1467-9310.00301

8/3/2019 1467-9310.00301

1/15

Technology transfer astechnological learning: a source ofcompetitive advantage for firmswith limited R&D resources

Bou-Wen Lin

Institute of Technology Management, National Tsing Hua University, 101, Sec. 2, Kuan-Fu Rd.,Hsinchu, 30013, [email protected]

The objective of this article is to answer why and how firms in developing countries with

limited R&D resources can gain sustainable competitive advantage through technology

transfer (TT). Successful firms are those that can accumulate competence through internal

technological learning after transferring technologies from external technology sources.

Organizational intelligence, firm specificity of technology, and causal ambiguity are identifiedas three mediators between technological learning performance and several antecedents

previously discussed in the literature. A survey of Taiwanese manufacturers is conducted to

explore the technological learning phenomenon as an integral part of TT, which is important

but often neglected. This article also provides an interesting research setting for the evaluation

of technological learning theories.

1. Introduction

T he resource-based view of the firm postulatesthat valuable resources are difficult toimitate or transfer and thus transferable resourcesor technologies themselves cannot be a source of

a firms competitive advantage (Barney, 1991).

Yet we observed that many manufacturing firms

in newly industrialized countries with limited

R&D resources could still compete successfully

internationally. Those manufacturers usually

depend on technologies transferred from partners

in developed countries. The bad news is that

the transferred technologies are also accessible

to other competing firms worldwide. To gain

sustainable competitive advantage, those firms

must facilitate technological learning and accu-

mulate firm-specific competence based upon the

transferred technologies so that their competitors

cannot be easily imitated. It is certainly intriguing

that the transferee could greatly enhance its

technological capability within a short time

period through an effective communication pro-

cess with the technology provider. The TTliterature is thus dominated by research on the

interaction and communication processes be-

tween transferor and transferee (e.g., Gibson

and Smilor, 1991; Lin, 1998). Little attention

has been paid to how the transferred technology

is assimilated and adapted by the transferee and

how the technology evolves to be the core

competence of the transferee and eventually

becomes a competitive weapon. This article

addresses how a transferee can assimilate external

technology through a technological learning

process. Successful transferees are those that

can integrate transferred technologies into their

existing knowledge bases and innovate in the

R&D Management 33, 3, 2003. r Blackwell Publishing Ltd, 2003. Published by Blackwell Publishing Ltd, 3279600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

8/3/2019 1467-9310.00301

2/15

subsequent technological learning stage. Perfor-

mance of subsequent technological learning is the

key and the ultimate judge of the performance

of technology sourcing efforts. This article also

provides a research opportunity of measuring

organizational learning performance, which had

hindered the empirical efforts in organizational

learning research (Gallagher and Fellenz, 1999).

2. Technology transferability

Some scholars (e.g., Mowery and Rosenberg,

1998) suggest that technology/knowledge is the

dominant feature of the world today. Burgelman

et al. (1996) define technology as the theoreticaland practical knowledge, skills, and artifacts that

can be used to develop products and services as

well as their production and delivery systems.

(p. 2) Technology is embodied in people, materi-

als, cognitive and physical processes, facilities,

machines, and tools. Unfortunately, the capabil-

ity of developing technologies is not homo-

geneously distributed among firms around the

world. TT therefore lies at the heart of the process

of economic growth, and that the progress of

both developed and developing countries depends

on the extent and efficiency of TT (Mansfield etal., 1982). Baranson (1970) also claims that

multinational production and interchange inten-

sifies the need for TT as a contributor to

efficiencies in resources utilization. High-tech

manufacturers in newly industrialized countries

such as China, Taiwan, and Korea have limited

R&D capabilities and depend on TT as an

important route to gain access to advanced

technologies. Kogut and Zander (1993) even

claim that TT is at the center of issues about

growth of firms, domestically and internationally.

A. Two schools of thought

There are two schools of thought about the

transferability of technology. On the one hand,

economic theories often take technology as given

that is embodied in products or processes. Such

technology resembles blueprints, machines, or

materials, which is easily available. For example,

Solows growth model (1957) sees technology as

information and techniques that are easily

replicated and transferred. Contractor (1991)

argues that in recent decades, the transferability

of technology has increased, as recipients oftechnology become more sophisticated, requiring

less training and start-up assistance than before.

On the other hand, a knowledge-based view (e.g.,

Kessler et al., 2000) suggests that tacit knowledge

is not easily replicable and transferable (Mowery

and Rosenberg, 1989). The inherent immobility

or stickiness of the valuable resource demands

insurmountable time and costs to transfer. The

two schools of thought represent two extreme

ends of a continuum of technology transferabil-

ity. A technology consists of both parts. One

portion of a technology can be readily transferred

and the other portion of it cannot. Valuable

technological knowledge is never easily trans-

ferred from one firm to another. A time-consum-

ing technological learning process is needed to

assimilate and internalized the transferred tech-

nology. Technological learning performance de-pends on both the firms capabilities to learn new

knowledge and the nature of the technological

knowledge. The former is associated with the

intelligence of a firm that is characterized by

organizational variables. The latter is associated

with the extent to which the technological

knowledge can be learned by the firm. Firm

specificity (e.g., Williamson, 1985) and causal

ambiguity (e.g., Simonin, 1999) are the two

widely recognized concepts in the literature to

characterize the transferability of a technology.

The resource-base view of the firm stresses theimportance of internal resources for sustainable

competitive advantage. This view posits that firms

compete on the basis of unique resources that are

valuable, rare, difficult to imitate, and non-

substitutable by other resources (e.g., Barney,

1991). Resource-based theorists (e.g., Prahalad

and Hamel, 1990) broadly define resources to

include physical assets, knowledge, technology,

organizational capabilities, and operations proce-

dures. Inimitability is central to understanding the

sustainability of competitive advantage (Spender

and Grant, 1996). There are two types of barriers

to imitation that sustain firms competitiveadvantage. First is firm specific assets or resources

that were developed due to history-dependent

factors (Barney, 1991), such as a first mover in a

market with time compression diseconomies

(Dierickx and Cool, 1989) or socially complex

resources, such as trust and organizational

cultural and routines (Barney, 1991). Second is

causal ambiguity, which is one of the key concepts

that construct the resource base theory of the firm

(Barney, 1991). Lippman and Rumelt (1982: 420)

suggest that causal ambiguity (i.e., the ambiguity

regarding the nature of causal relations betweenactions and outcomes) acts as a powerful on both

imitation and factor mobility. The resource-based

Bou-Wen Lin

328 R&D Management 33, 3, 2003 r Blackwell Publishing Ltd 2003

8/3/2019 1467-9310.00301

3/15

view also poses a question: how can some

technology recipients outperform other technol-

ogy adopters even the original technology donors?

If the technology is critical resources for compe-

titive advantage, how can it be transferred? One

possible answer is that the recipients can enhance

their technological capabilities through technolo-

gical learning after TT, a topic not yet explicitly

addressed in the literature. It is interesting to

investigate how technology transferees can learn

and assimilate external technological knowledge

into an integral part of their competitive advan-

tages (Olayan, 1999).

B. Technological capability andorganizational learning

Kessler et al. (2000) distinguish two types of

organization learning process in the context of

technology management: internal learning and

external learning. As Simon (1991) suggests, the

internal learning process starts with the creation

and utilization of technology by individuals. The

external learning process, on the other hand,

starts with the identification of knowledge created

outside the organization. Although tradeoffs are

involved in the internal and external learning

process (Bierly and Chakrabarti, 1996), both

internal and external learning constitutes an

integrated whole of an organizational learning

process. TT is a technological learning process

involving internal and external learning. The locus

of technological learning in TT research has

shifted from external learning to internal learning.

Evidence suggests that firms learn from their

previous experience when gradually expanding

into culture space, and that centrifugal expansion

patterns are more successful than a random

strategy (Barkema et al., 1996). Cusumano and

Elenkov (1994) report that technology capability,

including through international TT, comes fromestablishing appropriate organizational routines,

accumulating specialized industrial skills, and

acquiring the ability to learn selectively. Steensma

(1996) further argues that in contemplating a

collaborative technology acquisition, a firm needs

to both access the characteristics of the incoming

technology and establish goals in regard to the

desired competency development.

3. Theoretical model and hypotheses

This study focuses on the technological learning

after transferring technology from external orga-

nizations. A conceptual model for technological

learning performance is proposed as shown in

Figure 1. Variables in the model are classified into

three categories: the independent variables, med-

iating variables, and dependent variables (e.g.,

Lee and Kim, 1999). Organizational intelligence,

firm specificity, and causal ambiguity and are

treated as three mediators between technological

learning performance (the dependent variable)

and several antecedent variables. This model

differs from previous studies in the literature in

that it distinguishes the antecedents and the

mediators. Antecedents impact on technological

learning performance indirectly through those

three moderators. Causal ambiguity (e.g., Reed

and DeFillippi, 1990) is a well-establishedconstruct, which is independent of the firm to

characterize the technological knowledge. The

model neglects the antecedents to causal ambi-

guity since the antecedents of causal ambiguity

have been extensively studied (e.g., Simonin,

1999).

Technological learning performance is deter-

mined by the capability of an organization to

learn, that is, organizational intelligence (Glynn,

1996) and the characteristics of technological

knowledge (i.e., causal ambiguity and firm

specificity). Organizational intelligence is a con-struct developed to measure the capacity of an

organization to create and apply knowledge. The

construct, firm specificity, is drawn from transac-

tion cost theory (i.e., Williamson, 1985). Techno-

logical knowledge can be firm specific in that it

can be very specialized to the firms organiza-

tional settings. Firm specific technological knowl-

edge can be valuable to the firms competitive

advantages, difficult to transfer, and time-

consuming to develop. Causal ambiguity (e.g.,

Lippman and Rumelt, 1982) refers to the extent

to which a technology is difficult to be explicitly

articulated because the relations between actionsand results are ambiguous. Technologies that are

difficult to be articulated and codified cannot be

efficiently communicated, accumulated or assimi-

lated within an organization.

A. Organizational intelligence

A learning organization refers to an organization

skilled at creating, acquiring, and transferring

knowledge, and at modifying its behavior to

reflect new knowledge, and insights (Hall, 1995).The technology transfer literature suggests that

tacit knowledge is not easy for an organization to

Technology transfer as technological learning

r Blackwell Publishing Ltd 2003 R&D Management 33, 3, 2003 329

8/3/2019 1467-9310.00301

4/15

assimilate in a short period of time. Glynn (1996:

1088) defines organizational intelligence as an

organizations capability to process, interpret,

encode, manipulate, and access information in

a purposeful, goal-directed manner, so it can

increase its adaptive potential in the environment

in which it operates. Therefore, an organization

with a high level of organizational intelligence is a

learning organization that can learn correctly,

accurately, and appropriately from its experience.

A firms distinctive competence is a set of

differentiated technological skills, complemen-

tary assets, and organizational routines and

capacities used to create sustainable competitive

advantage (Burgelman et al., 1996: p. 34). Grant

(1991) characterizes a hierarchy of organizationalcapabilities that specialized capabilities are inte-

grated into functional capabilities such as mar-

keting, manufacturing, R&D, and IT capabilities.

Technological learning capability is in line with

the term competence, which has been described

differently by a number of scholars. Prahalad and

Hamel (1990) downplay physical assets and

define core competence as the collective learning

in the organization which creates the ability to

consolidate corporate-wide technologies and pro-

duction skills into competencies that empower

individual businesses to adapt quickly to chan-ging opportunities.

Organizational intelligence can thus be seen as

a source of a firms competitive advantage. The

resource-based view thus suggests that technol-

ogy is part of firms intangible or firm-specific

assets. In an empirical study on US firms

international strategic partnerships, Lynskey

(1999) reports that it is useful to understand

firms technology capacity as its ability to learn

new external technologies. Technology adopters

can gain competitive advantages only through a

technological learning process to build their own

core technological compences that can be quitedifferent from those of the original technology

developers. Grant (1991) classifies a firms critical

resources as tangible, intangible, and personnel-

based resources. While resources serve as a basic

unit of analysis, a firms competitive advantages

come from assembling resources that work

together to create capabilities that can be defined

as the ability to assemble, integrate, and deploy

valued resources, in combination or co-presence.

In the context of technology sourcing, Lynskey

(1999) suggests that a distinction can be made

between those technology agreements involvingthe exchange of resources and those involving

the exchange of competencies. The former type

is traditional TT agreements that technical know-

how is exchanged in return for licensing fees or

royalties. The latter involves information-based

invisible assets, representing tacit knowledge that

cannot be appropriated readily. We can expect

that a firm with higher organizational intelligence

will have a higher level of technological learning

performance both during and after TT. There-

fore, the following hypothesis is proposed:

H1: A firms organizational intelligence is

positively associated with its technological

learning performance after technology transfer.

B. Firm specificity

A portion of technological knowledge could be

codified as documentation, hardware, software

packages, standard operation procedures, and

drawings. This portion of technology can be

transferred through a well-planned project. How-

ever, a large portion of the technological knowl-

edge is embedded in context and idiosyncrasy

(Kogut and Zander, 1993) that the recipient could

not imitate directly. On the one hand, this portion

of technological knowledge hinders the recipients

desire to keep up with the technology provider ina short run. A firm-specific technology, which is

embedded in a firm, is difficult to transfer from

one to another. We can expect a wide perfor-

mance gap between the technology recipient

and the technology provider right after the TT

project. On the other hand, the gap also leaves

much room for the recipient to further develop its

own firm-specific knowledge with technological

learning. Lado and Wilson (1994: 699) therefore

suggest that firms can build sustained competi-

tive advantage through facilitating the develop-

ment of competencies that are firm specific,

produce complex social relationships, are em-bedded in a firms history and culture, and

generate tacit organizational knowledge.

Bharadwaj et al. (1993: 89) further assert that

competitive cost and differentiation advantages

associated with synergy are less likely to be

imitated, because these are often achieved under

a unique set of circumstances as well as on the

basis of unique firm specific resources and skill

base. Firms investments on competence directed

toward developing what Nordhaug (1994) terms

standard technical competences and unique

competences. Their high task specificity, butlow firm and industry specificity characterize stan-

dard technical competences. Unique competences

Bou-Wen Lin


8/3/2019 1467-9310.00301

5/15

are both highly firm specific and task specific but

industry nonspecific. In other words, a major

proportion of employer-financed training is

directed at the creation of static fit between

employees and current work tasks (p. 70). Firms

can gain sustained competitive advantage through

facilitating the development of competencies

that are firm specific, produce complex social

relationships, are embedded in a firms history

and culture, and generate tacit organizational

knowledge (Barney, 1991; Reed and DeFillippi,

1990).

Firms in the resource-based perspective are

characterized as distinct sets of firm-specific skills

and routines so that the more an activity accesses

these valuable routines; the lower should be itscosts. As Conner (1991: 140) points out, firm-

specific activities are both more efficient and

qualitatively more productive because of the

opportunity to gain from asset interdependencies

within the firm. Kogut and Zander (1992: 395)

also formally predict that firms will make those

components that require a production knowledge

similar to their current organizing principles and

information. Therefore, the technological learn-

ing to share routines, exploit common language

and more broadly exploit task interdependencies

suggests a positive relationship between the firm-specificity of technology and its performance

within the firm. Firm specific technologies, which

are often more deeply embedded, are predicted to

be higher performing in technological learning

after TT. The following hypothesis is proposed:

H2: Firm specificity of the transferred tech-

nology is positively associated with a firms

technological learning performance after tech-

nology transfer.

C. Causal ambiguityAnother barrier preventing valuable technology

resources from imitation is the causal ambiguity

of technology. Lippman and Rumelt (1982: 420)

suggest that causal ambiguity acts as a powerful

on both imitation and factor mobility. In the

literature, causal ambiguity appears as several

similar notions such as stickness in Szulanski

(1996) and sticky information in von Hippel

(1988). Those notions are devised to describe a

similar lack of understanding of the logical

linkages between actions and outcomes, inputs

and outputs, causes and effects that are related totechnological or process know-how (Simonin,

1999: 597). For example, Szulanski (1996: 29)

defines the concept, internal stickiness, as the

difficulty of transferring knowledge within the

organization in a study of internal transfer of

best practice. Reed and DeFillippi (1990: 89)

define tacitness as the implicit and noncodifiable

accumulation of skills that result from learning

by doing. Nonaka (1994) argues that tacit

knowledge cannot be easily communicated and

shared and is highly personal that is often deeply

rooted in action and in an individuals involve-

ment within a specific context. Causal ambiguity

has been addressed in the literature in two

different ways (King and Zeithaml, 2001): linkage

ambiguity and characteristic ambiguity. Linkage

ambiguity is ambiguity about the link between

core competence and competitive advantage(e.g., Lippman and Rumelt, 1982). Characteristic

ambiguity refers to characteristics of com-

petences y that can be simultaneous sources of

advantage and ambiguity (Reed and DeFillippi,

1990). This study focuses on the latter because

technological competence is evaluated.

Resource-based theorists (e.g., Barney, 1991)

suggest that causal ambiguity of technological

knowledge is an important source of competitive

advantages that keep a firms core competence

from imitation. In practice, firms do sometimes

bribe or hire away knowledgeable employees tolearn about a competitors superior capabilities

(e.g., see Besanko et al., 2000). These intelligence-

gathering strategies will be less productive when

employees can explain little about how a firm

achieves superior performance (McEvily et al.,

2000).

McEvily et al. (2000: 294) argue that as a firm

extensively acquires explicit knowledge (through

a TT project) it reduces the level of causal

ambiguity that protects its distinctive competence

from imitation. Causal ambiguity impedes not

only technology transfer across firms but also the

creation of new knowledge within the firm. Itwould frustrate efforts to diffuse technological

knowledge with organizational boundaries to at

least the same degree. Szulanski (1996) found

causal ambiguity to be one of the primary factors

hindering best practice transfer within firms.

Teece (1976) also reports that firms incur high

costs to transfer poorly understood technologies,

which is consistent with the resource-based

arguments. Causal ambiguity also prevents a firm

from learning from its own experience and from

improving its performance over time (e.g., see

Huber, 1991). Causal ambiguity, as well ascharacteristics of technological competencies that

give rise to it, has been a particular focus in



8/3/2019 1467-9310.00301

6/15

studies of knowledge resources (Barney, 1991;

Dierickx and Cool, 1989; Reed and DeFillippi,

1990). Causal ambiguity hinders the internal

diffusion of technological knowledge and decele-

rates the rate of knowledge creation within a

company. The following hypothesis is proposed:

H3: Causal ambiguity of the transferred

technology is negatively associated with a

firms technological learning performance after

technology transfer.

D. Complexity and maturity of technology

Singh (1997: 340) define a complex technology as

an applied system whose components havemultiple interactions and constitute a nondecom-

posable whole. Following the definition, complex

technologies are systemic, have multiple interac-

tions, and are nondecomposable. Singh further

elaborates on the three characteristics: the

systemic characteristic means that a complex

good or technology comprises elemental units or

components, usually organized in hierarchies of

subsystems. This hierarchical structure causes

complicated interdependencies among interre-

lated subsystems and components. This structure

leads to multiple interactions. Though interac-tions between individual components are often

simple and direct, multiple interactions and

feedback between components within subsystems,

between components across subsystems, and

between subsystems at various hierarchical levels

create a complicated network of nonsimple

relationships. A product is nondecomposable if

it cannot be separated into its components

without seriously degrading its capabilities or

performance. After reviewing research on three

technologies of nuclear power stations, aircraft

carriers, and US Army M1 tanks, Steele (1991)

also found the complexity of organizational andsocial systems to support a technology can be

positively associated with the complexity of the

technology. Therefore, a complex technology at

the firm level often is embedded in the firm and

co-evolves with the firms organizational as well

as social systems. The unique combination of

components and their interactions within a

complex technology create outputs that are not

easily reproducible with other combinations of

inputs or with other configurations of the same

inputs. Many or most of the components in a

complex technology are highly complementary,or co-specialized (Teece, 1986). Therefore, a

complex technology system tends to be firm

specific. A technology is relatively simple if it is

embodied in the forms of documentation, materi-

als, and machines. The skill and education levels

of TT team members required to transfer a

technology are other indicators to measure the

complexity of a technology (Robinson, 1991).

Chakrabarti and Rubenstein (1976) point out

that the maturity of a technology will affect its

transferability from one firm to another. Tech-

nology is increasingly codified as its life cycle

progresses. Industrial standards emerge when

a technology becomes mature. Abernathy and

Utterback (1978) asserted that after a design

standard (or an industrial standard) emerges. A

transition form radical to incremental innova-

tions occurs when a dominant product designemerges. Incremental innovation typically results

in a increasingly specialized system in which

economy of scale in production and the develop-

ment of mass markets are extremely important

(Abernathy and Utterback, 1978). Radical in-

novation and incremental innovations require

very different organizational capabilities, which

are difficult to create and costly to adjust

(Hannan and Freeman, 1984). Incremental in-

novation reinforces the capabilities of establish

firms, while radical innovation demands them to

acquire a new set of technological knowledgeand/or reengineer their organizational structure.

Incremental innovations involve the adaptation,

refinement, and, enhancement of products and

services, which are usually more firm specific in

nature. As a technology becomes mature, the

locus of innovation may shift from product

innovation to process innovation and it becomes

increasingly firm specific. Therefore, the follow-

ing hypotheses are proposed:

H4: Complexity of a firms technology is

positively associated with the firm specificity

of the technology.

H5: Maturity of a firms technology is posi-

tively associated with the firm specificity of the

technology.

E. Organizational intelligence and itsantecedents

Organizational intelligence itself is a complex

construct demands further exploration. A com-

prehensive investigation into the construct is

beyond the scope of this study. This studyconsiders only two antecedents to organizational

intelligence: employee qualification and innovative

Bou-Wen Lin


8/3/2019 1467-9310.00301

7/15

orientation. The two variables are widely dis-

cussed in the literature as determinants of

technological learning and technology transfer.

This study intends to demonstrate that organiza-

tional intelligence is a useful mediator of techno-

logical learning and several of its explanatory

variables identified in the literature. Glynn (1996)

proposes a two-stage process for innovation: (a)

initiation and idea generation, (b) implementa-

tion and adoption. Each stage is characterized by

a variety of different activities, with contrasting

demands at both the individual and firm levels.

Corresponding to the four antecedents of orga-

nizational intelligence, employees qualification

and innovation orientation are the main drivers

of organizational intelligence at the first stage.At the second stage, goal alignment is the

innovation driver. The initiation stage of organi-

zational innovation requires fewer controls,

greater autonomy, diversity and informality,

whereas implementation is facilitated by goal

alignment approaches, programmed tasks and

central direction.

Glynn (1996) proposes three theoretical models

of organizational intelligence. The aggregation

model assumed that the intelligence of individual

members aggregates as organizational intelli-

gence. She further suggested that it would bepossible to measure organizational intelligence as

the aggregated total, maximum or mean of

individual intelligences. Sadler-Smith and Badger

(1998) report that the thinking processes of

individual members of a firm may affect the

global approach to learning and some members

of the organization have greater influence. They

suggest that one mechanism for organizational

learning is to surface and share of individual

mental models to form shared mental models.

Kessler and Chakrabarti (1996) reviewed re-

search into new product innovation and found at

the work-team and individual level, decentraliz-ing decision-making can facilitate innovations. It

motivates employees to go against the status quo,

increases employees involvement in and aware-

ness about technological learning, and subse-

quently strengthens employees commitment to it.

Kessler and Chakrabarti (1996) also found that

greater experience of employees is associated

with relatively faster product innovation. Osborn

(1988) revealed that 3Ms use of autonomy

and personal innovation time facilitated idea

generation.

Damanpour (1991) found that a positiveattitude toward change was significantly related

to firms innovativeness. Dougherty (1992) also

found that innovation was furthered when

different functional groups programs and rou-

tines were synthesized by a common direction

and belief system (i.e., a shared culture). What

seems to set the initiator apart from other

individuals is the drive to make a difference,

combined with a feeling of responsibility to the

organization. This balance between individual

and organizational success was expressed during

the research in several ways. Frohman (1999)

studied several innovations and found that all

initiators knew the goal of their companies and

aligned their innovation efforts to it. Froman

reports that many initiators knew their company

suffered from critical problems, such as being a

high-cost producer, and that management wassearching for ways to drastically cut costs. An

organization can be seen as two or more

individuals working in concert to achieve a

common goal. Goal alignment and coordination

are central to the very existence of organizations.

Organizational control may be viewed as every-

thing that can help to assure that individuals

work together toward common goals. Therefore,

the following hypothesis are proposed:

H6: Overall employee qualification of a firm is

positively associated with the firms organiza-tional Intelligence.

H7: Innovative orientation of a firm is posi-

tively associated with the firms organizational

Intelligence.

4. Research method

The unit of analysis was the technological

learning process within a firm after transferring

a technology from an external technology source.

A survey was conducted to collect data for thestudy. This study adopted five-point Likert type

scale in the survey. Data needed to examine the

research model were collected through the self-

reported survey questionnaires. The population

of interest was engineering and technology units

of firms in Taiwan that transfer technologies from

foreign technology sources. This study limited the

sampling frame to larger manufacturers with

more than 250 employees. This study followed a

systematic approach in constructing the mailing

list for the survey. First, the larger manufacturing

firms were identified through a search of thefactory database provided Industrial Develop-

ment Bureau, Ministry of Economic Affairs,



8/3/2019 1467-9310.00301

8/15

Taiwan (http://icmfac.moeaidb.gov.tw/fidbweb/

first.jsp). Manufacturers that are not likely to

adopt technologies from foreign sources were

excluded from the list. This yielded a set of 850

manufacturers. The mailing addresses for these

manufacturers were obtained from the factory

database. Senior managers were chosen as the

respondents as they are likely to be most

informed about quality initiatives in engineering

and technology units. A total of 750 question-

naires were mailed. A total of 84 usable responseswere received, resulting in a response rate of

11.2%. The response rate is below the minimum

recommended level of 20% but is similar to those

obtained in many surveys of Taiwanese firms.

The low response rate is probably because many

of the manufacturing firms do not have TT

projects in the last few years.

Measures

The constructs that need to be operationalized

are complexity, maturity, employee qualification,innovation orientation, organizational intelli-

gence, firm specificity, causal ambiguity, and

technological learning performance. The scales

were refined based on a pilot study conducted

with two managers of technology unit, two senior

executive managers, and two researchers working

in the area of technology management. Techno-

logical learning performance after TT is oper-

ationalized with seven question items including:

overall learning results, competitiveness enhance-

ment, market performance, technological perfor-

mance, technology independence, and technologymodification. Organizational intelligence is oper-

ationalized with four question items: problem-

solving capability, responsiveness to environmen-

tal changes, information processing, and learning

by experiences. Causal ambiguity is operationa-

lized as three question items: ambiguity in input-

output relationships, uncodifiable, and tacitness.

Firm specificity is measured with two question

items: required investments on both specialized

physical assets and human resources. Complexity

is measured with a single question item and

maturity with two items. Employee qualification

is measured with four question items: education,problem-solving capability, training programs,

and creativity. Finally, innovation orientation is

operationalized with two question items: open-

ness and risk-taking. Appendix summarizes the

results of scale reliability. The results indicate

that all of the scales meet acceptable levels of

reliability. Based on the constitutive definition of

the constructs presented earlier, factor scores

were computed by averaging the item scores for

each factor used as indicators of the constructs in

the research model.

5. Results

To examine potential non-response bias, we

compared respondents and the population on

three variables (number of employees, sales, and

age of the firm). None of these three t-tests for

differences between the sample and the popula-

tion means was statistically significant at the

significance level of 0.05. To check for nonre-

sponse bias, I divided the returned surveys into

two groups according to the dates they were

returned. The two groups demographic profilesare similar. We also found no significant differ-

ence between earlier respondents and later

Firm

Specificity

Organizational

Intelligence

Causal Ambiguity

Maturity

Complexity

Employee Qualification

Technological

Learning Performance

H3 (-)H2 (+)

H4 (+)

H6 (+)

H5 (+)

H1 (+)

Innovation Orientation H7 (+)

Figure 1. A conceptual framework for technological learning performance.

Bou-Wen Lin


8/3/2019 1467-9310.00301

9/15

respondents on the scores of all question items.

The absence of differences would be consistent

with the claim that response bias seems not to be

a major problem (Armstrong and Overton, 1977).

Table 1 reports the construct intercorrelations.

The proposed hypotheses were tested with multi-ple linear regression analysis. Three regression

models are used to separately test hypotheses

derived from the research model.

A. Technological learning performance

Table 2 presents the effects of causal ambiguity,

firm specificity, and organizational intelligence

on technological learning performance after TT.

The correlation coefficients among regressors, on

average, are not high. Further tests of the value of

the variance inflation factor (VIF) yielded a valueless than 1.5 for all the cases, indicating no

existence of severe multi-colinearity. The first

general regression is conducted with technologi-

cal learning performance as dependent variable.

The result is presented in Table 2. The overall

model specification is robust. The F statistic

(F30.049) is significant at the 0.01% level

(p-value o0.0001) indicating at least a 99.99%

probability that at least one coefficient of the

explanatory variables is not zero. The adjusted

R-square also indicates the high explanatory

power of the model, accounting for 51.8% ofthe variance. Causal ambiguity turned out to be a

significant factor, negatively influencing techno-

logical learning performance. Firm specificity

positively influences technological learning per-

formance, which is significant at 5% level.

Organizational intelligence is the most influential

factor among the three explanatory variables that

positively influences technological learning per-formance at 0.01% significance level. This result

is in line with our hypothesis H1, H2, and H3.

B. Firm specificity

The second regression is conducted with firm

specificity as dependent variable. The result is

presented in Table 3. The correlation coefficient

between two regressors complexity and matur-

ity is 0.29, which is not high. Further tests of the

value of the variance inflation factor (VIF)yielded a value less than 1.5 for both cases,

indicating no existence of severe multi-colinear-

ity. The F statistic (F28.052) is significant at

the 0.01% level. The adjusted R-square also

indicates the high explanatory power of the

model, accounting for 40.05% of the variance.

Table 3 reports the effects of two antecedents

technology complexity and maturity on firm

specificity. The regression results show a positive

relationship between technology complexity and

firm specificity (0.424, po0.0006). Maturity is

positive associate with firm specificity (0.362,po0.0001). This result is consistent with the

hypothesis H4 and H5.

Table 1. Correlations between variables

Construct 1 2 3 4 5 6 7 7

1. Technological Learning Performance 12. Causal Ambiguity 0.46 13. Firm Specificity 0.49 0.24 14. Organizational Intelligence 0.68 0.42 0.46 15. Complexity 0.44 0.22 0.57 0.54 16. Maturity 0.28 0.41 0.47 0.12 0.29 17. Employee Qualification 0.55 0.42 0.44 0.69 0.46 0.21 18. Innovative Orientation 0.48 0.37 0.34 0.58 0.39 0.13 0.65 1

Table 2. Regression model one technological learning performance

Variable Parameter T for H0: Prob.4|T|

H1 Causal Ambiguity 0.154* 2.364 0.0216H2 Firm Specificity 0.146* 2.345 0.0206H3 Organizational Intelligence 0.379** 5.389 0.0001

Adjusted R-square0.518

N84, *po0.05, **po0.01



8/3/2019 1467-9310.00301

10/15

C. Organizational intelligence

The third regression is conducted with organiza-

tional intelligence as dependent variable. The

result is presented in Table 4. The correlation

coefficient between two regressors employee

qualification and innovative orientation is 0.65,

which is acceptable. Further tests of the value of

the variance inflation factor (VIF) yielded a valueless than 2.5 for both cases, indicating no existence

of severe multi-colinearity. The F statistic

(F52.5) is significant at the 0.01% level. The

adjusted R-square also indicates the high expla-

natory power of the model, accounting for 55.98%

of the variance. Table 4 reports the effects of two

antecedents employee qualification and innova-

tive orientation on organizational intelligence. The

regression results show a positive relationship

between employee qualification and organizational

intelligence (0.389, po0.004). Innovational orien-

tation is positive associate with organizationalintelligence (0.386, po0.002). This result is con-

sistent with the hypothesis H6 and H7.

D. Tests of mediation

Results suggest that causal ambiguity, firm

specificity, and organizational intelligence med-

iate the effects of antecedent variables on

technological learning performance. In this arti-

cle, causal ambiguity is taken into account but its

mediating role is not tested since Simonin (1999)

has empirically demonstrated the mediating effectof causal ambiguity between its antecedents and

knowledge transfer. A sequential procedure

recommended by Baron and Kenny (1986) is

adopted to test the mediating effects of firm

specificity and organizational intelligence on

technological learning performance. In the first

step of the analysis, the dependent variable (i.e.,

technological learning performance) is regressed

on antecedent variables corresponding to firm

specificity and organizational intelligence respec-

tively. These results are shown as model 1A and

2A in Table 5. In the second step, each of the twomediators is included in the models to assess

whether it reduces the effects of the antecedents

Table 4. Regression model three organizational intelligence


H6 Employee Qualification 0.389** 3.73 0.004H7 Innovative Orientation 0.386** 3.92 0.002


N84, *po0.05, **po0.01

Table 3. Regression model two firm specificity


H4 Complexity 0.424** 5.287 0.0006H5 Maturity 0.362** 3.592 0.0001


N84, *po0.05, **po0.01

Table 5. Alternative models for testing mediation effects

Model 1A 1B 2A 2B

Firm specificity 0.235**Organizational intelligence 0.395**Complexity 0.244** 0.145Maturity 0.134 0.048Employee qualification 0.213* 0.058Innovative orientation 0.253** 0.100R2 0.209 0.273 0.367 0.486Adjusted R2 0.189 0.245 0.351 0.467F value 10.49 9.76 22.97 24.655

N84, *po0.05, **po0.01

Bou-Wen Lin


8/3/2019 1467-9310.00301

11/15

to nonsignificance. Mediation occurs if the effects

of the antecedents on IT outsourcing perfor-

mance are reduced in the presence of the

mediator and the overall fit is improved. Both

of these conditions are acceptable, as is shown in

Table 5.

6. Discussion and conclusion

Transferring technology from developed coun-

tries is a major route of sourcing advanced

technology for manufacturers with limited R&D

capabilities. Previous work on TT has focused on

the interaction and communication process be-

tween transferee and transferor. McEvily et al.(2000: 294) argue, a firm acquires explicit

knowledge, it reduces the level of causal ambi-

guity that protects its distinctive competence

from imitation. An organizational learning

perspective (Senge, 1990) can shed some light

on the technological learning process. This article

proposes a conceptual model with three media-

tors causal ambiguity, firm specificity, and

organization intelligence to explain technological

learning performance. The conceptual model

accounts for the leaning capability of the subject

(organizational intelligence) and the subject to belearned (causal ambiguity and firm specificity).

Factors that can influence technological learning

performance can be categorized into three groups

and the three mediators in turn mediate their

effects on technological learning accordingly. For

example, technology complexity does not impact

on technological learning directly. It impacts

on technological learning indirectly through its

impacts on one of the three mediators, firm

specificity. The model is supported by data of

manufacturing firms in Taiwan.

Causal ambiguity of the transferred technology

has a negative impact on technological learningperformance. This result is consistent with that in

the organizational learning literature (e.g., Simo-

nin, 1999). A technology with a high level of

causal ambiguity is less likely to diffuse and

communicate and thus technological learning and

knowledge performance will be low. Firm speci-

ficity is positively related to the technological

learning performance. This result differs from

previous work in the technology transfer litera-

ture (e.g., Teece, 1976). Since most previous TT

studies focused on the communication process of

TT that firm specificity of the transferredtechnology can be a major barrier of interfirm

communication. This article investigates the

technological learning performance after the

inter-firm TT communication that a techno-

logy with a higher level of firm specificity leaves

more room for further improvement through the

transferees technological learning. Although a

high level of firm specificity restricts the transferee

to imitate the transferors technology capabilities

in the short term, the transferee can exploit a full

potential from the transferred technology

through technological learning and adaptation.

Results also suggest that the level of organiza-

tional intelligence is positively associated with

technological learning performance. This result

is consistent with the organizational learning

literature (e.g., Glynn, 1996) as well as the TT

literature (e.g., Enos, 1991; Radosevic, 1999). Afirm with a higher level of organizational

intelligence has a higher organizational learning

capacity that can lead to higher technological

learning performance.

A noticeable literature was developed in the

last few decades to analyze the factors affecting

the accumulation of technological capabilities for

firms in developing countries (e.g., Enos, 1991).

Developing countries grow by effectively exploit-

ing an international pool of existing technologies

available from technology leading nations. The

exploitation is not done through a simplepurchase of ready-made solutions but through

an active effort by technology recipients to master

various elements of technology (Radosevic,

1999). Results of this study suggest that techno-

logical learning capability is at the center stage of

technology transfer. Technology transfer should

be conceived as a technological learning process

and the process has at least two distinct phrases.

The first phase is an inter-organizational com-

munication process that the major goal is to

duplicate the technological capability of the

transferor. The second phase is an organizational

learning process that the goal is to assimilate andintegrate the transferred technology into the

firms existing knowledge base and to foster

innovation. This article provides some evidence

that the second phase of technological learning

needs a management system different from that

of the first phase.

A. Managerial implications

Results of this study have several profound

managerial implications for managers of firmswith limited R&D resources. First, technology

transferees can gain competitive advantages and



8/3/2019 1467-9310.00301

12/15

bargaining power through its technological

learning capability. Many technology-receiving

firms can learn the easily transferable part of

a technology through a TT transaction with

technology providing firms. The technological

learning performance differs after the TT trans-

actions. Since technology markets is increasingly

efficient, technology-receiving firms must differ-

entiate themselves through their technological

learning capabilities. Second, the selection of

technology has a major impact on technological

learning performance. A technology with a high

level of firm specificity can be a bad choice for

technology transfer communications but an ex-

cellent choice for technological learning. The

transferees should not see firm-specific technolo-gies as a threat for its performance but an

opportunity for competitive advantages. Third,

organizational intelligence is the key to the

success of adopting a technology. A firm with a

high level of organizational intelligence can

effectively communicate with its counterpart

during the TT transaction and have effective

technological learning after the transaction.

Cultivating organization intelligence is a useful

tool for managers to enhance knowledge creating

as well as the assimilating of external knowledge.

Finally, manufacturers with limited R&Dresources should manage their technology acqui-

sition process strategically. Technological learn-

ing performance is predetermined by organiza-

tional intelligence and the characteristics of

technology. Technologies transferred must fit

with the firms learning capability as well as

strategic goals. If the firm is good at assimilating

external technologies and its goal is to differenti-

ate from its competitors, it can select technologies

with a high level of firm specificity and a low level

of causal ambiguity. If the firms goal is to earn

profits quickly and to exploit its advantage of

low-cost labors, technologies with a low level offirm specificity and causal ambiguity are a better

choice. As technological innovation becomes

increasingly complex and risky, hardly individual

firms own all the technological knowledge re-

quired to bring new products to the market

before their competitors. An innovative firm

needs not only to facilitate internal organiza-

tional learning in order to create new knowledge

within the firm, but also to assimilate external

knowledge through a technological learning

process. For firms with limited R&D resources,

integrating external technological knowledge intotheir existing knowledge bases is the key to their

competitive advantages. The literature on orga-

nizational learning and knowledge management

can shed some light on how we can manage

technology transfer effectively.

B. Future research

There are some limitations of this study. First, the

research relies on the self-reported survey from

R&D managers. This provides an incomplete

view of the technological learning process. For

further research, employees who involved in the

technological learning should be included. Sec-

ond, this article examines only a subset of the

many antecedents that have been reported in the

literature. Further studies can include a completeset of the antecedents of organizational intelli-

gence, causal ambiguity and firm specificity.

Third, the proposed conceptual framework is

very complex and holistic in nature. Structural

equation modeling techniques such as LISREL

are appropriate. Due to the small number of

useful samples in the study, such techniques are

not applicable. We have not test the relationship

between firm performance and technological

learning performance. Some variables such as

causal ambiguity can have a direct effect on firm

performance. The simple model proposed in thisarticle provides a step stone to explore the

complex phenomenon of technological learning

between and within organizations. We need to

understand more about the technological learn-

ing capability as strategic assets; the antecedents

and process variables affecting technological

learning performance; and the strategic fit be-

tween the nature of technology, organizational

intelligence, and the firms strategic goals.

References

Abernathy, W.J. and Utterback, M.M. (1978) Patterns

of industrial innovation. Technology Review, 80, 7,

4047.

Armstrong, J.S. and Overton, T. (1977) Estimating

nonresponse bias in mail surveys. Journal of Market-

ing Research, 14, August, 396402.

Baranson, J. (1970) Technology transfer through the

international firms. American Economic Review

Papers and Proceedings, 60, 435440.

Barkema, H.G., Bell, J. and Pennings, J.M. (1996)

Foreign entry, cultural barriers, and learning.

Strategic Management Journal, 17, 151166.

Barney, J.B. (1991) Firm resources and sustainedcompetitive advantages. Journal of Management,

17, 99120.

Bou-Wen Lin


8/3/2019 1467-9310.00301

13/15

Baron, R.M. and Kenny, D.A. (1986) The moderator/

mediator variable distinction in social psychological

research: conceptual, strategic, and statistical con-

siderations. Journal of Personality and Social Psy-

chology, 51, 11731182.

Besanko, D., Dranove, D. and Shanley, M. (2000)

Economics of Strategy, 2nd ed. New York: John.

Bharadwaj, S.G., Varadarajan, P.R. and Fahy, J.

(1993) Sustainable competitive advantage in service

industries: a conceptual model and research Proposi-

tions. Journal of Marketing, 57, 4, 8399.

Bierly, P. and Chakrabarti, A.K. (1996) Generic

knowledge strategies in the U.S. pharmaceutical

industry, Strategic Management Journal, 17, Winter

Special Issue, 123135.

Burgelman, R.A., Maidique, M.A. and Wheelwright,

S.C. (1996) Strategic Management of Technology andInnovation, 2nd ed. Chicago, IL: Irwin.

Chakrabarti, A.K. and Rubenstein, A.H. (1976)

Interorganizational transfer of technology: a study

of adoption of NASA innovations, IEEE Transac-

tions on Engineering Management, EM-23, 2034.

Conner, K.R. (1991) A historical comparison of

resource-based theory and five schools of thought

within industrial organization economics: do we

have a new theory of the firm? Journal of Manage-

ment, 17, 1, 121154.

Contractor, F.J. (1991) Interfirm technology transfers

and the theory of multinational enterprise, in:

Robinson, R.D. (ed.), The International Communica-

tion of Technology: A Book of Readings. Taylor &Francis.

Cusumano, M.A. and Elenkov, D. (1994) Linking

international technology transfer with strategy and

management: a literature commentary, Research

Policy, 23, 195215.

Damanpour, F. (1991) Organizational innovation:

a meta-analysis of effects of determinants and mod-

erators, Academy of Management Journal, 34, 555591.

Dierickx, I. and Cool, K. (1989) Asset stock accumula-

tion and sustainability of competitive advantage.

Management Science, 35, 12, 15041511.

Dougherty, D. (1992) Interpretive barriers to successful

product innovation in large firms. OrganizationScience, 3, 179202.

Enos, J.L. (1991), The Creation of Technological

Capability in Developing Countries. London: Pinter.

Frohman, A.L. (1999) Personal initiative sparks

innovation. Research Technology Management, 42,

3, 3238.

Gallagher, S. and Fellenz, M. (1999) Measurement

Instruments as a Tool for Advancing Research into

Organizational Learning. Proceedings of 3rd Inter-

national Conference on Organizational Learning,

Lancaster.

Gibson, D.V. and Smilor, R.W. (1991) Key variables in

technology transfer: a field-study based empirical

analysis. Journal of Engineering and TechnologyManagement, 8, 287312.

Glynn, M.A. (1996) Innovative genius: a framework

for relating individual and organizational intelli-

gences to innovation, Academy of Management

Review, 21, 4, 10811111.

Grant, R.M. (1991) The resource-based theory of

competitive advantage: implications for strategy

formulation. California Management Review, 33, 3,

114135.

Hall, A. (1995) A structure for organizational learn-

ing. Journal of Technology Transfer, December, 20,

1119.

Hannan, M.T. and Freeman, J. (1984) Structural

inertia and organizational change. American Socio-

logical Review, 49, 149164.

Huber, G.P. (1991) Organizational learning: the con-

tributing processes and the literatures. Organization

Science, 2, 88115.Kessler, E.H. and Chakrabarti, A.K. (1996) Innovation

speed: a conceptual model of context, antecedents,

and outcomes. Academy of Management Review, 21,

4, 11431191.

Kessler, E.H., Bierly, P.E. and Gopalakrishnan, S. (2000)

Internal vs. external learning in new product develop-

ment: effects on speed, costs and competitive Advan-

tage. R&D Management, 30, 3, 213223.

King, A.W. and Zeithaml, C.P. (2001) Competencies

and firm performance: examining the causal ambi-

guity paradox. Strategic Management Journal, 22,

7599.

Kogut, B. and Zander, U. (1992) Knowledge of the

firm, combinative capabilities, and the replication oftechnology. Organization Science, 3, 383397.

Kogut, B. and Zander, U. (1993) Knowledge of the

firm and the evolutionary theory of the multinational

corporation. Journal of International Business Stu-

dies, 24, 4, 625645.

Lado, A. and Wilson, M. (1994) Human resource

systems and sustained competitive advantage: a

competency-based perspective. Academy of Manage-

ment Review, 19, 699727.

Lee, J.-N. and Kim, Y.-N. (1999), Effect of partnership

quality on IS outsourcing: conceptual framework

and empirical validation. Journal of Management

Information Systems, 15, 2961.Lin, B.-W. (1998) Strategic management of interna-

tional communication of technology: an empirical

study of Taiwans manufacturing industries. Unpub-

lished PhD Thesis. Rensselaer Polytechnic Institute,

Troy, New York.

Lippman, S.A. and Rumelt, R.P. (1982) Uncertain

imitability: an analysis of interfirm differences in

efficiency under competition. Bell Journal of Eco-

nomics, 13, 419438.

Lynskey, M.J. (1999) The transfer of resources and

competencies for developing technological capabil-

ities: the case of Fujitsu-ICL. Technology Analysis &

Strategic Management, 11, 3, 317336.

Mansfield, E., Romeo, A., Schwartz, M., Teece, D.,Wagner, S. and Brach, P. (1982) Technology



8/3/2019 1467-9310.00301

14/15

Transfer, Productivity, and Economic Policy. New

York: Norton.

McEvily, S.K., Das, S. and McCabe, K. (2000)

Avoiding competence substitution through knowl-

edge sharing. Academy of Management Review, 25, 2,

294311.

Mowery, D. and N. Rosenberg (1998) Paths

of Innovation: Technological Change in 20th

Century America, Cambridge: Cambridge University

Press.

Nonaka, I. (1994) A dynamic theory of organizational

knowledge creation. Organizational Science, 5,

1437.

Nordhaug, O. (1994) Human Capital in Organizations:

Competence, Training and Learning. New York:

Oxford University Press.

Olayan, H.B. (1999) Technology transfer in developingnations. Research Technology Management, 42, 3,

4348.

Osborn, T. (1988) How 3M manages innovation.

Marketing Communications, 13, 1722.

Prahalad, C.K. and Hamel, G. (1990) The core

competence of the corporation. Harvard Business

Review, 68, 7791.

Radosevic, S. (1999) International Technology Transfer

and Cat-up in Economic Development. Northampton,

MA: Edward Elgar.

Reed, R. and DeFillippi, R.J. (1990) Causal ambiguity,

barrier to imitation, and sustainable competitive

advantage. Academy of Management Review, 15,

88102.Robinson, R.D. (1991) International technology com-

munication in the context of corporate strategic

decision-making, In Robinson, R.D. (ed.), The

International Communication of Technology: a Book

of Readings. London: Taylor & Francis.

Sadler-Smith, E. and Badger, B. (1998) Cognitive style,

learning and innovation. Technology Analysis &

Strategic Management, 10, 2, 47265.

Senge, P.M. (1990) The leaders new work: building

learning organizations. Sloan Management Review,

32, 1, 723.

Simon, H. (1991) Bounded rationality and organiza-

tional learning. Organization Science, 2, 125134.

Simonin, B.L. (1999), Ambiguity and the process of

knowledge transfer in strategic alliances. Strategic

Management Journal, 20, 595623.

Singh, K. (1997) The impact of technological complex-

ity and interfirm cooperation on business survival.

Academy of Management Journal, 40, 2, 339367.

Solow, R.M. (1957) Technical change and the aggre-

gate production function. Review of Economics and

Statistics, 39, 3, 312320.

Spender, J and Grant, R.M. (1996) Knowledge and the

firm: overview. Strategic Management Journal, 17,

Winter Special Issue, 59.Steele, L.W. (1991) Needed: new paradigms for R&D.

Research Technology Management, 34, 4, 1319.

Steensma, H.K. (1996) Acquiring technological com-

petencies through inter-organizational collaboration:

an organizational learning perspective. Journal of

Engineering Technology Management, 12, 267286.

Szulanski, K. (1996) Exploring internal stickness:

impediments to the transfer of best practice within

the firm. Strategic Management Journal, 17 (Winter

Special Issue), 2743.

Teece, D.J. (1976) The multinational Corporation and

the Resource Cost of International Technology

Transfer, MA: Ballinger.

Teece, D. J. (1986) Profiting from technologicalinnovation: implications for integration, collabora-

tion, licensing, and public policy. Research Policy,

15, 6, 286305.

Von Hippel, E. (1988) Sources of Innovation. New

York: Oxford University Press.

Williamson, O.E. (1985) The Economic Institutions of

Capitalism: Firms, Markets, Relational Contracting,

New York: Free Press.

Appendix

Constructs Cronbach Alpha Question items

Technologicallearningperformance

0.839 Overall technological learning successCompetitive position enhancementMarket share increaseTechnical performance enhancementTechnical independenceTechnical superiority to competitorsTechnical adaptation to local environment

Organizationalintelligence

0.871 Problem solving capabilityResponses to environmental challengesInformation processing capabilityLearning from experience

Causal ambiguity 0.775 Ambiguous association between causes and effectsNot easily codifiableTacitness

Bou-Wen Lin


8/3/2019 1467-9310.00301

15/15

Firm specificity 0.755 Investments in specialized equipment and facilitiesInvestments in skilled human resources

Complexity Not applicable Composed of many interdependent techniques, routines,

individuals, and resources.

Maturity 0.423 Mature and standardizedNo major changes for many years

Employee qualification 0.828 Level of EducationWork experience and knowledgeTraining programs

Innovation orientation 0.858 New ideas generationOpenness and flexibility of corporate climateRisk taking



Documents

1467-9310.00301