Computers in Human Behavior 25 (2009) 444–449

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Computers in Human Behavior

journal homepage: www.elsevier .com/locate /comphumbeh

An assessment of organizational size and sense and response capability on theearly adoption of disruptive technology

Gary Garrison *

Information Systems Management, College of Business Administration, Belmont University, 1900 Belmont Boulevard, Nashville, TN 37212, USA

a r t i c l e i n f o

Article history:Available online 20 November 2008

Keywords:Disruptive technologyTechnological opportunismOrganizational size

0747-5632/$ - see front matter � 2008 Elsevier Ltd. Adoi:10.1016/j.chb.2008.10.007

* Tel.: +615 460 5440; fax: +615 460 6455.E-mail address: garrisong@mail.belmont.edu

a b s t r a c t

This study used the results from a survey of 73 senior executives, representing their Fortune 1000 or INC500 firm, to better understand the relationship between an organization’s size and its technological-sens-ing and response capability on the early adoption of disruptive technology. Results indicated that anorganization’s size, measured by number of employees and annual sales, was shown to be an impedimentto an organization’s response capability despite its increased capability to sense the technology earlierthan smaller firms.

� 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Increasingly, emergent advances in technology are threateningto disrupt established products and markets, creating an atmo-sphere of turmoil and difficult decision-making for organizations(Borck, 2002). Such technologies, defined as ‘‘disruptive technolo-gies,” continue to reshape the manner in which organizations oper-ate, thereby increasingly making the strategic use of informationtechnology an even more critical issue facing organizations. Chris-tensen (1997) argues the organizations that have succeeded in thetransition toward disruptive technologies have ignored the charac-teristics and capabilities of existing technologies and sought to de-velop new ways in which a disruptive technology could be used. Incontrast, those organizations that were toppled by a disruptivetechnology failed to recognize the technology’s long-term, wealthcreating potential and did not proactively search for ways to createvalue with the technology.

Recent technologies such as Voice over Internet Protocol (VoIP)and open-source software have provided organizations alternativeavenues for pursuing their strategic initiatives over existing tech-nologies. Following the theoretical underpinnings of the re-source-based view of the firm (RBV), technological resourcesalone are unlikely to provide organizations with sustained compet-itive advantage (Mata, Fuerst, & Barney, 1995; Tallon, 2008), there-fore, organizations need to develop organizational capabilities tobetter sense and respond to new technological developmentsahead of their competitors if they are to remain viable in today’stechnology-driven environment.

Research has shown technology to affect organizational perfor-mance via firm specific resources designed to provide those orga-

ll rights reserved.

nizations with an ability to respond to technological changes(Sambamurthy, Bharadwaj, & Grover, 2003). Such firm specific re-sources may include an organization’s ability to sense and respondto technological changes in a timely fashion. To this end, this paperlooks at an organization’s size and its technological-sensing and re-sponse capabilities as factors that may impact the early adoption oftwo potentially disruptive technologies, VoIP and open-sourcesoftware. Examining these factors address the research questions:Are technology opportunistic organizations early adopters of dis-ruptive technology? and How does an organization’s size impactits sensing and response capabilities? A review of the managementliterature on strategy and change provided the theoretical frame-work as to why some organizations are more likely to embracedisruptive technologies as an attempt to remain viable in a hy-per-competitive environment.

2. Background

2.1. Disruptive technologies

A disruptive technology is a radically new scientific discoveryproviding organizations the capability and/or foundation for alter-ing the business environment by destroying the value of existingtechnical competencies and in the process, creating new businessmarkets (Christensen, 1997). At the opposite end of the continuumare technologies classified as sustaining technologies; or technolo-gies that offer incremental improvements over technologies al-ready in existence. A major distinction between the two forms oftechnology is that as one moves along the continuum from a sus-taining to a disruptive classification, there is far greater uncertaintyas to the value of a disruptive technology than there is for asustaining technology. In other words, inherent with disruptive

G. Garrison / Computers in Human Behavior 25 (2009) 444–449 445

technologies is a degree of new knowledge embedded within thetechnology making it harder to understand the technology’s valueand uses (Dewar & Dutton, 1986).

Few technologies are ever classified as disruptive; as most new-ly introduced technologies are of the sustaining type. Disruptivetechnologies, however, represent fundamental changes in technol-ogy that bring about revolutionary change signifying clear depar-tures from existing practice (Duchesneau, Cohn, & Dutton, 1979;Ettlie, 1983). Research demonstrates that disruptive technologieshave different key success factors than sustaining technologies;therefore, managers are likely to differ in their evaluation andadoption of these technologies based on their level of familiarityand experience in assessing technologies.

To this point, Christensen (1997) postulates that during times ofdisruptive transitions, the organizations that have succeeded ig-nored the characteristics and capabilities of existing technologyand sought to develop new ways in which a potentially disruptivetechnology could be used. In contrast, the downfall of organiza-tions could be traced back to poor decision-making with respecttoward disruptive technology adoption. As such, the organizationsthat were toppled by disruptive technologies took a less aggressiveapproach to technology adoption and continued with the estab-lished technology and did not proactively search for new ways tocreate value with the potentially disruptive technology.

Research shows that sticking with a proven technology hasbeen noted as being a plausible cause for organizational failureduring times of disruption in that some organizations view apotentially disruptive technology and see an innovation that ap-pears insignificantly small and cannibalistic to their existing infor-mation technology infrastructure (Christensen, 2003). In contrast,continuing with a sustaining technology or existing technologyare viewed as being safe. Future-oriented organizations, however,view potentially disruptive technology and recognize opportunityfor new growth. The two opposing views is due in part to the factthat many organizations make a judgment error in assuming thatinvesting early and aggressively in disruptive technologies is anirrational financial decision since the technology’s value andwealth creating potential are unknown; whereas, more technolog-ically opportunistic organizations assess the same technology andrecognize transformational opportunities (Christensen, 1997).

Researchers have reported that organizational survival dependson its ability to make rapid and continuous changes to keep pacewith technological advances (Brown & Eisenhardt, 1997). A ‘readi-ness for change’ has been considered a strategy to change processin that the primary mechanism for creating ‘readiness’ is a corpo-rate culture that embraces an ability to change as prescribed by se-nior management (Armenakis, Harris, & Massholder, 1993).Further, Armstrong (1993) suggests that organizational leadersneed to be flexible in their ability to adapt and respond quicklyto a changing environment. These change agents invoke a techno-logically opportunistic mindset that shapes a culture into one thatis proactive and receptive to change possibly increasing the likeli-hood of successful adoption of a disruptive technology as employeeresistance would be lessened.

2.2. Technological opportunism

Technological opportunism is an organizational trait providingfirms the capability to sense and respond to new technologies inthe anticipation of creating sources of competitive advantage (Srin-ivasan, Lilien, & Rangaswamy, 2002). As this definition implies,there are two distinct dimensions to technological opportunism.The first dimension details a technological-sensing capability –the extent to which an organization has the capability to acquireknowledge about and understand new technological develop-ments. The second dimension is an organization’s technological-

response capability – the extent to which an organization is willingand able to respond to new technological information it senses inthe environment that it believes may affect its competitive posture(Srinivasan et al., 2002).

Technologically opportunistic organizations are proactive intheir search for innovations in that they tend to explore severalnew technologies they deem to be potential opportunities orthreats to their competitive posture. The more technologicallyopportunistic organizations are, the more likely they would proac-tively search for and respond to disruptive technologies. Cannibal-ization is one type of response a technologically opportunisticorganization can take. Cannibalization usually requires an organi-zation to reallocate resources, develop new procedures and rou-tines, and alter its organizational strategy. Since, disruptivetechnologies have the potential to destroy core competencies andrender obsolete costly investments that have accrued over theyears, in times of disruption, a critical concept separating success-ful organizations from faltering ones is a successful organization iswilling to cannibalize its own investments.

Studies show the managers, who have rejected disruptive tech-nologies, did so because they wanted to remain focused on theircurrent business strategy (Charitou & Markides, 2003) and wereunwilling to cannibalize their existing information technologies.Because organizations adopt emerging technologies to help itachieve competitive advantage (Porter, 1985), organizations thatare more technologically opportunistic may ultimately realizesuperior organizational performance than those organizations thatdo not share this same trait. Therefore, an organization’s techno-logical opportunism could be a good indicator of its ability to thriveamidst a disruptive technological change.

Hypothesis 1. An organization’s technological-sensing capabilitysignificantly influences its propensity to be an early adopter ofdisruptive technology.

Hypothesis 2. An organization’s technological-response capabilitysignificantly influences its propensity to be an early adopter of dis-ruptive technology.

2.3. Organizational size

Organizational size may be an important factor in the earlyadoption of disruptive technology, as it provides an organizationwith more opportunities to discover an innovation. Such may bethe case since organizations are connected through a web of net-works which influences their behavior (Pavitt, 1994). Further, anorganization’s network provides it an extended view allowing afirm to better disseminate and receive information (Ibarra, 1993).Tushman (1977) suggests larger organizations are comprised of agreater number of individuals who interact with the external envi-ronment, thus providing it a greater number of pathways for newinformation to diffuse. Kelley and Helper (1999) suggest that largerorganizations are better positioned to acquire new informationbased on their embeddedness within a larger, more diverse net-work. To this end, the greater number of individuals employed ata single firm, the more opportunities it will have to discover anew technology. Therefore,

Hypothesis 3a. An organization’s size positively influences itstechnological-sensing capability.

Contrary to previous research that found a positive relationshipbetween organization size and technology adoption (Gopalakri-shan & Damanpour, 2000; Liberatore & Breem, 1997), Yeaple(1992) found that smaller organizations are more efficient than lar-ger organizations when adopting various technologies. Similarly,as organizations grow in size, there is a tendency to be more struc-

Fig. 1. A conceptual model of organizational size and technological opportunism onthe early adoption of disruptive technology.

Table 2Respondents by industry and business area.

Industry MFG WHSLR RET SVC Total

Automotive 2 2 4Software development 8 5 13Department store 2 2Networking 14 14Pharmaceuticals 1 1 1 3Insurance 1 1 2Medical equipment 2 1 1 4Construction 1 4 5Staffing 4 4Consulting 2 2Records management 6 6Finance and banking 1 1Technology 1 1 3 5Communications/Media 4 4Advertising/Marketing 2 2Appliance 1 1Other 1 1Total 17 1 7 48 73

446 G. Garrison / Computers in Human Behavior 25 (2009) 444–449

turally rigid and less flexible in operations (Rogers, 2003), poten-tially constraining the ability of managers to innovate (DeTienne& Koberg, 2002). Operational inflexibility may be due to largefinancial outlays for the existing resources and the severity ofchange needed to adopt a technology (e.g., workplace practices,policies and rules). Therefore,

Hypothesis 3b. An organization’s size negatively influences itstechnological-response capability.

Fig. 1 depicts the before mentioned constructs and their pro-posed relationships. While the literature provides evidence ofother organizational factors impacting an organization’s adoptiondecision (e.g., role of champions, organizational culture, perceivedusefulness, perceived ease of use, etc.), this research focused moreon the discrepancies found in the literature with respect towardorganizational size and technology adoption. Specifically, this re-search assessed the impact organizational size has on early adop-tion via an organization’s sensing and response capability. Fig. 1illustrates technological opportunism and a two-dimensioned con-struct as previously defined. Dimension one is an organization’ssensing capability and dimension two serves and the responsecapability.

3. Research design

An online survey methodology was employed using key infor-mants for data collection. To maximize variation in the variablesand generalization, the study was not restricted to one specificindustry or one type of disruptive technology. Both Fortune 1000and INC 500 firms were targeted for study, since IT investmentsand implementations represent significant activities. Senior execu-tives served as key informants for data collection and were ob-tained from D&B Million Dollar Database, Mergent database, andInc.com. Random sampling was used to control for industry fac-

Table 1Breakdown of survey respondents.

Business area Mailed/Phoned/E-mailed Received Totalsraw %

Initialsample

Badcontacts

Totals Wave1

Wave2

Wave3

Manufacturers 150 3 147 3 0 14 1711.33%

Wholesale 10 1 9 0 0 1 11.00%

Retail 40 2 38 1 0 6 715.00%

Service 200 8 192 14 9 25 4824.00%

Totals 400 14 386 18 9 46 7318.25%

tors; however, the degree of response bias among various firmsand industries is unknown.

The total sample size was 400 randomly selected companieshalf of which were INC 500 firms. Seventy-three participants com-pleted the survey yielding an 18.25% response rate. Missing valuesin the data set among the 73 respondents were replaced with themean. Table 1 shows that key informants were contacted in threewaves one week apart. Eighteen organizations were represented inthe first wave. Nine and 25 organizations were represented inwaves two and three, respectively. Approximately, 17 differentindustries at four levels are reported (see Table 2). Therefore, itcan be inferred that industry bias should not be an issue in dataanalysis with the exception of the wholesale industry.

The two potentially disruptive technologies surveyed includedVoIP and open-source software. VoIP is a technology for transmit-ting voice, fax, and other information over the Internet instead ofusing a standard public switched telephone network. It is consid-ered a disruptive technology because it provides more featuresand customization options than circuit switched technology, it cur-rently has no access fee, and it has the ability to work around nodefailure or congestion (Bischoff, 2002). Open-source software is freesoftware developed and improved through public collaboration. Itis free in the sense that individuals have unrestricted access to thesource code. Open-source software is considered disruptive be-cause it challenges conventional thinking with regard to how soft-ware should be created and sold (Jackson, 2004). Respondentswere asked to rate the two technologies presented to them inthe survey based on its perceived level of disruptiveness. Resultsindicated both technologies (VoIP = 5.26 and open-source = 5.34)were rated above the neutral point of 4.0 and were considereddisruptive.

3.1. Measures

3.1.1. Early adoptionEarly adoption served as the dependent variable and was as-

sessed by having respondents indicate whether or not their organi-zation had adopted and were using, rejected, or had not discoveredthe technology; and if they adopted, the date of adoption. For thepurpose of this study, early adoption was defined as the top 16%of the organizations that have adopted the technology and wereactually using it. These early adopting organizations have a ten-dency to be labeled as innovators and early adopters accordingto Roger’s (2003) five adopter categories. At the other end of theadoption continuum are those organizations that are not earlyadopters (not among the top 16% of early adopters, rejected, or

Table 3Individual item reliability and composite reliability of variables.

Construct Item Weight IIR loading Composite reliability

Technology sensing TS1 0.471 0.943 0.889TS2 0.389 0.823TS3 0.281 0.834

Technology response TR1 0.382 0.968 0.931TR2 0.321 0.915TR3 0.355 0.947

Early adoption EA1 0.395 0.951 0.883EA2 0.389 0.907

G. Garrison / Computers in Human Behavior 25 (2009) 444–449 447

not discovered the disruptive technology), defined as nonadoptersin the data analysis. For purposes of data analysis, early adopterswere coded with a 1 and nonadopters were coded with a 0.

3.1.2. Technological opportunismTechnological opportunism was assessed using a two-dimen-

sion construct adopted from Srinivasan et al. (2002) whoreported a coefficient alpha of 0.89. Technological opportunism in-cluded: Technological-sensing capability – the extent to which anorganization has the capability to acquire knowledge about andunderstand new technological developments and Technological-response capability – the extent to which an organization is willingand able to respond to new technological information it senses inthe environment that it believes may affect its competitive pos-ture. Respondents entered their level of agreement to these itemson a 7-point Likert-type scale ranging from 1 (strongly disagree)to 7 (strongly agree). Items were summed and averaged to givean overall score.

3.1.3. Organizational sizeOrganizational size was hypothesized to be positively related to

an organization’s technology-sensing capability and negatively re-lated to its technology-response capability. Organizational size wasmeasured using two items: the organization’s annual sales for themost recent year and number of employees. Companies with salesless than $10 million were treated as small, those with sales be-tween $10 and $249 million as medium, and those with sales of$250 million or more as large (coded as 0, 1, and 2, respectively)(Drury & Farhoomand, 1999). Following Boden (1997) and Wolffand Pett (2000), a small organization has between 1 and 500employees and a large organization has over 500 employees (codedas 0 and 1, respectively).

Table 4AVE and correlation of latent variables.

AVE Tech sensing Techresponse

Org size Earlyadoption

Tech sensing 0.731 0.855Tech response 0.818 0.526 0.904Org size 0.849 0.635 �0.448 0.921Early adoption 0.779 �0.270 0.718 �0.332 0.883

*The italic elements in the matrix are the average variance extracted scores gen-erated by PLS Graph 3.0.

4. Data analysis

PLS Graph 3.0 was used to validate the instrument and test thehypothesized relationships among the constructs. The advantagesof PLS include its applicability for small sample sizes (Chin,1998) and that it simultaneously tests both the psychometric prop-erties of the scale items and analyzes the strengths and directionsof the relationships among variables (Lohmöller, 1989). A two-stepapproach was used to first evaluate the measurement model andthen test the structural model. The measurement model was eval-uated to confirm the relationships between the scale items andeach related construct, which in effect is an evaluation of the reli-ability and validity of the survey instrument. The structural modelevaluates the hypothesized relationships between constructs byexamining the significance of the path loadings among the vari-ables (Chin, 1998).

4.1. Measurement model analysis

Individual item reliability was assessed by evaluating the indi-vidual item loadings or simple correlations of the measures as theyrelated to each construct. Following Carmines and Zeller (1979),item loadings of 0.707 or greater were accepted as this impliedmore than 50% of the variance in the observed variable was sharedwith the construct (Barclay, Higgins, & Thompson, 1995). Itemswith reliability loadings below the minimum threshold weredropped. Fortunately, all items were retained as they met the min-imum threshold for individual item reliability (see Table 3).

Composite reliability was recommended to assess the reliabilityof the reflective measurement [6] Composite reliability is similarto Chronbach’s alpha in that Chronbach’s alpha presumes, a priori,that each item measuring a single construct contributes equally

(Barclay et al., 1995). With PLS, composite reliability is calculatedby using the item loadings estimated within the causal modeland is not influenced by the number of items in the scale (Fornell& Larcker, 1981). PLS Graph 3.0 generated composite reliabilityscores for each construct with values of 0.7 or greater being consid-ered adequate (Nunnally, 1978). Table 3 shows the composite reli-ability scores for the tested constructs ranged between 0.889 and0.934 providing evidence for sufficient reliability.

The final measurement model analysis was in determiningwhether the constructs had adequate discriminant validity. Dis-criminant validity indicates the degree to which one construct isdifferent from all other constructs in the instrument and is evalu-ated using the average variance extracted (AVE) statistic (Fornell &Larcker, 1981). Discriminant validity is adequate when constructshave an AVE loading greater than 0.5, which means that at least50 percent of measurement variance was captured by the con-struct (Chin, 1998). All constructs demonstrated AVE scores greaterthan the 0.5 recommended minimum, thereby providing evidenceof discriminant validity. Further, discriminant validity is evidencedby larger values for the square root of the AVE, shown in bold alongthe diagonal in Table 4, compared to the correlations among thefactors (Chin, 1998).

4.2. Structural model analysis and hypotheses testing

Following measurement model assessment, the structural mod-el was evaluated to test the relationships between the constructs.PLS Graph 3.0 yielded two critical pieces of information, whichindicated how well the structural model predicted the hypothe-sized relationships. The first piece of information was the squaredmultiple correlation (R2) for each endogenous construct. This num-ber measured a construct’s percentage variation explained by themodel (Wixom & Watson, 2001). Results showed OrganizationalSize explained 36.4% of an organization’s Technological-SensingCapability and 12.8% of an organization’s Technological-ResponseCapability, while Technological Opportunism explained 59.7% ofa firm’s early adoption of VoIP and open-source software (seeFig. 2).

The second piece of information provided by PLS Graph 3.0 wasthe path coefficients, which indicated the strength of the relation-ship between two constructs (Wixom & Watson, 2001). PLS Graph3.0 uses a bootstrap procedure with 200 resamples to calculate the

Fig. 2. A tested model of organizational size and technological opportunism on theearly adoption of disruptive technology.

448 G. Garrison / Computers in Human Behavior 25 (2009) 444–449

path coefficients. Fig. 2 illustrates the structural path diagram,which provided evidence to support three out of the fourhypotheses.

Hypothesis 1 stated a positive relationship between an organi-zation’s Technological-Sensing Capability and early adoption ofVoIP and open-source. The negative path coefficient between thesetwo variables (�0.104) caused to reject H1. Hypothesis 2 stated apositive relationship between Technological-Response Capabilityand the Early Adoption of VoIP and open-source software. Thepath coefficient (0.718) supported H2. Organizational Size washypothesized to be positively related to an organization’s Techno-logical-Sensing Capability (H3a) and negatively related to its Tech-nological-Response Capability (H3b). Organizational Size had apositive path coefficient (0.431) between it and Technological-Sensing and a negative path coefficient between it and Technolog-ical-Response Capability (�0.317). Therefore, H3a and H3b weresupported (see Fig. 2).

5. Discussion of results and conclusion

Results indicate organizational size (assessed in terms of annualsales and number of employees) was an impediment to the earlyadoption of VoIP and open-source software. With respect towardssize, ironically, it was the INC 500 firms that reported a greatercapability to respond (adopt) to each of these potentially disrup-tive technologies than did the Fortune 1000 firms. One possibleexplanation is that many of the INC 500 companies are in the ser-vices industry, such as software development and consulting.Therefore, their capital expenditures are much smaller comparedto Fortune 1000 firms (per employee), which tend to operate inmore capital-intensive industries. Also, INC 500 firms do not facethe challenges of altering their existing strategies, routines, andprocesses that support the functioning of their core competencies;at least on the magnitude of Fortune 1000 firms. Therefore, evi-dence suggests it is easier for smaller firms to respond to a disrup-tive technology.

This finding leads credence to the notion that over time, largerorganizations tend to become more structurally rigid, potentiallyconstraining the ability of managers to adopt disruptive technolo-gies. Additionally, larger organizations could possibly face greaterchallenges altering their existing strategy in favor of implementinga new one; not to mention the difficulty in allocating the necessaryresources required to supplant existing routines and processes thatsupport the functioning of their core competencies.

Results demonstrate Fortune 1000 firms had a greater ability tosense a disruptive technology than did the INC 500 firms. However,the data indicate a negative relationship between sensing capabil-ity and early adoption further highlighting the difficulties largerorganizations experience in changing direction.

This finding suggests the mere sensing of a potentially disrup-tive technology does not lead to its early adoption. Although a firm

discovered a potentially disruptive technology, it may have beenunable to respond due to a lack of resources required to adoptthe technology early, faced employee resistance, or was unwillingto change its established routines or procedures at the cost of rede-fining and/or restructuring their existing ones.

There is little doubt that adopting a disruptive technology hasthe potential to generate high-risk and high-reward for the adopt-ing organization. However, adoption alone could trigger majororganizational changes and employee resistance if the technologyis misaligned with the corporate culture; as there exists the poten-tial for significant transformations in business processes andemployees’ job functions. Therefore, it is important for senior man-agement to develop a technologically opportunistic culture inwhich the people who are affected by the change are preparedand willing to embrace the change rather than resist it.

6. Limitations

As with any empirical research, there are limitations to thisstudy. First, a cross-sectional design is a limitation, because resultsare usually more robust if an observation is made over time, ratherthan a snapshot in time. Second, an individual’s experiences andresulting decision-making processes change over time, therefore,this study does not reflect any future decisions made by therespondent. Additionally, this study does not capture an organiza-tion’s future growth or how its recent adoption decisions will influ-ence its long-term viability. Fourth, the use of a key informant tocollect data is a limitation, because a single individual may notbe representative of an entire organization. However, the use of se-nior managers should negate this limitation as they are in a posi-tion of authority and make many strategic decisions.

Sample size is a limitation, in terms of providing adequate sta-tistical support for rejecting or failing to reject the hypotheses andin its ability to detect a true difference between organizations. Sim-ilarly, 73 organizations cannot fully represent all of the differingtypes of organizations operating in various industries. Therefore,any generalizations made about this study should go little furtherthan the organizations having similar characteristics to those firmsthat responded to the survey. Finally, these limitations and otherconsiderations suggest replicating the research for further valida-tion and generalization.

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