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Innovation adoption and exploitation in SMEs:

A Systematic Literature Review

LOREDANA VOLPE1 FRANCESCO RICOTTA• GIANLUCA VAGNANI♠ MARCO VALERI∗∗

Abstract

Innovation adoption has been widely debated among scholars in order to identify variables and

models that boost adoption processes within firms. Recently, increasing attention has been devoted

to understanding effective innovation adoption in Small and Medium-Sized Enterprises (SMEs).

Taking this issue, we focus our attention on potential gaps within current knowledge on the

adoption of innovation in SMEs. To this purpose, we conduct an extensive literature review on

articles and papers dealing with innovation adoption, published in the last 23 years, i.e. from 1989

to 2012. We run content and structural analysis on the collected data.

Our results contribute to the debate on innovation adoption through systematization of literature.

They also suggest a number of research directions that have not been adequately investigated yet. In

particular, scholars do not seem to have caught all the implications of innovation adoption,

especially for SMEs. Our results suggest that only a few papers have hitherto addressed the main

impediment to innovation adoption, i.e. the ability to capture its benefits, and to enhance the

effectiveness of the technology being adopted. The paper is organized as follows: Section 1 briefly

introduces the topic; Section 2 discusses some of the literature on innovation adoption. Section 3

describes the research framework and method applied. Section 4 presents our results. In section 5

we discuss some implications and directions for future research.

Keywords: innovation adoption, small and medium-sized firms, bibliometric review, content

analysis.

                                                                                                               ♦ This study is the outcome of the joint effort of all authors. Nevertheless, it must be noted that the paragraphs 2.1, 2.2 can be

ascribed to Marco Valeri; paragraph 3 to Gianluca Vagnani; paragraphs 3.1, 3.2 to Francesco Ricotta; paragraphs 4, 5, 5.1, 5.2, 5.3 to Loredana Volpe. Introduction and conclusion can be ascribed to all authors. Furthermore, this study presents the results of the scientific work coordinated by Prof. Francesco Ricotta and related to the development of the research project “Integrazione semantica di dati e servizi per le imprese in rete”, co-funded by Regione Lazio for the SSD SECS-P/08, Economia e Gestione delle Imprese, at Sapienza Università di Roma, Department of Management. We are grateful to all colleagues who have contributed to improving our research. Errors remain our own.

1 Ricercatore t.d. (Assistant Professor) of Economia e gestione delle imprese (SECS/P08) - Sapienza Università di Roma e-mail: loredana.volpe@uniroma1.it • Professore associato (Associate Professor) of Economia e gestione delle imprese (SECS/P08) - Sapienza Università di Roma e-mail: francesco.ricotta@uniroma1.it ♠ Professore ordinario (Full Professor) of Economia e gestione delle imprese (SECS/P08) - Sapienza Università di Roma e-mail: gianluca.vagnani@uniroma1.it ∗∗ Assegnista di ricerca (Research Fellow) at Department of Management - Sapienza Università di Roma e-mail: mavaleri@libero.it

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1 Introduction

The adoption of technological innovation aimed at enhancing firm competitiveness has been at the

core of the academic debate since the 1980s. Innovation is commonly conceived as the invention

and commercialization of new products or services based on the application of technological and/or

market knowledge.1 Technological innovations are valuable because they tend to increase the

accessibility and availability of resources2, stimulate collaboration between firms for innovation

exploitation, and favor access to external financing3 or technological expertise.4 Notwithstanding

scholars’ growing attention toward innovation adoption, the academic literature has mostly focused

on innovation adoption processes within large firms rather than in SMEs. Even some recent

systematic review works have not specifically considered SMEs (5-12). Thus, tracing the evolution

of the academic literature on such topic is not an easy task, especially in the area of SMEs.

Here comes the objective for our study. Our aim is to explore the evolution of the academic

literature on innovation adoption and innovation exploitation, with particular reference to SME’s

adoption decisions.

Indeed, adoption choices are even more critical when it comes to Small and Medium-Sized

Enterprises (SMEs). Firm size has been associated to a number of important implications for the

success of particular stages of the innovation process, and for the adoption of particular types of

innovation.13 Scholarly attention has mostly focused on the relationship between optimal

organizational size, market structure and technological innovation.14 The organization of markets

and the ways in which technological innovations spread within them are rapidly changing. In an

ever more competitive environment, the adoption and exploitation of appropriate technological

innovations play a strategic role for several reasons. One concerns challenging the firm knowledge

base and capacity to absorb competencies that have been created elsewhere.15 A second reason

involves the possibility to extend technological innovation to the overall organizational structure.

The adoption of technological innovations thus strengthens the firm structure by facilitating the

exploitation of environmental opportunities. Furthermore, globalization has shed new light on the

implications of innovation adoption in terms of creation of new “markets for technology skills” and

acceleration of the obsolescence of technological innovation in ways that often lead to the

emergence of a dominant design or the broad acceptance of certain technological “standard” or

product attributes.16

More specifically, we seek to analyze the literature on determinants and consequences of innovation

adoption and innovation exploitation, during a period of roughly 20 years. Methodologically, to

trace the development of the literature in a systematic and comprehensive way, we perform a

bibliometric analysis, which allows obtaining an overview of the intellectual structure of the field

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under study,17, 18 and suggests how such field is moving forward.19, 20 In our study, we partly follow

De Bakker et al. (2005) who have applied a similar analysis to describe the evolution of research

and theory on corporate social responsibility (CSR) and corporate social performance (CSP).18

Our paper is organized as follows. Firstly, we review the theories and models that have been

dominating the academic literature on innovation adoption in the last 23 years of research. In the

Research Framework and Method sections, we describe how we build our datasets. Here, we also

present the methodology used to interpret our datasets. Finally, we discuss our results and provide

recommendations for future research.

2 Literature review. Different theoretical explanations and models on innovation adoption

2.1 The development of theory Rogers 21 (1995) classical adoption of innovation model has provided the theoretical foundation for

many studies in this domain. According to the Author (p. 21), the adoption process is ‘‘the process

through which an individual or other decision-making unit passes from first knowledge of an

innovation, to forming an attitude toward the innovation, to a decision to adopt or reject, to

implementation of the new idea, and to confirmation of this decision.’’ Following Rogers,

innovation scholars have mostly used intention models or behavioral decision theories to explain

the adoption and use of new technological solutions. Among the most popular and well supported

models, which are mostly rooted in social psychology, are Ajzen’s (1991) Theory of Planned

Behavior (TPB),22 Davis’ (1989) Technology Acceptance Model (TAM)23 and the more recent

UTAUT (Unified Theory of Acceptance and Use of Technology) developed by Venkatesh et al.

(2003).24 However, these studies have focused on the consumer as the primary unit of analysis,

whereas only some more recent contributions have started to extend models of innovation adoption

from social psychology to the organizational level. In particular, it has been suggested that, since

innovation adoption decisions are typically made by a firm top management, the consumer-level

models of innovation adoption may shed light even on organizational-level adoption processes25.

At the organizational level of analysis, the debate on the adoption of technological innovations has

focused on the role of technology as a means to enable firm management beyond the boundary of

individual rationality, favor both intra-firm and inter-firm relationships,26 and increase firm

potential to follow new evolutionary paths.27-29 Nevertheless, the strategic management and

innovation literatures still do not seem to have unanimously grasped the strategic role of the

adoption of technological innovation, especially in SMEs.30 Indeed, the scientific debate has mostly

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focused on large-sized firms, such that the study of the antecedents and consequences of innovation

adoption has broadly been referred to this type of organizations.

On one hand, in terms of the consequences of adoption, scholars have proposed many and varied

justifications to innovation adoption. In particular, research on organizational learning has

distinguished between the notion of “competence-destroying” and “competence-enhancing”

innovation adoption. A competence-destroying innovation implies that adoption leads to a

substantial reconfiguration of the drivers of competition by redefining capacities and relationships,

which are deemed critical for firm survival in a competitive environment.31 Conversely,

competence-enhancing innovation implies that adoption builds on the organization’s existing

competencies and know-how, resulting in an incremental increase in the efficiency of offering a

given product or service. Furthermore, the academic literature32 has emphasized how innovation

adoption contributes to managing organizational interdependencies, and to solving the problems of

coordination between various organizational units. More generally, it has been observed that

innovation adoption helps improve the efficiency and effectiveness of firms, and thus positively

contributes to performance.26, 33, 34

These perspectives of analysis, mostly developed within the learning and innovation literatures, thus

highlight the strategic role of innovation adoption in supporting firms’ decision-making and

operational processes. More specifically, within the strategic management domain, a further

perspective on the implications of innovation adoption is rooted in the Resources Based Theory.35, 36

By postulating that firm growth and competitive advantage depend on the possession of strategic

and highly specific resources,37, 38 this perspective has emphasized the role of technological

innovation in improving the way firms are managed.39 Building on it, a number of contributions

have been developed that start to address the implications of innovation adoption even for SMEs. In

particular, researchers have pointed at SMEs’ resource shortcomings that limit their ability to

appropriate rents from technological innovations and associated market niches they have entered.40,

41 Insofar as innovation involves technologies which are increasingly standardized, widely used and

easily accessible, their adoption does not ensures SMEs are able to achieve a competitive advantage

that might prove sustainable over time.42, 43 In such instances, the contribution of adoption to the

competitive advantage of SMEs depends primarily on the ability of the firm to integrate and

combine creatively the innovation being adopted with the sets of internal human, technical and

financial resources.39

On the other hand, concerning the antecedents of innovation adoption, extant literature emphasizes

how adoption may be the result of specific determinants, especially when referred to SMEs. In fact,

scholars have identified different factors that influence SMEs decision to invest in innovation.

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One factor refers to the level of SMEs organizational development. The adoption of technological

innovations is essential to support the improvement and rationalization of business processes and

infrastructure, and to enhance the value of extant information and knowledge. A second factor

concerns the possibility to increase SMS’s level of knowledge and skills. The growth of knowledge

and skills requires technological innovation to support decision-making processes.44 To this end, it

can be said that innovation adoption is a way to meet and support SMEs processes of structural

change and growth. On the flip side, in adopting and using innovations, SME’s are likely to be

penalized by more limited resources and technological competencies,45 which often affects adoption

decisions.

Furthermore, when it comes to reaping the benefits of innovation once adopting, a number of

studies have emphasized the role of organizational size in steering organizations toward either

exploration or exploitation processes.46 Exploitation and exploration processes may take place

conditioned on their determinants.47 Exploration is determined by simply desire, the wish to

discover something new. Conversely, the main determinant to exploitation is the existence of an

exploitable set of resources, assets, or capabilities under the control of the firm. Given this

distinction, conflicting findings exist concerning the impact of organizational size on the tendency

to explore versus exploit the adopted innovation. 48 In particular, prior research has argued that size

positively relates to the propensity to engage in exploitation, 47 whilst larger organizations may have

better access to internal resources and might thus support exploration. 49

The above explanations have dominated the literature on innovation adoption, in general, and

research on SMEs’ innovation adoption and exploitation in particular. Still, none of them can be

said to dominate the others and to have gained undisputed academic legitimacy. Thus, following our

review, we would expect to find a few central concepts shaping papers in our dataset.

2.2 Models on the factors influencing the propensity to adopt technological innovations in SMEs

Decisions to adopt an innovation are influenced by a number of variables concerning both the

inherent characteristics of the technology to be adopted, and behavioral aspects of the decision-

maker. In his pioneering model, Rogers (1995) identifies five general factors that influence the

widespread adoption of technological innovations. The characteristics of the technological

solutions, which are able to influence the adoption decisions, are the following:

1) perceived relative advantage, expressing the degree of perception of a technological innovation

both before and after its adoption. The higher the level of technology adoption by firms, the greater

the perceived relative advantage;

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2) structural compatibility, expressing the perception of the degree of compatibility of a

technological innovation with the structural configuration of the organization. The greater is the

perception of compatibility of the innovation with the organization’s structural undertaking, the

higher the probability for the innovation to be adopted;

3) complexity, i.e. the perception of the level of difficulty in implementing a technological

innovation both at the beginning of implementation and during its use;

4) observability, i.e. the perceived level of performance that an organization can achieve thanks to

innovation adoption;

5) experimentability. It expresses the experimental possibilities of the new technology before being

adopted. Some technologies are hardly divisible in modules to be tested before use.

A further important model of innovation adoption stems from the Theory of Planned Behavior.22

With reference to the behavior of the decision-maker, the Theory of Planned Behaviour (TPB)

shows that the decision to adopt a technological innovation depends on three variables, namely (1)

the positive or negative attitude of the decision maker towards adoption, (2) the social pressure to

adopt, intended as a sum of all social pressure and expectations to adopt, and (3) the perception of

difficulty in implementing the new technology.

This perspective, and the related TAM model thus point at the factors that influence individual and

organizational behaviors. The intention to adopt technological innovations is a means by which

decision makers stimulate organizational behavior. Specifically, according to the TAM model, such

intention is determined by both the attitude of the decision-maker to adopt the innovation, and by

social pressures to adopt. The attitude of the decision-maker to adopt the innovation is in turn

influenced by the level of perception of the benefits that the adoption behavior will generate and by

the consequences of this decision for firm performance and growth. The second factor that

influences the intention to adopt is represented by social pressures, which affect the behavior of the

decision-maker to comply or not with a set of socio-economic rules in the firm environment. The

intention to adopt a technological innovation also depends on the perception of the decision-maker

of the difficulty of maintaining a certain behavior once deciding to adopt the innovation (so called

“perceived behavioral control”). This perception is a function of various factors, such as past

experience of the decision-maker and the perceived difficulty in implementing the new technology.

It might therefore be said that the successful adoption and subsequent exploitation of technological

innovation depend on a complex interaction between technological factors and SME’s structural

configuration.50

Given the somehow prescriptive nature of the aforementioned models of innovation adoption and

exploitation, and the cumulative body of knowledge accumulated over the years from TAM

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research, we would expect to find scarce divergences in terms and concepts within in the literature

on innovation adoption and exploitation in SMEs. Rather, we expect a pattern of overlap of existing

concepts. Indeed, new concepts substitute for extant ones if only their explanatory power is more

effective than that of established concepts in explaining why firms choose to adopt a new

technology.

3 Research framework and method

To trace the actual evolution of academic literature on innovation adoption and exploitation, we

partly followed Damanpour’s (1991)51 procedure and more recent systematic reviews published in

management journals.18 Based on these works, publications were included in our review if only they

met the following criteria: (1) the main focus of the study (either conceptual or empirical) was on

innovation adoption and/or innovation exploitation; (2) the analysis was at an organizational level

rather than an individual or organizational population level. On this point, it must be clear that we

delimited the level of analysis in our dataset when only searching for theoretical and empirical

research on innovation adoption and exploitation in SMEs rather than for all types of firms; (3) the

study was published in a scholarly book or journal.

To find studies, we searched the ABI/Inform Complete and Ebsco databases for a period of 23 years

from 1989 to 2012.We also conducted an Internet search through the Google Scholar search engine.

We searched the databases on the following categories: keywords, title and abstracts. We based our

search on terms composed of combinations of innovation adoption, innovation exploitation, and

SMEs. In total, we extracted contributions from 44 journals: Academy of Management Journal,

Academy of Management Review, Administrative Science Quarterly, American Journal of

Sociology, Annual Review of Sociology, Cambridge Journal of Economics, Decision Sciences,

Econometrica, Engineering Management, Entrepreneurship Theory & Practice, European Journal

of Marketing, Industrial and Corporate Change, Industrial Marketing Management, Information

and Management, Information Systems Research, International Journal of Innovation Management,

International Journal of Management Review, International Journal of Technology Management,

Journal of Business Research, Journal of Business Venturing, Journal of Economic Literature,

Journal of Engineering and Technology, Journal of Information Technology, Journal of Knowledge

Management, Journal of Management, Journal of Management Information Systems, Journal of

Management Research, Journal of Management Studies, Journal of Marketing, Journal of

Marketing Research, Journal of Operation Management, Journal of Organizational Behavior,

Journal of Politics, Journal of Product Innovation Management, Long Range Planning,

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Management Science, Marketing Science, MIS Quarterly, Organization Science, R&D

Management, Research Policy, Strategic Management Journal, Technovation, The British Journal

of Social Psychology.

In order to clean up the raw dataset, we manually examined all the research sources and excluded

duplicates, book reviews and articles with no apparent relationship to the topic at hand.

Furthermore, we attempted to make our dataset as comprehensive as possible by examining the

references listed in a number of recent reviews.9, 25, 52 In total, through these procedures, we

extracted 363 publications. We divided them in three datasets. One contains 324 entries on

Innovation adoption in general. A second dataset—Innovation adoption & SMEs—focuses on

innovation adoption in SMEs, and contains 20 entries. The third dataset—Innovation exploitation &

SMEs—addresses innovation exploitation processes in SMEs and contains 19 entries.

More generally, in analyzing our datasets, we based on two fundamental sets of research questions.

Firstly, in which journals has research on innovation adoption, innovation adoption in SMEs, and

innovation exploitation in SMEs been published? Answer to this question provides elements to

evaluate the representativeness of our datasets, and to explain further results. Secondly, we tried to

understand the development of innovation adoption and innovation exploitation as fields of

research: what have been the most recurrent themes addressed within research on innovation

adoption, innovation adoption in SME’s and innovation exploitation in SME’s? Are there any

differences between the datasets?

The methods used to address these sets of questions are presented more extensively in the following

section.

3.1 Review process and technique adopted A systematic review method allows searching for assessing the field of studies, which is relevant to

specific research questions53. As Macpherson and Holt (2007:172)53 note, “the intent is to generate

collective insights through a meta-synthesis of findings thereby increasing methodological rigour

and developing a reliable knowledge base from which to take policy lessons and orient future

research (Tranfield et al., 2003).” Based on this premise, we attempted to address the

aforementioned research questions by using a variety of methods. More specifically and firstly, we

tested the representativeness of our datasets by straightforward counting the number of publications

that appeared across the defined time period (1989-2012), and of the journals in which they were

published. Second, the patterns we observed from counting and categorizing the papers were

supplemented by text analysis to provide an additional view on our datasets. In this way, we

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identified the most recurrent themes in both the overall innovation literature and the SME’s

innovation adoption and exploitation literature that emerged during the review process.

3.2 The content analysis method Content analysis has been defined as a systematic, replicable technique for compressing many

words of text into fewer content categories based on explicit rules of coding.54-56 We used text

analysis to gain further insight on the patterns observed from counting the number of publications

per year in the three datasets. As Lasswell et al. (1952: 45)57 observe, “Content analysis will not tell

us whether a given work is good literature; it will tell us whether the style is varied. It will not tell

us whether a paper is subversive; it will tell us if the contents change with party line. It will not tell

us how to convince the Russians; it will tell us what are the most frequent themes of Soviet

propaganda.”

In our case, we resorted to computer-supported text analysis as a useful technique to discover and

describe the focus of attention in three different literature streams.56 To this purpose, following De

Bakker et al.18, we used titles as text inputs for examining trends and patterns in the literature. Titles

from papers in our datasets were extracted and categorized in a spreadsheet that was then analyzed

using the content-analysis software package WordStat 6 (Provalis Research, 2010). This package is

specifically designed to process textual information such as responses to open-ended questions,

interviews, titles, journal articles, public speeches, and so on. We used the software to compute

word frequencies and map combinations of words across time, such that to trace emerging trends

and development in the field of inquiry. Doing a word-frequency count is one of the most common

assumption in content analysis.58 The underlying premise is that the words that are mentioned most

frequently are those that highlight the “hot topics” and greatest concerns.59 We thus generated a list

of word frequencies to compare the use of title words across several year groups.18 In order to avoid

common problems associated with word frequencies count, such as synonyms used for stylistic

purposes that may lead the researcher to underestimate the relevance of a concept,56 we performed

some pre-processing operations on our datasets.

As start, we did not analyze title words appearing in a low frequency. We decided to take as a cutoff

point a frequency of four, which means that we included in our analysis only those words that

appeared in titles four times or more. Then, we manually cleaned up the data by excluding

redundant words or words with little semantic value, such as prepositions, pronouns, conjunctions,

and so on. Once determining word frequency counts, we used a Key Word In Context (KWIC)

search to test for the consistency of usage of words. KWIC allows the researcher to isolate the

sentence in which that word was used so that she can see the word in some context. This procedure

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thus helps strengthen the validity of the inferences that are being made from the data.58 We

attempted to balance words exclusion with generalization concerns by also excluding all words

occurring in a number of cases less than the number of periods studied. This procedure allowed to

better pursuing our goal of making a comparison throughout years and between different datasets,

whilst balancing the need of maintaining a basic enough set of words with that of results’

generalization. To make generalization easier, some words were combined via a categorization

process.56 “SMEs” and “small and medium-sized firms,” for instance, were recoded to SME.

Indeed, in our quite special datasets, small and medium-sized firms can be regarded as one single

construct. After these pre-processing procedures, word frequency counts were determined for all

our datasets.

As a further extension of this content analysis, we also analyzed word or categories that tended to

appear together through WordStat hierarchical clustering method, and related dendrogram display

of keyword co-occurrence. The outcomes of word co-occurrencies have also been graphical

represented in network form by applying UCINET network software and Netdraw.60 Some of these

clusters will be analyzed and discussed in what follows.

4 Results

This section presents an in-depth analysis of the data through various bibliometric procedures. The

objective is to provide answer to our research questions. We discuss the number of papers that

appeared across time and the journals in which these papers were published. Results from content

analysis are also presented, which add to those based on straightforward papers count.

Number of papers. Figure 1 suggests that the innovation adoption literature in general and the

literature on innovation adoption in SMEs basically overlap until the early 2000s; only from that

period onwards, we can observe differences in the number of publications within the two fields,

with research in the SMEs domain hanging behind overall innovation adoption literature. The same

holds if one compares the publications counts in the innovation adoption literature with that relative

to the innovation exploitation in SMEs literature. This finding supports the validity of our decision

to compare research on innovation adoption and exploitation in SMEs with the more general

literature on innovation adoption. Conversely, one can observe how, in terms of number of studies,

research on innovation adoption and that on innovation exploitation in SMEs fundamentally

overlap, with a slight increase of scholarly interest on the topic of exploitation over innovation

adoption between 2008 and 2009.

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---------------------------- Figure 1 here ----------------------------

Figure 2, figure 3 and figure 4 show variations in the number of publications in the three datasets

over time, where the time interval is distributed over different periods. Comparison between these

figures reveals a similar trend in the number of publications in all the three datasets, with a decrease

in 1995-2000 and a considerable increase in the following period 2001-2006.

---------------------------- Figure 2, Figure 3, Figure 4 here ----------------------------

Journals. Our analysis also considers the concentration of research in the field of innovation

adoption in general and of innovation adoption and exploitation in SMEs. To this purpose, we

compared the journals in which the articles encompassed in our three datasets have been published.

Concerning the total 44 journals included within the analysis, it is evident (table 1) that the

specialist journals Journal of Management Information Systems, Management Science, Research

Policy and Strategic Management Journal have published the largest percentage of papers.

Nevertheless, there is a substantial overlap in the three datasets, and especially between those

concerning SMEs, with Technovation accounting for almost the same percentage of published

papers in both databases. Overall, the Innovation adoption & SMEs dataset appears more

concentrated than the Innovation exploitation & SMEs dataset, with 6 journals containing around

60% of all innovation adoption in SMEs papers (table 2).

---------------------------- Table 1 and Table 2 here ----------------------------

Content analysis on title words. For each of our three datasets, content analysis has been

performed on title words in order to identify word frequencies and co-occurrencies. We also used

network analysis software tools to graphically represent word pairs and the most central words in

the maps. To illustrate this procedure, results are reported for only the Innovation adoption (table 3)

dataset and the Innovation adoption & SMEs (table 4) dataset. We do not illustrate results for the

Innovation exploitation & SMEs dataset because the same patterns observed for the Innovation

adoption & SMEs dataset hold even for this dataset. As table 3 shows, the words associated to

innovation adoption increase over time, thus signalling that the literature on the topic is

progressively broadening. In particular, it might be interesting to underline the growing frequency

of the word “network effects,” which seems to suggest the raising attention of the literature toward

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the adoption implications of collaboration between firms, and between SMEs and large firms in

particular. A similar increase in theory-related terminology across time emerges from table 4 and

table 5. Interestingly, these tables highlight the higher frequency of the word “performance.” Yet,

such increase could be interpreted as a broadening of the literature toward a more comprehensive

analysis of the implications for firm performance of innovation adoption and exploitation,

especially when referred to SMEs.

--------------------------- Table 3, Table 4, Table 5 here ----------------------------

Analysis of word co-occurrencies. To further illustrate the development of central concepts in the

literature on innovation adoption in SMEs, figure 5 and figure 8 allow comparing, based on

Freeman’s (1979) degree centrality,61 the most frequent words in the Innovation adoption and

Innovation adoption & SMEs datasets. Again, we mainly focus on the latter dataset since for the

Innovation exploitation & SMEs the same patterns apply. From these figures, one can infer the

highest centrality of the two concepts “innovation” and “knowledge” in both fields of literature in

the period 1989-2012. Yet, figure 8 seems to validate our previous observation that the innovation

adoption literature in SMEs is more concerned with performance implications and the determinants

of orientation toward adoption. This pattern appears even more evident if one looks at word co-

occurrencies. Some variety emerges in the different word pairs contained in the two datasets. For

the Innovation adoption dataset we find, among the clusters having clear mutual relationships,

“innovation-adoption-technology-knowledge” and “innovation-adoption-SME-knowledge.” For the

Innovation adoption & SMEs dataset, instead, these clusters refer to “innovation-SME-

performance” and “innovation-orientation-performance.” Furthermore, in the general Innovation

adoption network, it can be seen that the number of word pairs is expanding over time. New

emerging themes, in the time period considered, seem to especially relate innovation adoption to

globalization issues (“global”), and again to network effects.

5 Discussion

Where do we go from here? At the beginning of this article, we made our expectations on a twofold

level: (1) to find a few central concepts shaping papers in our datasets and (2) to find a pattern of

overlap of existing concepts, rather than divergent terms and concepts in the literature on innovation

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adoption and exploitation in SMEs. Our results confirm both these expectations. Still, a number of

interesting observations can be made out of our current analysis.

5.1 Implications for innovation adoption theory Although earlier studies already accounted for the state of the art in the innovation adoption

literature, our analysis extends and complements these findings by using bibliometric methods and

by visualizing the areas/topics on which scholars have tended to focus their attention. In particular,

some significant implications for theory can be emphasized.

Firstly, we observe a decrease in the number of publications on innovation adoption in general for

the period 1995-2000, whereas a considerable increase in the following period 2001-2006. To

explain such increase one should consider that the highlighted trend might signal a real shift in the

interest on the topics of innovation adoption and innovation exploitation.

Secondly, the fact that all the three datasets overlap until 2000 is also meaningful. Indeed,

considering the different sizes of the Innovation adoption & SMEs and Innovation exploitation &

SMEs datasets, our results mean that, especially between 1997 and 2000, the two datasets might

hide a nested relationship, with the Innovation exploitation & SMEs dataset being part of the

Innovation adoption & SMEs dataset. Such nested relationship in turn suggests that, at least until

2000, research tends to be dominated by a strong focus on innovation adoption itself—e.g. on its

determinants62—rather than on innovation exploitation.

Thirdly, our results stimulate further understanding of why the number of publications in the three

datasets diverges after 2000. Although inquiring into the causes of this divergence is beyond the

scope of the present study, one might tentatively hypothesize that such a shift occurred in the last

ten years (2000-2012) due to a rising attention toward both the pursuit of innovation as something

vital to achieve competitive advantage and growth in dynamic, technology-based industry,63 and to

the relationship between firm size and the innovation adoption process.13 In terms of extant

innovation adoption literature, this shift has led to a lower interest in traditional models of

innovation adoption—e.g. Davis et al.’s23 TAM model—substituted by a growing focus on the

consequences of innovation adoption, such as its effects on firm growth, market value64, and

performance65. In terms of innovation exploitation, instead, scholars appear more and more willing

to provide evidence to conventional wisdom15, 66 that innovative activities in a previous period can

influence a firm’s future innovation activities by providing a knowledge base, which is able to

encompass an “absorptive capacity” toward technological competence from external sources.13

However, future research should seek further explanations for the changes we observed in our

datasets.

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5.2 Implications for managerial practice Our results have direct managerial implications.

First, our research delineates some factors that can be expected to foster or discourage innovation

adoption. These factors are largely controllable by managers and therefore can be altered to orient

innovation adoption decisions for their firms. In particular, we register the progressive emergence

of some concepts, which are related to the relationship between network “externalities” and firm

decisions to adopt an innovation and later exploit it. On the one hand, such increasing focus of

literature on network effects may signal the importance for managers to consider the advantages of

alliances or cooperative arrangements with partner firms, such as complementary resources and

expertise (eg. 67-70) or enhanced legitimacy of products and services (eg.71). On the other hand,

being aware of network effects may help managers understand how their firms are likely to react to

early adopters imitative pressures (e.g.72, 73) or face technology compliance needs (eg.74).

Second, our study warns managers to look at firm size as a critical determinant not only when

choosing to adopt an innovation, but also in deciding the innovation mode13 which is likely to affect

future prospects for firm growth. For example, motivated by the search for complementary

resources to successfully adopt and exploit an innovation, acquisitions might take place through

which small growing firms are acquired by larger firms, which aim at increasing their growth rate.

However, it has been shown that this kind of innovation adoption-related acquisitions impose a

limit on further growth of the resulting firm.75 Therefore, managers should be aware of these issues

related to firm size if they want to make appropriate innovation adoption decisions for their firms.

5.3 Limitations We acknowledge the presence of some limitations in our study, which however provide the

opportunity to advance the field beyond the confines of our exploration.

To begin with, one might notice that we mainly use titles in our analysis. Our choice to use titles is

motivated by the fact of being readily available in electronic form. However, in order to corroborate

our findings and reveal additional patterns, we encourage future research to extend content analysis

to also encompass abstracts and full papers.

Furthermore, it cannot be excluded that the observed patterns, i.e. the decrease in the number of

publications on innovation adoption between 1995-2000, followed by an increase between 2001-

2006, be the result of our data selection procedure in the event that the ABI/Inform Complete and

Ebsco databases are more comprehensive in the 2000s than in the 1990s. Consideration of longer

time periods might therefore represent a valuable extension of our analysis.

  15  

Conclusions The dominance of knowledge, orientation, performance, network effects as central concepts, and the

proliferation of the number of links with new still minority words signal that the field of innovation

adoption is vibrant and developing. Although it might be difficult to draw any strong conclusions

without extension of our analysis beyond its limitations, we can see the contribution of this paper as

being in further exploring and complementing the findings of earlier studies on innovation adoption,

innovation exploitation and SMEs by using bibliometric methods. Thanks to these methods we have

tried to uncover relevant features in the evolution of the literature on innovation adoption and

therefore in the intellectual structure of this research domain. We have shown how the latter have

become solidly integrated within the management science. However, on the other hand, research on

innovation adoption and exploitation in SMEs still needs to be expanded. Our results thus

eventually emphasize the potential of such a field of research to contribute to understanding SMEs’

behavior, performance and growth substantially more than it currently does.

  16  

Table1.  Number  of  articles  published  on  Innovation  adoption  between  1989-­‐2012  per  publication  source  

Innovation adoption

Journal No publications % of total Journal No publications

% of total

Academy of Management Journal 5 1,5% Journal of Marketing Research

16 4,9%

Academy of Management Review 8 2,5% Journal of Operation

Management

1 0,3%

Administrative Science Quarterly 6 1,9% Journal of Organizational

Behavior

2 0,6%

American Journal of Sociology 1 0,3% Journal of Politics 1 0,3%

Annual Review of Sociology 1 0,3% Journal of Product Innovation

Management

16 4,9%

Decision Sciences 7 2,2% Long Range Planning

4 1,2%

Econometrica 2 0,6% Management Science

30 9,3%

Engineering Management 2 0,6% Marketing Science 3 0,9%

Entrepreneurship Theory and Practice 2 0,6% MIS Quarterly 3 0,9%

Industrial and Corporate Change 23 7,1% Organization Science

6 1,9%

Information and Management 15 4,6% R&D Management 5 1,5%

Information Systems Research 8 2,5% Research Policy 32 9,9%

International Journal of Management Review 1 0,3% Strategic Management

Journal

32 9,9%

International Journal of Technology Management 9 2,8% Technovation 21 6,5%

Journal of Business Venturing 4 1,2% The Journal of Marketing

1 0,3%

Journal of Economic Literature 1 0,3%

Journal of Engineering and Technology 2 0,6%

Journal of Information Technology 1 0,3%

Journal of Management 1 0,3%

Journal of Management Information Systems 30 9,3%

Journal of Management Studies 18 5,6%

Journal of Marketing 5 1,5%

  17  

Table2.  Number  of  articles  published  on  Innovation  adoption  &  SMEs,  and  Innovation  exploitation  &  SMEs  between  1989-­‐2012  per  publication  source  

Innovation adoption & SMEs

Innovation exploitation & SMEs

Journal No publications

% of total

Journal No publications

% of total

Cambridge Journal of Economics 1 5,0% Decision Sciences 1 5,3%

Entrepreneurship Theory & Practice

1 5,0% Entrepreneurship Theory and Practice 1 5,3%

European Journal of Marketing 2 10,0% European Journal of Marketing 1 5,3%

Industrial Marketing Management 1 5,0% Industrial Marketing Management 1 5,3%

Information Systems Research 1 5,0% International Journal of Innovation Management

2 10,5%

Journal of Business Research 2 10,0% Journal of Business Venturing 1 5,3%

Journal of Business Venturing 2 10,0% Journal of Information Technology 1 5,3%

Journal of Knowledge Management

1 5,0% Journal of Management Research 1 5,3%

Journal of Management Studies 1 5,0% Journal of Management Studies 1 5,3%

R&D Management 1 5,0% Journal of Marketing 1 5,3%

Research Policy 1 5,0% Journal of Marketing Management 1 5,3%

Strategic Management Journal 2 10,0% MIS Quarterly 1 5,3%

Technovation 2 10,0% R&D Management 1 5,3%

The British Journal of Social Psychology

2 10,0% Research Policy 1 5,3%

Strategic Management Journal 1 5,3%

Technovation 2 10,5%

The British Journal of Social Psychology 1 5,3%

   

 

 

 

 

 

 

 

 

 

 

 

 

 

   

  18  

Table3.  Word  Frequencies  Innovation  adoption  dataset  per  Year  Group  

1989-1994 1995-2000 2001-2006 2007-2012 INNOVATION 10 7 54 48 TECHNOLOGY 5 4 51 38 KNOWLEDGE 7 5 43 28 ADOPTION 4 2 39 36 RESEARCH 7 4 35 34 NETWORK_EFFECTS 4 4 27 24 DIFFUSION 12 4 22 20 FIRM 1 2 18 24 ORGANIZATION 3 3 12 14 THEORY 7 2 9 10 GLOBAL 1 1 14 10 PRODUCT

13 11

MANAGEMENT

1 10 12 PERFORMANCE

8 14

DETERMINANTS 2

10 7 INDUSTRY

1 9 9

STRATEGY 1 1 11 6 CAPABILITIES 2

11 4

MARKET 1

12 3 ROLE 1

10 4

DYNAMICS

10 4 BUSINESS 1

6 4

SME

6 5 IMPLEMENTATION 3

5 2

SYSTEMS 1

6 2 ELECTRONIC 2

3 3

SOFTWARE 1 1 5 1 HETEROGENEITY

2 5

INDIVIDUAL 1 1 4 1 CHANGE 1

2 3

 

Table4.  Word  Frequencies  Innovation  adoption  &  SMEs  dataset  per  Year  Group  

1989-1994 1995-2000 2001-2006 2007-2012 INNOVATION 2 1 8 6 PERFORMANCE 1

6 6

FIRM 1 1 3 2 KNOWLEDGE 1

4 1

ORIENTATION

4 2 RELATIONSHIP 1

2 3

SME

1 1 4

Table5.  Word  Frequencies  Innovation  exploitation  &  SMEs  dataset  per  Year  Group  

1989-1994 1995-2000 2001-2006 2007-2012 INNOVATION 1 1 7 6 SME 1 5 5 FIRM 1 1 3 3 PERFORMANCE 1 3 3 KNOWLEDGE 2 2

  19  

 Figure  1.  Number  of  publications  Per  Year,  Innovation  adoption,  Innovation  adoption  &  SMEs,  Innovation  exploitation  

&  SMEs  

Figure  2.  Number  of  publications  Per  Year  Group,  Innovation  adoption  dataset

0 5

10 15 20 25 30 35 40 45

Innovation adoption Innovation adoption & SMEs Innovation exploitation & SMEs

0 50 100 150 200

1989-1994

1995-2000

2001-2006

2007-2012

No of studies

  20  

Figure  3.  Number  of  publications  Per  Year  Group,  Innovation  adoption  &  SMEs  dataset  

             

 Figure  4.  Number  of  publications  Per  Year  Group,  Innovation  exploitation  &  SMEs  dataset  

                       

0 2 4 6 8 10 12

1989-1994

1995-2000

2001-2006

2007-2012

No of studies

0 2 4 6 8 10 12

1989-1994

1995-2000

2001-2006

2007-2012

No of studies

  21  

Figure 5. Title word degree centrality, Innovation adoption, 1989-2012  

Figure 6. Word pairs frequencies, Innovation adoption, 1989-2012  

 Note: Jaccard's coefficient - This coefficient is computed from a fourfold table as a/(a+b+c) where a represents cases where both items occur, and b and c represent cases where one item is found but not the other. In this coefficient equal weight is given to matches and non matches.

  22  

Figure 7. Word pairs grouped by closeness, Innovation adoption, 1989-2012    

    Figure 8. Title word degree centrality, Innovation adoption & SMEs, 1989-2012  

     

  23  

Figure 9. Word pairs frequencies, Innovation adoption & SMEs, 1989-2012    

 Note: Jaccard's coefficient - This coefficient is computed from a fourfold table as a/(a+b+c) where a represents cases where both items occur, and b and c represent cases where one item is found but not the other. In this coefficient equal weight is given to matches and non matches.      Figure 10. Word pairs grouped by closeness, Innovation adoption & SMEs, 1989-2012  

 

  24  

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