project which is expected to connect the gap between academic and industry projects by introducing undergraduate students in a dynamic organizational learning and culture to share knowledge earlier (Rus et al. 2001).

This document is structured as follows: initially a review of the work related to the objectives is presented. Next, the research method is described. Subsequently, the findings are presented. Then findings and implications are discussed. Finally, the conclusions are presented.

Literature review

Knowledge creation

Knowledge is defined as the information that the individual possesses in its mind (Alavi and Leidner  1999). On the other hand, knowledge is the interpretation of the information in a context; a result of the perception, learning, and reason (Shbair et al. 2016). Michael Polanyi (1962, 1967) classified knowledge as explicit and tacit for the first time. Subsequently, this classification was taken up focusing on the conversion between both types of knowledge. The difference between both types of knowledge is that explicit knowledge is objective and rational, that is encoded and can be stored in various physical and electronic formats. While tacit knowledge is the individual’s own experience, reflection, interiorization, or talents, which is difficult to express (Nonaka 1994; Hau and Evangelista 2007).

According to the theory of knowledge creation proposed by (Nonaka 1994; Nonaka and Takeuchi  1995), knowledge evolves ontologically in individuals, groups, and organizations from a tacit to an explicit mode and vice versa. These changes of modes are detonated epistemologically through the cycle of conversion or knowledge spiral between the processes of knowledge Socialization, Exteriorization, Combination, and Interiorization, known as the SECI model, on which the knowledge is created, considered critical for sustainability and success of any organization.

The process of knowledge creation begins with the process of tacit knowledge, Socialization, facilitation of experience, and the capacity among individuals where they reside and are needed. It happens regularly through meeting records, that modeling the way of work by repeating a task leads others to learn by example. This propitiates the Externalization in which all activities are grouped and aimed at capturing, organizing, structuring, representing, coding, etc. knowledge in order to facilitate the management by changing the mode of knowledge tacit to explicit. A formalization of knowledge is the standardization of documents through templates, the definition of knowledge maps as the experience and skills of employees (Flores-Ríos et al. 2014). When different pieces of existing explicit knowledge are merged to create a new explicit knowledge, the process of Combination of explicit to new explicit knowledge happens which is stored using repositories that facilitate their access. The fourth process is Internalization which is carried out by putting into practice what has been learned from explicit knowledge.

It is important to emphasize that the interaction between different types of knowledge is not a linear and sequential process, but also exponential and dynamic, known as knowledge spiral (Nonaka and Takeuchi 1995; Gil and Carrillo 2014). The difference between the modes of knowledge is determined by the 4 knowledge flows of SECI (Table 1). When the knowledge reaches the flow of Interiorization, tacit knowledge is enriched and the knowledge spiral begins again, but this time to a higher level which expresses the creation of new concepts based on the continuous dialogue between tacit and explicit knowledge (Nonaka 1994).
Table 1

Conversion of the four modes of knowledge based on (Nonaka 1994)

Modes of knowledge

Knowledge flows between processes

Spiral increment

From

To

Tacit–tacit

Socialization

Socialization

Open image in new window

Tacit–explicit

Socialization

Externalization

Explicit–explicit

Externalization

Combination

Explicit–tacit

Combination

Interiorization

Knowledge management in support of software organizations

Alavi and Leidner (1999) have defined KM as “systemic and organizationally specified process for acquiring, organizing and communicating both tacit and explicit knowledge of employees so that other employees may make use of it to be more effective and productive in their work.” A software organization is an entity that uses diverse and constantly increasing knowledge (Dingsøyr et al. 2009; Serna and Serna 2014). Likewise, a software process is an intensive learning organizational process knowledge and it needs to be supported by KM Wan (2009).

The application of KM in the SE field is seen as an opportunity to create a common language understood among software developers, so that they can interact, negotiate, and share knowledge and experiences (Aurum et al. 2003), apply what has been learned to improve the quality of software products by reducing defects (Dingsøyr and Conradi 2002) and costs.

Since software is not only a code but the sum of all the documents that accompany it, it is possible to operate and maintain it (Sommerville 2001). Software quality is defined as “the conformance to explicitly stated functional and performance requirements, explicitly documented development standards and implicit characteristics that are expected of all professionally developed software” (Pressman 1997). For software development, there are several models of software reference processes such as CMMI-DEV, ISO/IEC 12007, NMX-I-059-NYCE-2011 (MoProSoft), and specific models such as RUP, SCRUM, PSP, TSP, among others, with the purpose of introducing good practices that guarantee the quality of the software. These models define workflows that allow the project team to be guided in each of the activities and generating work products necessary for a success development and maintenance. In addition, these models propitiate knowledge processes of Socialization, Externalization, Combination, and Interiorization during the conversion of tacit and explicit knowledge present in the software development lifecycle (Wan et al. 2011; Chikh 2011).

Application of knowledge creation in software engineering

The applying of the SECI model for knowledge creation within SE and SPI projects is a way to increase and improve tacit and explicit knowledge among all the roles of the software project team: analysts, designers, programmers, testers, users, project managers, business experts, and other stakeholders (Chikh 2011). Explicit knowledge is easier to obtain and understand, but not tacit knowledge, which is associated with one of the main risks in software engineering, and the lack of understanding or omission of requirements, especially of those that have users in their minds and are difficult to transmit (Boehm 1991; Kwak and Stoddard 2004). Next, each knowledge process of the SECI model is described in the SE application.

Socialization

Encourages the exchange of experiences to learn from each other. It is of great importance the interaction that project team members perform with customers and users to promote socialization in which tacit knowledge of individuals and organization is accessible to others through previous systems, work sessions, and demonstrations in which they can observe how they execute the activities of their processes allowing them to obtain the requirements of the customer. Organizational members of the work team also require socialization to have a common understanding of the characteristics and requirements that the software must provide as well as their commitment to the Project. They are responsible for establishing the activities that each member should do (Chikh 2011) according to the software process models.

Exteriorization

From the exchange of knowledge that occurs in socialization, members of the project team are able to externalize the new explicit knowledge through meeting records, templates, etc., (Jensen and Szulanski 2007). This allows formalization to document the project’s implicit objectives, capture customer’s requirements, to relate the tacit requirements with specific project characteristics, etc., in business models, analysis and design, code, testing, manuals, etc., according to the software process model and specific methodologies for the software development and maintenance.

Combination

During the externalization, explicit knowledge versions are obtained in different work products that are used as inputs and outputs between the flows associated with the development life cycle that when combined becomes a new explicit knowledge. Software development processes are iterative and incremental, so the releases of software products are based on the previous baseline. These baselines are stored in the repositories of the knowledge base according to the nature of the documents. It is important to keep the traceability of the requirement until it becomes a software component so that every time changes arise and new versions or combinations are generated, the roles must know what to store and how to store it.

Interiorization

The explicit knowledge generated is reflected and discussed among the project team members, the customer, and users facilitating the internalization in which it is easier to understand and practice what has already been learned from the different roles involved, workflows, or documents evolving knowledge to a higher level. The evolution of tacit knowledge allows both users and project team members to propose changes to the requirements or to add new requirements in the early stages of the project, managing to satisfy the real needs of the customer and users (Chikh 2011). The training of the team members based on the explicit knowledge available in the knowledge base is another mechanism that motivates the internalization and increases new explicit knowledge.

Research method and hypotheses

Data collection

The population study is represented by undergraduate Computer Systems’ students in UABC that participate as personnel in a school company oriented on the software industry, in which they assume the responsibility of some of the required roles in this type of organizations, as analyst, architecture designers, programmers, testers, among others. Students participate in the company for a period of one year, later the team is rotated with the integration of new students (Astorga-Vargas et al. 2015). When students begin their participation in the company, they have little experience in the performance of the roles, so the training of their role is given by the most experienced students who leave their vacancy and by the professors associated with the project.

Research limitations

The population is limited to a small enterprise with an average of 12–20 students per generation. To have a representative population, the last three generations (2014, 2015, and 2016) were chosen in order to achieve greater feedback on the degree of perception knowledge managed by students. A total of 22 surveys were accounted (Table 2).
Table 2

Total students surveyed

Generation

Frequency

Percentage

1

17

38.6

2

15

34.1

3

12

27.3

Total

44

100.0

Hypotheses

Based on the proposed knowledge creation in organizations, four hypotheses were established (Table 3) to measure knowledge conversion effects that occur in undergraduate students. Knowledge flow of origin significantly affects the target knowledge flow causing a change in knowledge mode in a spiral between tacit and explicit knowledge.
Table 3

Research hypotheses

Methods and measures

Criteria measurement degree to evaluate the effect tacit interaction and explicit knowledge was taken from the declarative model of KM proposed by Flores-Ríos (2016). This model was selected to be oriented to the SPI project; in addition, each process of the KM life cycle is associated with a knowledge measurement component adapted from previous studies (Pérez-Soltero 2007; Wan et al. 2011). In order to establish correspondence with Socialization, Exteriorization, Combination, and Interiorization knowledge process, only Transfer, Exteriorize, Store, and Internalize processes were taken into account.

An instrument survey was developed with four constructs: transfer, externalize, store, and internalize with a total of 17 items. To maintain reliability and validity, a Likert scale was used, specifying that 5-Strongly Agree, 4-Agree, 3-Neutral, 2-Disagree, and 1-Strongly Disagree. Also, semi-structured interviews were conducted with the students to expand and corroborate the answers offered.

Reliability and validity

To estimate the internal consistency of the Likert scale applied in items, a reliability analysis measurement was done using the Cronbach α method. According to Huh, DeLorme, and Reid (2006), the value of reliability in exploratory research must be equal to or greater than 0.6. The survey obtained a value of α = 0.879. Subsequently, the consistency was calculated by the construct. To improve exteriorization construct, an item was dropped. The value α of the constructs between 0.600 and 0.702 was considered acceptable when it is near 0.6 and above this (Table 4). The final value of the instrument was α = 0.88.
Table 4

Reliability constructs

Construct

No. items

α

Exteriorize

4

0.600

Store

4

0.727

Transfer

4

0.702

Internalize

3

0.702

Total items

15

0.880

To validate the survey, an analysis of factor was applied by the construct. The principal component extraction method was executed with a varimax rotation. To verify the normalization of the factors, the Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test of Sphericity were used. The KMO values ranged 0.548–0.656 (p < 0.001). Hence the sample was adequate to conduct factor analysis. The extraction of factors by construct was determined by the number of items with a value greater than 1.

Table 5 shows the transfer construct which was extracted in a single factor called Socialization. It integrates items S1, S2, S3, and S4. The factor explained 53.392% of the total variance; KMO = 0.627 and p < 0.001.
Table 5

Factor Socialization

Factor

Items

Loading factor

Socialization

S3 Roles know that their knowledge can be shared

0.775

S2 Roles are motivated to share knowledge by building trust, granting incentives, time, and available resources

0.769

S1 The knowledge that I possess is really accessible to others

0.696

S4 Existing explicit knowledge is distributed in electronic forms (emails, intranet, information systems, etc.)

0.677

 

Eigenvalue

2.136

 

Accumulated variance explained

53.392

The Exteriorize construct was extracted in 2 factors (Table 6). Factor 1 integrates items E1 and E3 called Exteriorization tacit requirements. Factor 2 integrates items E2 and E5 called Exteriorization explicit requirements. The two factors explained 75.644% of the total variance; KMO = 0.551 and p < 0.001.
Table 6

Factor Exteriorization

Factor

Items

Factor loading

Exteriorization tacit requirements

E1 There is a formalization to document the implicit objectives of the project

0.91

E3 The customer’s tacit requirements are related to specific project characteristics (subjective requirements to objective requirements)

0.842

Exteriorization explicit requirements

E2 The requirements of the customer are formally and systematically captured

 

0.935

E4 The work is verified and validated reciprocally

 

0.558

Eigenvalue

1.958 

1.068 

Accumulated variance explained

45.566

75.644

The Storage construct was extracted into 2 factors (Table 7). Factor 1 integrates items C1, C2, C3, and C4 called Combination. The two factors explained 55.492% of the total variance; KMO = 0.622 and p < 0.001.
Table 7

Factor Combination

Factor

Items

Factor loading

Combination

C1 The roles know how to store the knowledge and their experience that they own and generate

0.807

C2 The roles know or agree that knowledge must be stored

0.795

C3 Roles are motivated to capture knowledge assets (experiences, lessons learned, best practices, etc.) and have access to these

0.7

C4 Customer evaluations, comments, and complaints are recorded

0.669

Eigenvalue

2.220

Accumulated variance explained

55.492

The interiorize construct was extracted in a single factor called Interiorization. It integrates items I1, I2, and I3 (Table 8). The factor explained 64.034% of the total variance; KMO = 0.644 and p < 0.001.
Table 8

Factor Interiorization

Factor

Items

Loading factor

Interiorization

I2 Work products or procedures are reviewed to generate knowledge or reflections by the roles

0.855

I3 Tools are used that allow the innovation and generation of knowledge

0.813

I1 Role is motivated to take training courses

0.728

 

Eigenvalue

1.921

Accumulated variance explained

64.034

Research findings

In order to determine the effect of tacit and explicit knowledge conversion and vice versa, associated with the four hypotheses (Table 3), the stepwise regression analysis method was used. The significance level is p* < 0.05, p** < 0.01. For the hypotheses H1 the Socialization (S) process was considered as the independent variable on the two factors of Exteriorization (E), for that reason two regression models were executed. The whole Socialization factor (Table 9) shows that it is significant and positively correlated with the Exteriorization Tacit Requirement (p = 0.024) and also with the Exteriorization Explicit Requirement (p = 0.011). The effect of regression on Model 1 S-E (Table 10) taking the whole Socialization factor is significant with the Exteriorization tacit requirement with F = 5.497. Adjusted R2 is 0.951 indicating that a 9.51% of the total variance of tacit requirement generated by the students is explained by the Socialization. Likewise, it is significant with the Exteriorization Explicit Requirement in Model 2 S-E (Table 10) with F = 7.008 and adjusted R2 = 0.123 indicating that a 12.3% of a total variance generated by students is explained by Socialization. In the Model SE (Table 10), the analysis shows which items of the Socialization are most used in the Exteriorization tacit requirement (Table 10). The item that most contributes is S1 The knowledge that I possess is really accessible to others (p = 0.017) and the one that contributes least is S4 Existing explicit knowledge is distributed in electronic forms (emails, intranet, information systems, etc.) (p = 0.860). In the same way, in Model 3 S-E (Table 10) the item that contributes the most in the Exteriorization Explicit Requirement (Table 10) is S1 (p = 0.003) and the least contributing is S3 Roles know that their knowledge can be shared (p = 0.560).
Table 9

Correlation of Socialization and Exteriorization processes

 

Socialization

S1

S2

S3

S4

Exteriorization Tacit Requirement (ETR)

0.340*

0.357*

0.335*

0.123

0.202

Sig.

0.024

0.017

0.026

0.425

0.189

Exteriorization Explicit Requirement (EER)

0.378*

0.439**

0.263

0.045

0.373*

Sig.

0.011

0.003

0.084

0.773

0.013

Table 10

Effect of Socialization on Exteriorization

Model 1 S-E: Tacit–explicit (Exteriorization Tacit Requirement) = β0 + β1S

Model

β

t

Sig.

R2

R2 adjusted

F

Sig.

Socialization

0.340

2.345

0.024

0.116

0.095

5.497

0.024

Model 2 S-E: Tacit–explicit (Exteriorization Tacit Requirement) = β0 + β1S1 + β2 S2 + β3S3 + β4S4

Constant

−2.229

−2.448

0.019

0.128

0.107

6.144

0.017

S1

0.357

2.479

0.017

S2

0.253

1.725

0.092

S3

0.029

0.192

0.849

S4

0.030

0.178

0.860

Model 3 S-E: Tacit–explicit (Exteriorization Explicit Requirement) = β0 + β1S

Socialization

0.378

2.647

0.011

0.143

0.123

7.008

0.011

Model 4 S-E: Tacit–explicit (Exteriorization Explicit Requirement) = β0 + β1S1 + β2 S2 + β3S3 + β4S4

Constant

−2.741

−3.129

0.003

0.193

0.174

10.035

0.003

S1

0.439

3.168

0.003

S2

0.150

1.039

0.305

S3

−0.080

−0.549

0.586

S4

0.204

1.285

0.206

Variables predictors: (Constant), Socialization, S1, S2, S3, S4

Variable dependent: Exteriorization tacit requirement, Exteriorization explicit requirement

For hypotheses H2 the two factors of Exteriorization (E) were considered as independent variables on Combination (C). The whole factor of Exteriorization Tacit Requirement (ETR) (Table 11) shows that it is positively correlated (p = 0.043) with Combination, but its items are not significant. The correlation with Exteriorization Explicit Requirement (EER) is significant (p = 0.000) and positive, as are its items E2 and E4 (p < 0.01). The effect of regression Model 1 E-C (Table 12) shows that both factors of Exteriorization are significant with F = 4.837 and Adjusted R2 = 0.388 indicating that 38.8% of the Combination that the students perform to generate explicit to new explicit knowledge is based on the process of Exteriorization of which 33.2% is by explicit requirement and 38.8% by tacit requirement. The regression by items in Model 2 E-C (Table 12) corresponding to the Exteriorization Explicit Requirement shows that the items that contribute most are E2 The requirements of the customer are formally and systematically captured (p = 0.000) and E4 It is verified and validated the work reciprocally (p = 0.040), while the least contributing item is E1 There is a formalization to document the implicit objectives of project.
Table 11

Correlation of Exteriorization and Combination processes

 

Exteriorization Tacit Requirement

E1

E3

Exteriorization Explicit Requirement

E2

E4

Combination

0.262

0.219

0.280

0.590**

0.484**

0.531**

0.043

0.153

0.065

0.000

0.001

0.000

Table 12

Effect of Exteriorization on Combination

Model 1 E-C: Explicit–explicit = β0 + β1ERE + β2 ERT

Model

β

t

Sig.

R2

R2 adjusted

F

Sig.

Exteriorization Explicit Requirement (EER)

0.590

4.944

0.000

0.348

0.332

22.396

0.000

Exteriorization Tacit Requirement (ETR)

0.262

2.199

0.034

0.417

0.388

4.837

0.034

Model 2 E-C: explicit–explicit = β0 + β1E1 + β2E2 + β3E3 + β4E4

Constant

−5.454

−5.134

0.000

0.405

0.376

8.491

0.006

E4

0.613

3.434

0.001

E2

0.620

2.914

0.006

E1

0.100

0.736

0.466

E3

0.110

0.842

0.405

Variables predictors: (Constant), Exteriorization explicit requirement, Exteriorization tacit requirement, E4, E2, E1, E3

Variable dependent: Combination

For the hypotheses H3, the Combination process (C) was considered as the independent variable on Interiorization. The whole factor of Combination is significantly (p = 0.003) and positively correlated with Interiorization as its item C1. Items C3 and C4 are significant (Table 13). The effect of regression on Model 1 C-1 (Table 14) is significant with F = 9.921 and Adjusted R2 = 0.172, indicating that 17.2% of Combination that the students do impacts the Interiorization to generate tacit knowledge. The regression in Model 2 C-I of Combination by items (Table 14) shows that the item that contributes most is C1 The roles know how to store the knowledge and their experience that they own and generate is (p = 0.004). The item that contributes least is the item C2 The roles know or agree that knowledge must be stored (p = 0.980).
Table 13

Correlation of Combination and Interiorization

 

Combination

C1

C2

C3

C4

Interiorization

0.437**

0.424**

0.221

0.332*

0.311*

0.003

0.004

0.149

0.028

0.040

Table 14

Effect of Combination on Interiorization processes

Model 1 C-I: Explicit–tacit = β0 + β1C

Model

β

t

Sig.

R2

R2 adjusted

F

Sig.

Combination

0.437

3.150

0.003

0.191

0.172

9. 921

0.003

Model 2 C-I: Explicit–tacit = β0 + β1C1 + β2 C2 + β3C3 + β4C4

Constant

−3.018

−3.002

0.005

0.179

0.160

9.184

0.004

C1

0.674

3.031

0.004

C2

−0.004

−0.025

0.980

C3

0.158

0.971

0.337

C4

0.204

1.410

0.166

Variables predictors: (Constant), Combination, C1, C2, C3, C4

Variable dependent: Interiorization

For the hypotheses, H4 the Interiorization (I) was considered as the independent variable on Socialization (S). In the whole factor of Interiorization as in its items, it is correlated with the Socialization. Table 15 shows that the correlation between Interiorization and Socialization is significant (p = 0.000) and positive. The effect of regression on Model 1 I-S (Table 16) is significant with F = 16.368. Adjusted R2 is 0.263 indicating that 26.3% of student’s interiorization impacted on the conversion of tacit to tacit knowledge in Socialization. Model 2 I-S shows the effect of Interiorization on Socialization by items. The item that most contributes is I1 Role is motived to take training courses is significant (p = 0.000) and the item that contributes the least is item S2 Work products or procedures are reviewed to generate knowledge or reflections by part of the roles (p = 0.262).
Table 15

Correlation of Interiorization and Socialization processes

 

Interiorization

I1

I2

I3

Socialization

0.530**

0.572**

0.382*

0.337*

 

0.000

0.000

0.010

0.025

Table 16

Effect of Interiorization on Socialization

Model 1 I-S: Tacit–tacit = β0 + β1S

Model

β

t

Sig.

R2

R2 adjusted

F

Sig.

Interiorization

0.530

4.046

0.000

0.280

0.263

16.368

0.000

Model 2 I-S: Tacit–tacit = β0 + β1I1 + β2 I2 + β3I3

Constant

−3.348

−4.455

0.000

0.327

0.311

20.417

0.000

I1

0.779

4.519

0.000

I2

0.152

1.122

0.268

I3

0.160

1.137

0.262

Variables predictors: (Constant), Interiorization, I1, I2, I3

Variable dependent: Socialization

Discussions and implications

Undergraduate students’ education in SE, as well as other professions, is not only based on theory but also on practical insight into real environments learning mainly from the tacit knowledge of expert´s area (Göksel and Aydıntan 2016) or in environments similar as in the context of school companies (Smilor et al. 1990). The findings of this study in a school company context have shown that the conversion of tacit knowledge to explicit (H1), explicit knowledge to new explicit knowledge (H2), explicit knowledge to tacit (H3), and tacit knowledge to new tacit knowledge (H4) has a positive effect on each of the conversions of knowledge in students. Similarly, Wan et al. (2011) analyzed the interaction of tacit and explicit knowledge in SPI projects under the interaction of Socialization and Exteriorization, and Combination and Interiorization to generate explicit and tacit knowledge, respectively. On the other hand, the findings in Chikh (2011) describe the application of the SECI model within SE processes.

H1

in Model 2 S-E and Model 3 S-E (Table 10), it is shown that the item that contributes most is S1 The knowledge I have is really accessible to others, explaining 10.7%. This finding is comparable to the item Communication among project team members in Wan et al. (2011) and was considered one of the most success factors to SPI projects. One statement in student’s responses is ‘Knowledge is transmitted verbally or by providing an example so that others can acquire knowledge’. 

H2

in Model 2 E-C (Table 12), the significant items were E5 and E2. E5 The work is verified and validated reciprocally with a 24.4%. The contribution of this item allows a combination of explicit knowledge to new explicit knowledge based on changes made to previous versions, using verification and validation reports (Chikh 2011). One response from the programmers was ‘The framework allows us to test our work and have other people review us as well’. The item E2 The requirements of the customer are formally and systematically captured explains a 10.5%. One of the responses was ‘Customer request information is documented in user stories’. This item was also in Wan et al. (2011) only with the difference that its effect was measured in the generation of tacit knowledge within the factor requirements documentation.

H3

in Model 2 C-I (Table 13), the item C1 The roles know how to store the knowledge and their experience they possess shows a 16% effect on Interiorization to generate tacit knowledge from explicit knowledge. This finding can be compared with the integrating documents in Wan et al. (2011), having been one of the factors of greater contribution.

H4

in Model 2 I-S (Table 16), the item that most contributes is I1 Role is motivated to take training courses with a 31% effect on Socialization. In (Wan et al. 2011), this item was analyzed together with the combination to generate new tacit knowledge but was only statistically significant without contributing to the regression to explain its effect on knowledge generation. With respect to this item, one of the students answered: ‘all the personnel was trained with scrum videos, code and research topics for the best learning, as well as investigation of tools for better traceability’. 

In respect to the items that were not significant, the study reveals that there is the need to establish a plan of action to continue improving the results according to the findings found that allow students to achieve the practices with a greater effect on the conversion of the four modes of knowledge and impact on SPI projects.

Conclusions

The findings of this study allow to conclude that in a software school company the undergraduate students, who are currently in training and their experience is not yet comparable to professionals working in software organizations, can create knowledge from the interaction of knowledge flows of SECI model with a positive effect, that allows them to develop good practices associated to the KM as part of the software development lifecycle, which are key to the success of the SE and SPI initiatives. It emphasizes the value of interaction between knowledge creation processes to generate a knowledge conversion in which project team members must socialize with users to understand their needs by converting tacit knowledge into explicit knowledge. This allows the externalization of the requirements to an acceptance internalized by all stakeholders and project team members. This instrument is planned to be applied in software organizations of the locality where the graduates are working to have a comparison of their progress in their professional performance.

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Copyright information

© The OR Society 2017

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Maria Angelica Astorga-Vargas
    • 1
  • Brenda L. Flores-Rios
    • 2
  • Guillermo Licea-Sandoval
    • 3
  • Felix F. Gonzalez-Navarro
    • 2
  1. 1.Facultad de IngenieríaUniversidad Autónoma de Baja CaliforniaMexicaliMexico
  2. 2.Instituto de IngenieríaUniversidad Autónoma de Baja CaliforniaMexicaliMexico
  3. 3.Facultad de Ciencias Químicas e IngenieríaUniversidad Autónoma de Baja CaliforniaTijuanaMexico

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