Linking Knowledge Creating Capabilities, IT Business Value and

Digital Business Value: An Exploratory Study in Japanese SMEs

Christian Riera

and Junichi Iijima

Department of Industrial Engineering and Management, Tokyo Institute of Technology,

Ookayama 2-12-1-W9-66, Meguro, Tokyo, Japan

Department of Industrial Engineering and Economics, Tokyo Institute of Technology,

Ookayama 2-12-1-W9-66, Meguro, Tokyo, Japan

Keywords: Knowledge Creating Capabilities, Balanced SECI, Digital Business Value, IT Business Value.

Abstract: Aiming to address the increasing focus on digital technologies and the continuous concern of Knowledge

Management (KM) performance, this study explores the relationship between 'Knowledge Creating

Capabilities', 'IT business value' and 'Digital business value'. The latter two concepts are re-defined as the

achievement of business objectives by the use of IT or digital technologies in a balanced scorecard approach.

The concepts of 'Knowledge Creating Capabilities' and 'Balanced SECI' are leveraged. Balanced SECI

(Riera, Senoo and Iijima, 2009) refers to the balance of the four knowledge creation processes from Nonaka

and Takeuchi’s SECI model (1995). This framework is applied to Japanese small and medium enterprises.

A positive relationship between the achievement of business objectives by IT and the achievement using

digital technologies was verified. On the other hand, although a relationship of 'Balanced SECI' with 'IT

Business Value' or 'Digital Business Value' was not statistically significant; the observations showed that

higher levels of 'Balanced SECI' were negatively related to the achievement of Financial, Customer and

Business Processes types of business objectives and; positively related to Learning & Growth. Analysis

from each SECI process confirmed such behaviour.

1 INTRODUCTION

The increasing focus on Digitalization and digital

technologies, and in particular on how to gain a

competitive advantage is currently being actively

explored (Ross et al., 2016). These efforts aim to

facilitate the companies’ journeys onto Digital

Transformation. As mentioned by Ross et al. (2016),

digital technologies like the ones in SMACIT (social,

mobile, analytics, cloud and Internet of Things) are

currently available in the marketplace. Therefore

replication of the use of such technologies may not

sustain competitive advantage (Carr cited in Ross et

al.; Piccoli et al. cited in Ross et al., 2016). Also the

same study identified key elements that the

established players use to leverage the digital

technologies and integrate with the firm’s

capabilities and components such as digital strategy,

operational backbone and digital services backbone

become the key to successfully leverage the

opportunities of digital technologies (Ross et al.,

2016). This study also considers digital technologies

as part of the Digital Transformation but the real

transformation relies on the interaction with the

capabilities that already exist in the firm.

As classified by Chen and Chen (2006), in the

early years the evaluation of KM was approached

from perspectives like: qualitative, quantitative,

internal/external performance, and project/

organizational-oriented; while recently the research

mainly used the quantitative approach.

The Knowledge-based view considers

knowledge as a strategic asset of firms (Grant, 1996)

and one of the motivations for a firm to manage

knowledge is the business performance

improvement (Choi and Lee, 2003). Almost all the

different definitions of the KM processes

acknowledge some form of Knowledge Creation

(Benbya, Passiante and Belbaly, 2004; Chen and

Chen, 2006; Davenport and Prusak, 2000).

The Knowledge creation process is also

recognized as one of the most important strategic

assets of the firm (Dierickx and Cool, 1989;

Leonard-Barton, 1992; Conner and Prahalad, 1996;

Riera C. and Iijima J.

Linking Knowledge Creating Capabilities, IT Business Value and Digital Business Value: An Exploratory Study in Japanese SMEs.

DOI: 10.5220/0006487900290040

In Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (KMIS 2017), pages 29-40

ISBN: 978-989-758-273-8

Grant, 1996 cited in Lewin and Massini, 2004).

This study follows the same research path and is

aligned with other studies which are focused on the

Knowledge Creation Process (Choi and Lee, 2002;

Chou, 2005).

In terms of Knowledge Creation theory, Nonaka

and Takeuchi (1995) acknowledged tacit and

explicit knowledge types and their interaction and

transformation as key components of their

knowledge creation process under the name of

‘SECI Model’.

This study leverages the concept of 'Balanced

SECI' as a measurement of 'Knowledge Creating

Capabilities' (Riera, Senoo and Iijima, 2009). The

concept considers that a bottleneck in the knowledge

creation process may appear if a firm is over-

focused or has a lack-of focus on a particular SECI

process. It also identified a positive relationship

between Balanced SECI level and financial

performance.

Finally, there is extensive research that addresses

the concern of the IT effect on firm performance.

This concern officially started when the term IT

Productivity Paradox was coined in 1987 at the time

the economist Robert Solow mentioned that the

computer age could be seen everywhere except in

the productivity statistics (Brynjolfsson and Hitt,

1998). Together with the development of IT,

researchers have also changed their approach to this

phenomenon. Initially the analysis considered the IT

investment first independently in the form of IT

assets, Weill and Aral defined IT assets into

categories and found specific relationship between

certain types of assets with specific benefits (Weill

and Aral, 2007). Later on new theories emerged and

examined the business processes associated with the

IT utilization (Sandulli et al., 2007).

Subsequently, the research focused on organiza-

tional characteristics. Overall the results have not

been conclusive. Only some found a positive

relationship between IT and firm performance.

This study is aligned with other studies focusing

on the critical capabilities of the firms in the search

to unveil how IT enhances firm performance (Weill

and Aral, 2007; Brynjolfsson and Hitt, 1998).

Since the business value from IT has been

extensively analyzed, with the availability of digital

technologies it can be foreseen that similar concerns

will arise in the near future.

Acknowledging the individuality of each firm to

define and pursue its own goals; this study uses a

well known classification of business objectives

defined by the Balanced Scorecard (Kaplan and

Norton, 1996). It uses these to inquire the firms in

which type of business objectives the IT and digital

technologies were put into practice; while it inquires

also about the contribution obtained from IT and

digital technologies. This is not the first time that the

Balanced Scorecard concept has been linked with

KM (Cabrita, Machado and Grilo, 2010), however

no studies have tested the Balanced SECI concept

against the achievement of business goals.

In a nutshell, this study contributes to the

literature of KM performance measurement by using

Balanced SECI and considering that each firm

pursues its own objectives, while at the same time

inquiring on the level of achievement by the use of

IT and digital technologies, -defined as IT and

Digital Business Value.

The rest of the paper is organized as follows.

Section 2 presents the theoretical background.

Section 3 explains the framework and hypotheses.

Section 4 describes the data and metrics. The

analysis and findings are included on Section 5. A

discussion is included on Section 6, while the

conclusions are addressed in Section 7.

2 THEORETICAL

BACKGROUND

2.1 Knowledge Creating Capabilities

Over the years academia and scholars have studied

and developed several concepts with the aim to

explain how competitive advantage can be achieved

and sustained. It starts with the resource-based view

that considers that the organization is a collection of

resources (Amit and Shoemaker, 1993) and suggests

that competitive advantage can be achieved when an

organization is able to develop difficult-to-imitate

resources (Barney, 1986).

Later on, recognizing the dynamic nature of the

market and its changes over time, the concept of

Dynamic Capabilities was developed. This concept

states the need that the firm’s resources need to

change over a period of time to keep them relevant

(Teece and Pisano, 1997). Researchers (Grant, 1996)

explain that dynamic capabilities are the foundation

that makes managers acquire and combine resources

in order to generate value-creating strategies. To

make the difference clear between Resources and

Capabilities the academia (Amit and Schoemaker,

1993) defined that resources are converted into final

products or services, while capabilities enable a firm

to deploy resources, using organizational processes

to achieve a desired end.

The knowledge-based view considers the

knowledge as the most important resource for a firm.

Within the phases of KM, the knowledge creation

and integration phases were considered the most

important assets of the firm from a strategical point

of view (Lewin and Massini, 2004).

A very important work in the area of knowledge

was done by Nonaka and Takeuchi (1995). They

introduced the SECI Model as a model of

knowledge creation process to understand the

dynamic nature of knowledge creation, and to

manage such a process effectively. They suggested

that the most important aspect of understanding a

firm’s capabilities in terms of knowledge is the

dynamic capability to continuously create new

knowledge out of existing firm-specific capabilities,

rather than the stock of knowledge that a firm

possesses at one point in time (Nonaka, Toyama and

Takeuchi, 2000).

The concept of Balanced SECI (Riera, Senoo and

Iijima, 2009) was developed as a measure of

Knowledge Creating Capabilities (KCC) and

considers that there could be bottlenecks in the

process of knowledge creation when a firm is either

over-focused or has a lack-of focus in one of the

four processes of the SECI Model. Previously

Balanced SECI score has been linked with two

specific financial measures (Riera, Senoo and Iijima,

2009). This study also aims to expand the literature

on Balanced SECI by determining if there is a

relationship with the overall firm objective

achievement accomplished by the use of IT or

digital technologies in the categories provided by the

Balanced Scorecard.

2.2 Business Value from IT

Decades of studies have been dedicated to exploring

the effects of IT on firm performance. This

phenomenon is known as the “IT Productivity

Paradox”. Earlier studies found inconclusive results,

however as the research developed and started to

consider other firm characteristics as complements

to IT the results were more optimistic. Table 1 is

adapted from an existing study (Dedrick, Gurbaxani

and Kraemer, 2003) and shows major researches on

the topic. This research is consistent with studies

considering that the IT impact on firm performance

requires an analysis performed together with firm

capabilities such as the ones on the bottom section of

Table 1.

IT Business Value has become a term which

usually refers to the same concept as the IT

Productivity Paradox but with a more positive

perception in particular on the industry side. In this

study the definition of IT Business Value came from

the application of a concept found in the literature

relevant to IT maturity where IT Business Value is

defined as the contribution that IT resources and

capabilities make to help an organization achieve its

objectives (Curley, 2004 cited in Innovation Value

Institute, 2016).

Table 1: Key studies exploring IT, firm performance and

other firm capabilities (adapted from Dedrick, Gurbaxani

and Kraemer, 2003).

Study

Findings

Relationship among IT and firm performance

Mahmood M.A. et al. (1993), Weill

(1992), Wilson (1993), Loveman

(1994)

None or

Negative

Weill (1992), Wilson (1993), Loveman

(1994), Brynjolfsson and Hitt (1995),

Brynjolfsson and Hitt (1996), Hitt and

Brynjolfsson (1996), Brynjolfsson et

al.(1998), Greenan et al. (2001)

Positive

IT, firm performance and other firm capabilities

Bresnahan et al. (2002),

Brynjolfsson et al. (1998), Ramirez

et al.(2001), Francalanci and Galal

(1998), Devaraj and Kohli

(2002),Tallon et al. (2000), Weill et

al. (2004, 2005)

Positive

2.3 Business Value from Digital

Technologies

The development and availability of digital

technologies like social, mobile, analytics, cloud and

Internet of Things bring opportunities as well as

threats for established companies (Ross et al., 2016).

The definition of Digital Business Value is

derived in a similar way than the definition of IT

Business value and it is described as the contribution

that digital technologies make to help an

organization achieve its objectives.

3 FRAMEWORK AND

HYPOTHESES

This study explores the relationship between

Knowledge Creating Capabilities on one side, and

the business value of IT and digital technologies

measured as the level of achievement of 4 types of

business objectives on the other side. The model is

described in Figure 1.

Figure 1: Main Framework and Hypotheses.

The main hypothesis in this study is defined as:

“Knowledge Creating Capabilities, IT Business and

Digital Business Value are positively related”. The

detailed hypotheses are:

- H1: Knowledge Creating Capabilities are

present in firms that achieve business value

from IT (IT Business Value).

- H2: Knowledge Creating Capabilities are

present in firms that achieve business value

from digital Technologies (Digital Business

Value).

- H3: the firms’ objectives achieved by using

digital technologies (Digital Business Value)

are supported by the level of achievement in IT

(IT Business Value).

4 DATA AND MEASURES

An empirical analysis is used in order to validate

these three hypotheses. This is aligned with studies

that evaluated KM based on firm performance (Choi

and Lee, 2002) as well as with the literature on IT

and firm performance (Weill, 1992; Weill and Aral,

2007).

4.1 Target Population

This study focuses on Japanese SMEs that have been

selected by the Japanese Ministry of Economy,

Trade and Industry (METI, 2016a) in the list of

“Competitive IT Strategy SME Selection 100” from

2015 and 2016. The companies corresponding to the

year 2017 were not published at the time this study

was closed. The companies in this list are selected

due to their record of effective utilization of IT and

demonstrated good business performance.

This particular group of companies were selected

as the target population since this study aims to

clarify the relationship between Knowledge Creating

Capabilities, IT Business Value and Digital Business

Value. The characteristic of business achievement

by the use of IT is already verified by METI and

therefore, it makes these companies worth analyzing

in order to validate the hypotheses. Furthermore,

earlier studies have leveraged similar groups as

target population (Hirano, 2005; Riera, Senoo and

Iijima, 2009).

Nevertheless, it is important to justify the focus

on Small and Medium Enterprises (SME) that this

study addressed and the particular characteristics

and context of SMEs. The relevance of SMEs in the

Japanese economy is reported by the Ministry of

Economy, Trade and Industry. They account for

99.7% of all enterprises and approximately 55% of

gross value-add across the Japanese economy (Small

and Medium Enterprise Agency, 2016). Due to their

importance there is a need for SMEs to understand

how to use IT and digital technologies in order to

remain competitive.

The industry composition of the 60 companies in

the target population is as follows: 25.0%

Manufacturing, 11.7% Retail, 10.0% Services, 8.3%

Wholesale, 6.7% Information & Communication,

6.7% Construction, 5.0% Printing, 5.0% Other, 3.3%

Transportation, and with 1.7%: Wholesale and retail

trade, Other (nursing care), Accommodation, Retail /

Nursing care, Gravel sampling, Other (dental),

Agriculture, Food & Beverage, Real Estate,

Information service and Manufacturing and

Agriculture.

4.2 Measuring Knowledge Creating

Capabilities (KCC)

Consistent with similar studies that explored

Knowledge Creating Capabilities as Organizational

Characteristics in an SME context (Riera, Senoo and

Iijima, 2009), ‘Balanced SECI’, was used to

measure Knowledge Creating Capabilities (KCC).

This was captured with a questionnaire that listed six

items or behaviours related to each of the four SECI

Model processes. Firms were requested to select 12

out of 24 behaviours that most reflected their

Balanced

SECI

IT Business Value

FI-OA

- FI-OA: Financial-related objectives achievement.

- CU-OA: Customer-related objectives achievement

- BP-OA: Business Process-related objectives achievement

- LG-OA: Learning and Growth objectives achievement

CU-OA

BP-OA

LG-OA

Digital Business Value

FI-OA

CU-OA

BP-OA

LG-OA

H1 (+)

H2 (+)

H3 (+)

employees’ behaviours. The content came from

literature review (Nonaka et al., 1994; Nonaka and

Takeuchi, 1995; Nonaka, Toyama and Takeuchi,

2000). Balanced SECI as defined in Riera et al.

(2009) uses the results from the questionnaire to

calculate the scores in each of the SECI processes.

Afterwards, the firm’s Balanced SECI score is

calculated as the minimum score achieved in any of

the four processes. This concept tries to avoid

bottlenecks in the knowledge creating process and

represents the maximum level in which all the 4

SECI processes together can support the spiral of

knowledge creation and convert individual

knowledge into organizational knowledge which is

shared and internalized by the employees. The

Balanced SECI score is represented in Figure 2. A

sample of the SECI survey is available in Riera et al.

(2009). Using the questionnaire the highest Balanced

SECI score of a firm could be from a firm that

selected 4 items in each of the SECI process.

Figure 2: Balanced SECI score (sample).

4.3 Measuring IT Business Value and

Digital Business Value

This study defines IT Business Value as the

contribution that IT provides towards the

achievement of the firms’ objectives. This study

requested the firms to consider the IT Investment

over the last 3 years and classify it over four types of

objectives: Financial (expanding revenue, improving

productivity, improving the financial structure, etc.),

Customer-related (improving customer satisfaction,

improving customer loyalty, increasing sales to new

customers, etc.), Business Process (quality

improvement, productivity improvement, etc.),

Learning and growth (securing human resources,

human resources education, creativity, development

capability, etc.) .

The 3-years consideration was done in order to

minimize the impact of lagged results from IT

investment as well as to consider that companies can

pursue different objective types according to their

strategy. For instance a more customer-driven

company could invest in IT in order to increase

customer experience, while another could invest

focusing on fostering learning and growth to develop

new services and products. Once the IT investment

was classified into business objective types, then the

measurement of achievement used a scale with 4

levels: Not achieved (0-15%), partially achieved

(16-50%), highly achieved (51-85%) and fully

achieved (86-100%).

Following a similar approach, Digital Business

Value is defined as the contribution that digital

technologies provide towards the achievement of a

company’s objectives. From the data collected

during the IT inquiry, the participants were asked to

specify in which type of business objectives the

digital technologies were used and, the level of

objective achievement they experienced for the four

types of business objectives, using the same 4-level

achievement scale.

A list of digital technologies with definitions was

included in the questionnaire in order get responses

aligned with respect to what a specific technology

referred.

The responders were asked to consider these

digital technologies: Mobile, Cloud technology, SNS

(Social Networking Service), Big Data and

Analytics, IoT (Internet of Things), Artificial

Intelligence and 3D printing technology.

4.4 Data Collection

The questionnaire was distributed to the 60

companies registered mailbox address and they were

requested to complete the questionnaire over a

period of 2weeks. During the 2 weeks period follow

up calls were done to increase the response rate.

Twenty out of the sixty companies filled-out the

survey, representing a high response rate of 33%.

Factors which most likely helped with this response

rate could be the customized cover letter introducing

the background of the study, the follow-up calls, as

well as executing and closing the survey one month

in advance of the busy period of fiscal year end.

However the nature of the group was definitely a

factor because the list was validated by METI but

initiated by each company thru self-nomination.

Finally a report with the summary of the initial

findings was sent to the responders. The industry

composition is presented on Table 2.

Sample of firm focused on Combination

and Internalization

Balanced SECI = Min(1,1,5,6) = 1

Firm score

Firm KCC score

(Balanced

SECI)

Table 2: Response Ratio by Industry.

Industry

Target

Received

Resp.

Ratio %

Manufacturing

Service

Wholesale

Construction

Printing

Transportation

Gravel sampling

100

Other (dental technician)

100

Food & Beverage

100

*Industries with no

responses not included

Total

33.3 %

4.5 Reliability of the Data

As with any survey study, the data is as reliable as

the reliability of the responders. This is the reason

that the questionnaire was addressed to the main

responsible in each company (e.g. CEO, Director).

As a result the answers were filled by both business

and IT, such as business managers, IT strategy

representatives and CEOs. Considering that this

study focuses on the business value or achievement

of business objectives it is reasonable to accept this

mix of responders minimizing any bias that the IT

personnel may have either on purpose or per lack of

knowledge.

4.6 Validity of the Data

The same instrument to measure Balanced SECI and

its validity has been discussed previously (Riera,

Senoo and Iijima, 2009).

In addition, studies are vulnerable to non-

response and coverage errors when considering

external validity. Non-response type of error

happens when the subjects under study are different

on a characteristic relevant to the study from the

subjects which didn’t participate. In order to verify

this in the current study, firm size (number of FTE

and number of Total employees), capital, active

years were used in tests. Also as mentioned below

statistically significant differences were not found

between the groups of responders versus non-

responders.

Coverage error occurs when the sample itself

does not fully represent the characteristics of the

population to which the results are to be generalized.

In the case of this study the results are not to be

generalized to all Japanese SMEs because of the

particular characteristic of the target population

which have shown effective utilization of IT enough

to be nominated and selected as part of the list of

“Competitive IT Strategy SME Selection 100” by

the Ministry of Economy, Trade and Industry

(METI) in Japan. Because of this reason the results

cannot be generalized. However they can be used as

a reference to understand how Knowledge creating

capabilities in such companies can support the

effective utilization of IT and digital technologies to

achieve business objectives.

5 ANALYSIS AND FINDINGS

5.1 Relationship between KCC and IT

Business Value (Firm’s Objective

Achievement by IT)

In order to assess this relationship, correlation

analysis was done and included both parametric

(Pearson) and non-parametric (Kendall's tau) tests.

The tests failed to identify a statistically

significant relationship between KCC and IT

business value.

Four measurements were used to explore KCC:

 SECI aggregated score result as the sum of

results of each knowledge conversion process.

 Balanced SECI as the minimum score of the 4

processes in the SECI model.

 Balanced SECI based on the minimum score

using proportional scale of the number of

responses by each firm (i.e. some firms

selected less items than the 12 requested in the

questionnaire).

 Individual score of the 4 processes in the

SECI model.

IT Business Value was measured as the level of

objective achievement. Two criteria were explored:

 IT BV score (IT Business Value AVG)

 IT BV for each of the 4 objectives categories

(financial, customer, business process,

learning and growth).

5.1.1 Differences in the IT Contribution

towards the Achievement of Business

Objectives According to Balanced

SECI Score

The firms were divided into 3 categories according

to their Balanced SECI score: Low, Medium and

High. Analysis of variance (ANOVA) was used in

order to verify if there were differences.

Although no statistical difference was found, a

small tendency could be observed where the level of

business objectives’ achievement decreased as the

level of Balanced SECI increased for Finance,

Customer, Business process objectives; but

increased for Learning and Growth objectives group.

This is observed in the overall achievement score.

Figure 3 shows the graph using the average score.

Figure 3: Differences of Achievement of Overall business

objectives (average score) by IT according to Balanced

SECI groups (Low, Medium and High).

Each type of business objectives is also explored,

trends can be observed in Figures 4, 5, 6, 7 for each

of the objective types.

Figure 4: Differences among Balanced SECI groups (Low,

Medium and High) - Achievement of Financial obj. by IT.

Figure 5: Differences among Balanced SECI groups (Low,

Medium and High) - Achievement of Customer obj. by IT.

Figure 6: Differences among Balanced SECI groups (Low,

Medium and High) - Achievement of Business Proc. obj.

by IT.

Figure 7: Differences among Balanced SECI groups (Low,

Medium and High) - Achievement of Learning and

Growth obj. by IT.

5.1.2 Differences in the KCC According to

the Level of Achievement of Business

Objective by IT

The firms were divided into 3 categories according

to their Level of Achievement of business

objectives: Low Achievers, Medium Achievers and

High Achievers. Analysis of variance (ANOVA)

was also tested in order to explore the differences.

There were no companies that fall into the

category of High Achievers (i.e. full achievement of

business objectives in all types) therefore these are

inconclusive results. No statistically significant

difference was identified between the Low and

Medium achievers.

5.2 Relationship between KCC and

Digital Business Value (Firm’s

Objective Achievement by Digital

Technologies)

With similar results, this study did not find a

statistically significant relationship between

Knowledge Creating Capabilities and Digital

Business Value.

The same four measurements were used to

explore Knowledge Creating Capabilities. Digital

Business Value was measured as the level of

objective achievement by the use of digital

technologies; similar criteria as in section 5.1 were

explored.

5.2.1 Differences in the Digital

Technologies’ Contribution towards

the Achievement of Business

Objectives According to Balanced

SECI Score

In the same way Knowledge Creating Capabilities

and IT were analyzed in section 5.1, the categories

of Low, Medium and High Balanced SECI score

served to explore the group differences.

With similar non-statistically significant results,

the level of business objectives achievement

decreased as the level of Balanced SECI increased

for Finance, Customer, Business process objectives.

In contrast, it increased for Learning and Growth

objective group. Figure 8 shows the results at an

overall level.

Figure 8: Differences of Achievement of Overall business

objectives (average score) by digital technologies

according to Balanced SECI groups (Low, Medium and

High).

The view by each business objective category

also shows a similar tendency and can be observed

in Figures 9, 10, 11, 12.

Figure 9: Differences among Balanced SECI groups (Low,

Medium and High) - Achievement of Financial obj. by

Digital Technologies.

Figure 10: Differences on Balanced SECI groups (Low,

Medium and High) - Achievement of Customer obj. by

Digital Technologies.

Figure 11: Differences among Balanced SECI groups

(Low, Medium and High) - Achievement of Business Proc.

obj. by Digital Technologies.

Figure 12: Differences among Balanced SECI groups

(Low, Medium and High) - Achievement of Learning and

Growth obj. by Digital Technologies.

5.2.2 Differences in the KCC According to

the Level of Achievement of Business

Objective by Digital Technologies

There were no companies that fall into the category

of High Achievers therefore these are considered

inconclusive results. No statistically significant

difference was identified between the Low and

Medium achievers.

5.3 Relationship between Firm

Objective Achievement by IT and

Digital Technologies

The relationship between IT Business Value and

Digital Business Value (measured as achievement

objective by the use or contribution of IT in the first

case and digital technologies in the second case) was

explored by correlation analysis (both parametric

and non-parametric tests). This analysis produced

the statistically significant positive relationships

between the variables below:

The criteria used for IT Business Value:

 IT BV score (IT Business Value AVG)

 IT BV for each of the 4 objective categories

(financial, customer, business process,

learning and growth).

The criteria used for Digital Business Value:

 Digital BV score (Digital Business Value

AVG)

 Digital BV for each of the 4 objective

categories (financial, customer, business

process, learning and growth).

Although this may seem obvious from a first

look, it is important to remember that there is also an

extensive research related to IT project success and

IT project failure rate.

The results on this particular case seem to

suggest that the companies in the sample that have

experienced achievement of financial objectives by

the use of IT also have experienced achievement by

digital technologies. This effect may be because of

the particularity of the sample: these are companies

that have been recognized because of their efficient

use of IT in their business.

Results from correlation analysis (parametric)

are listed below (all results with P<0.01).

At overall level:

 IT_Ach and DI_Ach (r=0.885, n=16)

Between each IT and Digital objective counterpart:

 IT_Ach_Fi and DI_Ach_Fi (r=0.850, n=13)

 IT_Ach_Cu and DI_Ach_Cu (r=0.837, n=16)

 IT_Ach_Bp and DI_Ach_Bp (r=0.701, n=14)

 IT_Ach_Lg and DI_Ach_Lg (r=0.911, n=12)

These results confirmed the relationship at a

consolidated level as well as for each objective type;

where the achievement of each type of business

objective supported by IT is related with the

achievement of the same objective type by Digital

Technologies.

Additional relationships unveiled by the analysis

are presented here.

Between IT business value supported objective

types (** for P<0.01, * for P<0.05):

 IT_Ach_Cu and IT_Ach_Bp (r=0.532*,

n=15)

Between Digital Business Value supported objective

types:

 DI_Ach_Fi and DI_Ach_Lg (r=0.632*, n=11)

 DI_Ach_Cu and DI_Ach_Bp (r=0.802**,

n=14)

 DI_Ach_Lg and DI_Ach_Cu (r=0.583*,

n=12)

 DI_Ach_Bp and DI_Ach_Lg (r=0.758**,

n=12)

Also below IT and Digital cross relationships

were found. However these are not pursued in detail

in this study as explained below.

 IT_Ach_Bp and DI_Ach_Lg (r=0.812**,

n=12)

 IT_Ach_Cu and DI_Ach_Bp (r=0.708**,

n=14)

These results suggest that achievement of

Business and Process objectives by using IT (IT

business value in BP) is related to the achievements

of Learning and Growth by using digital

technologies (Digital Business Value in Lg)

achievement.

A similar relationship is found between the IT

Business value in Customer objectives and Digital

Business Value in Business processes.

Although if we consider only the objective areas

from these two relationships it could be generally

accepted that Learning and Growth could relate to

Business Process objectives; and that Business

Process objectives may relate to Customer

objectives; the variables are one related to IT

technologies and other to digital technologies. This

could be true only if all the IT technologies used for

the achievement are digital technologies. Therefore

these relationships could actually be the result of

indirect effects (e.g. relation with a common

variable) and are not pursued in more detail in this

study.

5.4 Relationship within the Processes of

SECI Model

This study also identified a negative relationship

between SECI knowledge processes.

 Socialization and Combination (r=-0.751**,

n=19)

 Externalization and Internalization (r=-

0.593**, n=19)

These could be expressed also as:

 Socialization (Tacit to Tacit) and Combination

(Explicit to Explicit) are negatively related.

 Externalization (Tacit to Explicit) and

Internalization (Explicit to Tacit) are

negatively related.

These results are not surprising but it is

important to remember that this negative

relationship exists when designing knowledge

creation initiatives.

The research framework is updated and included

in Figure 13.

Figure 13: Updated Framework.

6 DISCUSSION

This study did not explicitly inquire the participating

firms to define a specific type of performance

measurement, but instead concentrated on the

overall contribution of IT and digital technologies

towards the achievement of business objectives in

four categories.

In general, available studies used standard

measures like profitability, revenue, ROI, Net

Present Value, etc. to explore the impact or

contribution that IT as well as KM have on

organizations. While such approaches serve well the

purpose of generalizing findings on a specific type

of measure, they do not acknowledge the

individuality of each firm, as each firm could pursue

different objectives while engaging in KM or IT

initiatives.

Therefore the way how this study approached the

measurement of KM, IT business value and Digital

business value can offer a fresh look about benefit

measurement in such areas.

Digital transformation is a topic that both

academia and industry are increasingly focusing on.

Studies aim to unveil how to effectively apply

digital technologies and identify which specific

Balanced

SECI

IT Business Value

FI-

- FI-OA: Financial-related objectives achievement.

- CU-OA: Customer-related objectives achievement

- BP-OA: Business Process-related objectives achievement

- LG-OA: Learning and Growth objectives achievement

CU-

BP-

LG-

Digital Business Value

FI-

CU-

BP-

LG-

+ + + + +

+ + + +

characteristics a firm needs to develop in order to

obtain benefits. It could be expected that in a similar

way that the IT Paradox raised concerns on the value

from IT investments it will be sooner than later

when similar concerns will rise towards digital

technologies. This study aimed to leverage from past

attempts to clarify the IT Paradox in order to provide

insights on how to address the performance

assessment of digital technologies.

The results do not suggest a direct relationship

between the level of balanced knowledge creation

processes (Balanced SECI level) and the

achievement of business objectives by either the use

of IT or digital technologies as initially considered

by Hypotheses 1 and 2.

This suggestion should be further explored with

a larger set of data as the number of observations in

this study although had a good representation of the

target population can be considered low. Another

reason for the inconclusive findings could be that

additional firm capabilities not explored in this study

that may exist in the firms could shape the

relationship between Knowledge Creation

Capabilities and achievement of objectives by the

use of IT or digital technologies.

On the other hand, the findings confirmed that

firms with high level of achievement of business

objectives by IT also experience higher level of

achievements from digital technologies (Hypothesis

3). This could be a sign that such firms possess

specific characteristics different from balanced

knowledge creation processes. Characteristics such

as an effective decision process or alignment of IT

strategy with business strategy may be some factors

supporting the achievement of objectives using IT or

digital technologies.

The results showed a relationship between the

achievements of Customer and Business Process

objectives with both IT and digital technologies.

Furthermore, the relationship between the

achievement of Learning and Growth objectives by

digital technologies and the achievement of the other

3 types of objectives could indicate that in the

Digital Age, Learning and Growth focus goes hand

to hand with the achievement of other type of

objectives.

When deriving conclusions from this study, it

should be considered that a key characteristic of the

target population was that participating

organizations had achieved a level of success in the

implementation and use of technology; either by

creating new or improving existing services,

increasing customer experience, adding and making

decisions based on data captured thru mobile

technologies, etc. and they do not represent the

general population of Japanese SMEs.

Nevertheless it is worth considering that some

findings -although not statistically validated- suggest

that the levels of Balanced SECI are actually

negatively related with objective achievement of

Financial, Customer and Business Process objectives,

while positively related with the achievement of

Learning and Growth objectives.

This could mean that the more an organization is

focused on having a balanced and highly intense

Knowledge Creation Processes the more the firm

will be able to achieve Learning and Growth type of

objectives such as education, creativity,

development people capability. Likewise the same

intensity of higher balance in the Knowledge

Creation Processes may not necessarily help the

achievement of financial, customer and business

processes objectives; these types of objectives

include for example expanding revenue, improving

productivity, improving customer satisfaction,

improving customer loyalty, etc.

7 CONCLUSIONS

This study follows a business-oriented perspective

when defining IT and Digital Business Value as the

level of achievement of firm objectives by the use of

IT and digital technologies.

Exploring the relationship between Knowledge

Creating Capabilities and the achievement of

business objectives on several categories provided

insights about which objective area a firm focusing

on knowledge creation process could expect to

obtain results; as well as on which types may not

yield any results.

A relationship between the level of objective

achievement with IT and with digital technologies

was observed. In other words low achievers in the

utilization of IT also showed to be low achievers in

the use of digital technologies. Likewise high

achievers show similar performance in both business

objectives achievement with IT and digital

technologies. This is worth considering in particular

for the firms that plan to engage in digital initiatives

as a good prediction of possible digital initiatives’

performance could be taking a look at the

performance currently achieved with IT.

The future work includes increasing the target

population to provide further statistical evidence, as

well as enriching the research with a mixed-method

approach by using qualitative study on a selective

sample. In addition, exploring other organizational

characteristics that could help linking the knowledge

created through Balanced SECI with first the right

strategic decisions at an organizational level and

then the definition of IT and Digital initiatives (e.g.

Business and IT alignment) and later with its

execution (e.g. Program and Project Management,

Change Management).

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