ARTICLE IN PRESS - freescholar.yolasite.com · Inthisstudy,wecontributetoCEliteraturebyinvestigatingtheeffectsofCEondisruptiveBMIadoptionandtheeffects ofdisruptiveBMIadoptiononbusinessmodelperformance

Corporate Entrepreneurship, Disruptive Business ModelInnovation Adoption, and Its Performance: The Case of theNewspaper Industry ☆

Jahangir Karimi, Zhiping Walter

Recently, Internet and digitization, along with major news and information companies, have disrupted traditional newspaper compa-nies’ business models, and raised serious concerns about the future viability of the print newspaper industry. This study provides a theoreticalviewpoint, supported by empirical evidence from the newspaper industry, on how prominent corporate entrepreneurship attributes impactdisruptive business model innovation adoption, and how such adoption impacts business model performance. It finds that, while auton-omy, risk-taking, and proactiveness do have positive associations with the extent of adoption of disruptive business model innovation,innovativeness does not. Further, disruptive business model innovation adoption has a nonlinear association with business model per-formance. We conclude the paper by discussing theoretical implications of the study and by providing strategies that entrepreneurs andtechnology managers can use to adjust their corporate entrepreneurship activities in their effort to successfully adopt disruptive busi-ness model innovation.

© 2015 Elsevier Ltd. All rights reserved.

Introduction

Recently, Internet and digitization along with major news and information companies have collectively disrupted tra-ditional newspaper companies’ business models. Subsequently, many have raised serious concerns about the future viabilityof the print newspaper industry (Clemons et al., 2002/3). Some industry analysts are predicting a complete eventualcollapse of the century-old newspaper industry (Dumpala, 2009; Kiss, 2005)1. After analyzing many industries facing dis-ruption, Christensen (2006) has pointed to the fundamental challenge of disruptive technologies as “a business model problem,not a technology problem” (p. 48)2. Since disruptive products and services typically promise lower profit margin than theexisting ones, they create a conflict between the business model that is already established for existing technology and theone that may be required to exploit the emerging disruptive technology (Christensen and Raynor, 2003). Although the rolesof business model innovation (BMI) adoption in opportunity-seeking behavior (Chesbrough, 2010; Dewald and Bowen, 2010;Doz and Kosonen, 2010; George and Bock, 2011), and in dealing with organizational challenges of the incumbent firms inresponse to disruptive innovations, have been emphasized in the past (Lucas, 2012), little work has been done to under-stand how disruptive BMI adoption3 is influenced by corporate entrepreneurship (CE) (Dewald and Bowen, 2010; Georgeand Bock, 2011) or how such adoption can impact business model performance. Such understanding is important in de-veloping a strategy for survival and in making and executing management decisions to respond to disruption (Lucas, 2012).

☆This research was supported in part by a grant from the Jake Jabs Center for Entrepreneurship at the Business School, University of Colorado Denver.1 World Association of Newspapers and News Publishers (WAN) reported this in July 2010: “Since 2008, more than 166 newspapers in US have closed

down and stopped publishing a print edition. More than 39 titles did so in 2008, and the number rose to 109 in 2009. So far in 2010, more than 18 papershave closed down or stopped publishing a print version. There have been nearly 35,000 job losses or buyouts in the U.S. newspaper industry since March2007. From March to December 2007, more than 2,256 newspaper jobs have been reportedly eliminated or offered buyouts. The numbers increased tomore than 15,992 in 2008 and were at more than 14,783 in 2009. As of May 2010, there have been more than 1,797 job losses or buyouts in newspapercompanies in the country,” (http://www.sfnblog.com/industry_trends/2010/07/sfn_report_more_than_166_us_newspapers_h.php).

2 This is because, compared to commercializing sustaining technologies, commercializing disruptive products and services is an increasingly costly en-deavor with greater uncertainty, longer time for significant commercial success, and the possibility of disrupting the markets of existing profitable productsand services (Walsh et al., 2002).

3 In this context, adopting disruptive BMI or disruptive BMI adoption means developing and implementing new business models suitable for respondingto disruptive innovations.

http://dx.doi.org/10.1016/j.lrp.2015.09.0040024-6301/© 2015 Elsevier Ltd. All rights reserved.

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Please cite this article in press as: Jahangir Karimi, Zhiping Walter, Corporate Entrepreneurship, Disruptive Business Model Innovation Adoption, and Its Perfor-mance: The Case of the Newspaper Industry ☆, Long Range Planning (2015), doi: 10.1016/j.lrp.2015.09.004

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In this study, we contribute to CE literature by investigating the effects of CE on disruptive BMI adoption and the effectsof disruptive BMI adoption on business model performance. Although the need for further studies to find conditions underwhich CE is particularly beneficial or detrimental to performance has been recognized in the past, the vast majority of priorstudies have investigated the direct effects of CE on firm performance (Miller, 2011; Rauch et al., 2009)4. However, priorresearch has suggested that firm performance is dependent upon its business model characteristics (Zott and Amit, 2010).Further, BMI mediates the link between technology innovation and firm performance (Baden-Fuller and Haefliger, 2013).Therefore, a richer understanding of how CE affects performance requires understanding the role of BMI adoption in thisrelationship, especially when firms are faced with digital disruption. We intend to provide this understanding by investi-gating whether and how a firm’s being entrepreneurial — which is defined by prominent CE attributes — affects BMI adoptionand how BMI adoption affects business model performance. Such an investigation is important because, from a theoreticalperspective, the impact of prominent CE attributes on the extent of disruptive BMI adoption has never been empiricallytested in the past. Further, from a managerial perspective, entrepreneurs or technology managers need to learn how to adjusttheir CE activities in order to succeed in adopting disruptive BMI. To develop a full understanding of how prominent CEattributes affect business model performance through BMI adoption in response to digital disruption, we integrate disrup-tive BMI concepts into CE theoretical framework. We focus on the newspaper industry as an information intensive industryto provide an empirical context for our research model. According to Miller (2011), there is a need for richer characteriza-tion of entrepreneurial research through context specific studies because these studies “may enhance application and generatemore fine-grained and more empirically valid knowledge … may be of great interest to practitioners and scholars alike.”(p. 881). In addition, focusing on one industry gives us the empirical context in which to develop measurements for dis-ruptive BMI adoption and for business model performance. Two research questions are addressed: what are the prominentCE attributes that impact disruptive BMI adoption? And to what extent does disruptive BMI adoption influence businessmodel performance? This study, therefore, makes two important contributions. First, it develops 1) measurement scalesfor those prominent CE attributes that facilitate disruptive BMI adoption; 2) measurement scale for the extent of disrup-tive BMI adoption; and 3) measurement scale for business model performance. Second, it conducts an empirical study toexamine how prominent CE attributes impact disruptive BMI adoption and how disruptive BMI adoption influences busi-ness model performance.

Below we provide the theoretical framework for the study, followed by hypotheses development. In the Method section,we explain why disruptive innovations pose BMI adoption challenges for newspaper companies so as to provide the ground-ing for our empirical analysis. We then describe an empirical study designed to test the proposed research model using asample data from the newspaper industry. We discuss new measurement scales for prominent CE attributes for disruptiveBMI adoption, for extent of disruptive BMI adoption, and for business model performance. Finally, we present the results ofdata analyses and the study’s implications, limitations and conclusions.

Theoretical framework

Prior research on disruptive innovations has suggested that firms facing disruptive innovations must be prepared to respondto their unpredicted threats and opportunities by adopting disruptive BMI, since disruption creates opportunities that arealmost always associated with new products and services (Christensen and Raynor, 2003; Borrell Associates, 2007; Markidesand Oyon, 2010 Lucas, 2012). Although business models are frequently mentioned, they are rarely analyzed and often poorlyunderstood (Teece, 2010). From an entrepreneurial perspective, a business model is the design of organizational structuresto enact commercial opportunities explicitly initiated by market imperfections (George and Bock, 2011; Downing, 2005;Franke et al., 2008; Cohen and Winn, 2007). BMI is the replacement of the old business model with a new one for offeringproducts or services not previously available5 (Hwang and Christensen, 2008; Mitchell and Coles, 2004). It involves gradualtransition from the old business model to the new one (Cavalcante et al., 2011)6. Even though BMI adoption is a criticaldeterminant in realizing economic value, the right business model is rarely apparent (Achtenhagen et al., 2013). BMI re-quires companies to explore alternatives to current ways of doing business and to understand how they can meet customers’needs differently (Nidumolu et al., 2009). As such, it has become more important for the success of a business than productor service innovation (Johnson et al., 2008). BMI requires strong incentives to motivate entrepreneurial activities necessary

4 For example, prior research has shown that proactiveness has a linear impact on performance while risk-taking has a curvilinear impact on perfor-mance (Miller and Leiblen, 1996; Tang et al., 2008).

5 According to Zott and Amit (2010), from an activity system perspective, a business model is the template of how a firm conducts business, deliversvalue to stakeholders, and links factor and product markets. This perspective captures which activities should be performed, by whom, and how activitiesand actors are linked together. It suggests that firm performance is a function of not only financial but also social aspects of a business model. Therefore,BMI design needs to have 1) novelty to adopt innovative content, structure, or governance; 2) built-in elements to retain and lock-in business model stake-holders; 3) complementarities to bundle activities to generate more value; and 4) efficiency to reorganize activities to reduce transaction costs.

6 According to Cavalcante et al. (2011), BMI starts with new business model creation. This requires abandoning or removing some processes or closing abusiness area or unit associated with the old business model. This is usually followed by extending the new business model, through adding activities orexpanding core processes to an existing business model. Expansion of a new business model is usually followed by revision to the new business model,through exploring alternative ways of doing business. It is then followed by gradually removing processes that are associated with existing business model,and replacing them with new processes for the new business model. Eventually firms terminate the old business by terminating the existing processes,and completely replacing the old business model with the new one.

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for gaining insights into both technology and market domains in the face of disruption. It requires entrepreneurs and man-agers to 1) understand the “deep truth” about the fundamental needs of customers and how competitors are or are not satisfyingthose needs; 2) understand all technical and organizational possibilities for improvements; 3) make many informed guessesabout the future behavior of customers and competitors as well as about costs; and 4) make requisite adjustments to theexisting business model only after considerable trial and error learning (Sosna et al., 2010; Teece, 2010). The key disruptiveBMI adoption challenges are to find out how new products and services create and distribute value across the actors in afirm’s value network and how the different actors’ incentives need to be aligned with the new value distribution (Markides,2006; Sandström and Osborne, 2011; Sandström, 2010).

According to Covin and Miles (1999), CE activities are usually aimed at sustained regeneration, organizational rejuve-nation, strategic renewal, and redefinition of organizations, their markets, or industries7. They relate to “organization’s abilityto regularly introduce new products or enter new markets… and to the organization’s creation and exploitation of new product-market arenas” (Covin and Miles, 1999, 50). The commonality among all firms that could be described as entrepreneurialis, therefore, the presence of innovation (Covin and Miles, 1999). CE activities are found “in companies where the strategicleaders and the culture together generate a strong impetus to innovate, take risks, and aggressively pursue new ventureopportunities” (Dess and Lumpkin, 2005, 147). This is consistent with the view that firms need to have an entrepreneurialorientation (EO)8 to engage in successful CE (Covin and Miles, 1999; Dess and Lumpkin, 2005). In a recent review of the EOconstruct, however, George and Marino (2011) concluded that additional characteristics that entail action are required tocapture the concept of CE9. Citing Lumpkin et al. (2009), they proposed that autonomy is a key dimension that measuresCE. Accordingly, in this paper we conceptualize risk-taking, proactiveness, innovativeness, and autonomy as four promi-nent CE attributes. We discuss theoretical arguments for the inclusion of each dimension next.

Autonomy refers to freedom of action and decision-making that are often necessary for an organizational member to bringa new venture or business concept forward and carry it to completion (Lumpkin et al., 2009). It is a driving force for en-trepreneurial value creation and entrepreneurial initiatives enactment. Autonomy is especially important when the newventure is creating new growth using a new business model that may disrupt the established core business (Markides andOyon, 2010). The key dimensions of autonomy relate to resources, processes and values for a given growth group ratherthan geographic separation or ownership structure (Christensen and Raynor, 2003). Without autonomy, either a low pri-ority is likely to be assigned to new ideas or an old business model may be force-fitted onto the new opportunity (Govindarajanand Trimble, 2010). Therefore, establishing autonomous growth groups is essential for CE activities for creating new pro-cesses, capabilities, or ways of working together with responsibilities to do what needs to be done to ensure the success ofnew innovative projects10.

Risk-taking is defined as making decisions and taking actions without certain knowledge of probable outcomes, borrow-ing heavily, or committing significant resources to ventures in uncertain environments (Rauch et al., 2009). Without a degreeof risk-taking, firms delay or refrain from introducing innovations and from undertaking exploitative CE activities. This canresult in reacting conservatively to changing market conditions and in weak performance due to missed market opportu-nities. Timely risk-taking has been associated with strategic decision speed and it has subsequently been linked to improvedbusiness performance (Eisenhardt, 1989). However, risk-taking has also been found to negatively affect performance at theembryonic stage of growth (Hughes and Morgan, 2007).

Proactiveness is defined as opportunity-seeking, forward-looking behavior for introducing new products, services, or tech-nological capabilities ahead of the competition in anticipation of future demand, which can lead to new venture opportunities(Lumpkin and Dess, 1996). Receptiveness to market signals, awareness of customers’ needs, careful monitoring and scan-ning of the environment, and extensive feasibility research are often associated with a firm’s successful proactive strategy(Barclay and Benson, 1990; Wright et al., 1995). By actively anticipating and preparing for change and mobilizing re-sources far in advance of rivals, proactive firms are a step ahead of not so responsive competitors in accomplishing CE activities.

Innovativeness is defined as the predisposition and willingness to engage in creative behaviors, in experimentation throughthe introduction of new products or services, or in technological leadership via R&D in new processes (Dess and Lumpkin,2005). It is said to be present when firms pursue active implementations of new ideas, products, or processes (e.g., Hurleyand Hult, 1998). Innovativeness is suggested to be one of the most critical factors in accomplishing CE activities (Covin andMiles, 1999; Lassen and Nielsen, 2009) and corporate venture performance (Kandemir and Hult, 2005). It equally refers to

7 CE scholars generally agree that the nature of CE activities encompasses 1) product, process, or administrative innovation; 2) strategic renewal by trans-forming existing organizations; and 3) internal or external corporate venturing (Covin and Slevin, 1990; Dess et al., 1999; Ireland et al., 2009; Lumpkinand Dess, 1996; Pinchot, 1985; Riley et al., 2009; Sharma and Chrisman, 1999).

8 EO refers to the “processes, practices, and decision-making activities” characterized by one or more of the following dimensions: “a propensity to actautonomously, a willingness to innovate and take risks, and a tendency to be aggressive toward competitors and proactive relative to marketplace oppor-tunities” (Lumpkin and Dess, 1996, 136–137). EO measures a firm’s strategic posture “in taking business-related risks, to favor change and innovation inorder to obtain a competitive advantage for their firm, and to compete aggressively with other firms,” (Covin and Slevin, 1989, 77).

9 George and Marino (2011) argued against some authors’ (e.g, Kemelgor, 2002) view that EO dimensions of risk-taking, proactiveness, and innovativenessare a direct measure of CE attributes. Instead, they favored other authors’ (e.g., Zahra, 1991; Dess and Lumpkin, 2005) view that EO represents a firm’sorientation or proclivity towards entrepreneurship, but not actual engagement in CE activities.

10 In large organizations, one common method of promoting autonomy involves spinning-off a division, a separate unit, or a growth group with its ownbudgets and employees, which, in the long run, may provide sizable incentives and motivate the spinoff to succeed and subsequently become its ownpublicly-traded enterprise (Govindarajan and Trimble, 2010; Tchong, 2010).

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both a firm’s ability to create new to the world products, processes and services and its openness to new ideas and new-to-the-firm product launches (Lumpkin and Dess, 1996; Pérez-Luño et al., 2011). Without innovation, there is no CE regardlessof presence of other CE attributes (Covin and Miles, 1999).

Hypotheses development

Prior research across many industries has shown that autonomy is a necessary condition for creating new growth thatmay disrupt the core business (Christensen, 1997). It is even more important for accomplishing CE activities associated withcreating a new venture, especially when the new venture needs to take an approach the old venture will dislike or reject(Gilbert, 2005). Although senior management needs to set the innovation agenda and ensure that the right ideas get handledin the right way, a “growth group” (i.e., individuals, a dedicated team, a separate unit, “heavyweight” development teams,innovation council, or entrepreneurial champions) usually defines what ideas are in or out of bounds, evaluates and getsfunding for new proposals, and works with managers to solve problems and shape emerging businesses (Govindarajan andTrimble, 2010; Kim and Steven, 1992). Since individuals in autonomous growth groups do not represent their functionalgroup interest, they can more easily create new processes or ways of working together (Kim and Steven, 1992), which isrequired for BMI adoption. Therefore, one would expect that the more autonomous the growth groups are, the more likelythey will be able to adopt disruptive BMI in responding to digital disruption. We define the extent of disruptive BMI adop-tion as the extent to which a firm has successfully adopted and implemented disruptive BMI and propose the following:

Hypothesis 1. The greater is the extent of autonomy of the growth group in a company, the higher will be the extent of its dis-ruptive BMI adoption.

BMI often “requires managers to experiment to discover what can work and what fails, and communicate and institu-tionalize learning mechanisms (incorporating new knowledge and skills) into systems, procedures and structures acrossall echelons of the organization” (Sosna et al., 2010, 385). It requires significant risk-taking and entrepreneurial behavior insearching for alternatives outside the existing business model to identify and adopt a new or different business model(Chesbrough and Rosenbloom, 2002). Prior studies have also found that executives tend to be risk-averse and focus on tra-ditional short-term metrics at the expense of investments needed for long-term development (Burgelman and Valikangas,2005; McGrath et al., 2006). Risk-taking behavior has been found to influence a firm’s tendency towards exploitative inno-vation adoption (March, 1991). It is also positively associated with the number of internally generated innovations (Pérez-Luñoet al., 2011). Positive risk experience has also been found to moderate the relationship between the incumbent manager’sincreased perception of firm opportunity and intention to adopt disruptive BMI (Dewald and Bowen, 2010). Therefore, onewould expect risk-taking to be positively associated with the extent of disruptive BMI adoption. The following hypothesisis proposed next:

Hypothesis 2. The greater is the extent of the risk-taking behavior in a company, the higher will be the extent of its disruptiveBMI adoption.

Innovative firms bring external perspectives into the innovation process by having well-defined ways of interacting withtheir core customers, learning from non-consumers, and scanning for new ideas from other industries (Anthony et al., 2008).Innovativeness represents a basic willingness and culture to depart from existing technologies or practices and venture intothe unknown (Kimberly, 1981; Kitchell, 1995; Salavou, 2004). Innovativeness requires a corporate mindset and an innova-tion supportive culture to foster creative, innovative, and initiative-taking behaviors among participants (Jassawalla and Sashittal,2002). It also requires a gradual shift of the corporate mindset, from “not invented here” to “proudly found elsewhere” (Hustonand Sakkab, 2006). Since organizational innovativeness behavior is related to frequency and timing associated with gener-ation and adoption of new innovations (Salavou, 2004), one would expect a company’s innovative tendency to be relatedto its engagement in CE activities associated with disruptive BMI adoption. Therefore we propose the following:

Hypothesis 3. The greater is the extent of the innovative behavior in a company, the higher will be the extent of its disruptiveBMI adoption.

Proactiveness has been found to be associated with creating competitive advantage (Dess and Lumpkin, 2005) and withgreater firm performance in a variety of industry settings (Hughes and Morgan, 2007; Lumpkin and Dess, 2001). Further,the impact of proactiveness on innovation adoption is similar to that of risk-taking on innovation adoption (Pérez-Luño et al.,2011). Proactive firms have the desire to be pioneers, to adopt innovations, and to exploit the knowledge either generatedinternally or developed by other companies (Pérez-Luño et al., 2007). Therefore, one would expect proactiveness to be pos-itively associated with CE activities associated with disruptive BMI adoption. Consequently,

Hypothesis 4. The greater is the extent of the proactive behavior in a company, the higher will be the extent of its disruptiveBMI adoption.

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Prior studies have established a link between BMI and capturing value from innovation (Achtenhagen et al., 2013; Amitand Zott, 2001; Chesbrough, 2010; Chesbrough and Rosenbloom, 2002). However, changing the business model is likely tobe difficult for a firm and it may present a greater challenge than the technology itself (Achtenhagen et al., 2013; Asparaet al., 2013). Still, some companies such as Apple, IBM and Mozilla have successfully changed their business models to competeand have benefited from the disruption by focusing on new lines of businesses and deemphasizing their old business models(Lucas, 2012). A disruptive BMI with unique pricing and distribution strategies can generate attractive profits at discountprices for low margin businesses; it can establish a new cost structure, processes and new relationships with suppliers andwith partners so as to respond profitably to different classes of customers (Chesbrough and Rosenbloom, 2002; Christensenand Raynor, 2003). Therefore, one would expect that the extent of disruptive BMI adoption will positively influence busi-ness model performance. Hence, we propose,

Hypothesis 5. The greater is the extent of disruptive BMI adoption in a company, the higher will be its business model performance.

Research method

Guided by the literature, we developed our research model as depicted in Figure 1. The theoretical model in Figure 1includes patterns of interaction between CE attributes, BMI adoption, and business model performance in response to digitaldisruption. This model was tested using a sample data collected from the newspaper industry through a web survey. Theresearch process proceeded in two stages: measurement development and the main study for model testing. We focusedour attention on the newspaper industry because digital disruption has posed formidable challenges to it as discussed below.

Digital disruption and disruptive BMI adoption challenges

As digital publishing took off in Mid-1990s, many newspaper companies invested huge amounts in designing and mar-keting their websites to protect their print franchises. They were mainly concerned about the possibility that both readersand advertisers would move online, leaving them with lower revenues to cover their fixed production and distribution costs(Gilbert, 2005). Simply replicating their print business model11 online, they tried to sell blocks of online advertising spaceto their usual customers (e.g., department stores, car dealers), which did nothing to reverse the continuing declines in printcirculation, readership, advertising revenue, profits, or share prices (American Press Institute, 2008). To maintain or evenregain market share, newspaper companies were advised to behave like disruptors themselves by 1) understanding

11 As stated by Gillin (2006), “the business model of a metropolitan daily newspaper was developed over 150 years ago to support a delivery methodthat is becoming increasingly irrelevant” (p. 2). It requires large editorial staffs to create proprietary content and original news, a large and expensive cir-culation operation, enormous capital costs, and massive subscriber lists in order to support its ad rates (Gillin, 2006). It is established on the idea thattimely, local information is hard to come by. It is, however, quickly collapsing when confronted with Internet and digital products that are open, inex-pensive, and that allow advertisers to get to the same audience at a fraction of the cost. However, new online media entrants (e.g., Yahoo!, Monster.com,Google’s news and search offerings) built business models that captured the Internet interactive opportunities and sold to traditional newspaper compa-nies’ advertisers.

Autonomy

SIZE

Business Model Performance (BMP)

Risk-taking

Innovativeness

Proactiveness

Size1-Size5

BMIP1-BMIP4

Auto1-Auto3

Risk1-Risk3

Inno1-Inno3

Proc1-Proc3

Business Model Innovation Adoption

(BMIA)

BMIA1-BMIA6

Figure 1. Research model with constructs. BMIA: Business Model Innovation Adoption; BMP: Business Model Performance

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http://Monster.com

customers’ lives and businesses’ needs in completely new ways; 2) creating products that get customers’ information JOBS12

done better than any other solutions; and 3) moving fast, reducing risk and maximizing the likelihood of success (AmericanPress Institute, 2008). Heeding this advice, American Press Institute (API) recommends that newspaper companies go beyondthe core products and core functions of reporting news and create a wide range of digital noncore products as a response todigital disruption (American Press Institute, 2008). The API views newspaper industry’s core products as traditional printproducts that rely primarily on display, classified, or insert advertising sold by sales representatives to traditional adver-tisers and that use traditional production and distribution methods (e.g., paid daily or weekly newspapers, and all otherproducts based on a similar business model) (American Press Institute, 2006). Specifically, these include 1) print dailies andweeklies; 2) niche publications, either content-focused or advertising-focused; 3) news websites relying mainly on cost perthousand CPM (display) advertising and classified (American Press Institute, 2006). The digital noncore products include, however,1) niche websites targeting users’ JOBS (such as, community bulletin boards like craigslist, social networking portals, anduser-ratings engines); 2) digital products that rely on new revenue streams (such as paid search, lead generation and con-sumer direct marketing); and 3) digital products sold through nontraditional sales channels (such as self-serve advertisingvia Google’s AdWords) (American Press Institute, 2006). Some industry observers now believe that newspaper companiesneed to be “digital first and print last” by building compelling original content on platforms of their customers’ choice (Kirchner,2011). They also believe that such a digital first strategy can result in 1) more audience (crowd) engagement by increasingthe quality of shared content; 2) more robust growth of digital platforms and audiences; 3) more efficient cost structure byharnessing “both the cloud and the crowd”; 4) more revenue opportunities; and 5) more profits by allocating resources tocost-effective creation of content and sales and not just to the legacy mode of production13. This strategy, however, dependson having the freedom to create a unique cost structure and value network14 in order to be profitable in developing andselling its earliest products15, because the cost structure and value network associated with their core business cannot justifysuch a strategy. Since digital noncore products and services originate and prosper in new markets and value networks, theychange the way value is distributed across a newspaper company’s value network (Wirtz et al., 2010). Surviving and evenprospering in the new, complex value network (“value web”) of business-to-consumer (B2C) and business-to-business (B2B)require newspaper companies to adopt disruptive BMI (Lucas, 2012; Weill and Vitale, 2001; Wirtz et al., 2010; Ziv, 2009)16.This, however, is very difficult to do especially because such adoption needs to take place while the established print busi-ness still has substantial, profitable, and sustaining potential (Chesbrough, 2010; Dewald and Bowen, 2010; Markides andOyon, 2010; Shafer et al., 2005). Consequently, they face daunting tasks when they attempt to identify all affected actors,their activities, and incentives and target them with new products and services as required by disruptive BMI adoption17.Given the grave challenges facing the newspaper industry, it offers a prime empirical context to examine factors affectingdisruptive BMI adoption and its subsequent performance impacts.

12 JOBS is defined as an acronym for Jobs-to-be-done (what fundamental problem is a firm’s customers trying to solve?), Objectives (what objectives dothe customers use to evaluate the solutions?), Barriers (what barriers limit the customers’ ability to use the solution?), and Solutions (what solutions dothe customers consider?) (Innosight, 2009).

13 See more details about the Journal Register Company’s CEO John Paton’s Dialogue with Digital First Media Employees and the Public on “How a News-paper Company Becomes a Digital First Company,” which was presented at WAN IFRA International Newsroom Summit “How the crowd saved our company”(http://jxpaton.wordpress.com/2011/06/08/wan_ifra/). According to Paton, “if print dollars are becoming digital dimes then we better start chasing thedimes. And we better do it cost effectively.” To do so, it will be essential to “harness both the cloud and the crowd” to drive down the legacy model pro-duction costs (http://jxpaton.wordpress.com/2010/12/02/presentation-by-john-paton-at-inma-transformation-of-news-summit-in-cambridge-mass/).

14 “Value network” defines the context within which a firm “identifies and responds to customers’ needs, procures inputs, and reacts to competitors”(Rosenbloom and Christensen, 1995, 651), and “establishes a cost structure and operating processes and works with suppliers and channel partners inorder to respond profitably to common needs of a class of customers” (Christensen and Raynor, 2003).

15 The digital first strategy has resulted in remarkable payoffs in a short period of time for the Journal Register Company with more than 350 multi-platform products reaching an audience of 21 million Americans each month. While the company was bankrupt in 2009, in 2010 its profit was $41M.Further, 1) digital audience was up 100% from 5.5M to 11M unique visitors; 2) total audience — all platforms — was up 50% from 13M to 19.5M; 3) digitalrevenue Q1 was up 70% vs. industry’s 10%; 4) was producing 1000 videos per week vs. 400 per month; 5) it was streaming 1.7M videos per month vs. 117K;and 6) it outsourced noncore competencies (such as printing and mailroom services, delivery, pre-press, back-end digital, IT site maintenance, andfinance) (http://jxpaton.wordpress.com/2011/06/08/wan_ifra/). According to the weekly digest report published on September 5, 2012(http://jxpaton.wordpress.com/2012/09/05/another-tough-step/), “From 2009 through 2011, digital revenue grew 235% and digital audience more thandoubled at Journal Register Company. So far this year, digital revenue is up 32.5%. Expenses by year’s end will be down more than 9.7% compared to 2009”.

16 In addition to offering purchased and in-house content, they add value by offering interpretation of content, as well as by providing new innovativedigital platforms through which content may be consumed more widely (e.g., “Digital Kiosk” project is to set up a new platform for digital distribution inFrance or New York times’ NewsStand product, which provides access to hundreds of newspapers and magazines worldwide via a generic interface).

17 For example, recently, there have been concerted efforts by The New York Times to innovate by co-creating products and services with its customersand mobile technology partners, designing its products and services on mobile platforms for differential markets, and creating a mobile “experience” bybeing aware of the changing behaviors and needs of its customers (Ziv, 2009). Users, for example, can send SMS messages to The New York Times to getbreaking news alerts, weather updates, latest sports scores and headlines from any section of the newspaper. Further, The New York Times has also createdapplications for its mobile platforms both in house and by accessing innovative capabilities from partners outside its organizational boundaries in themobile technology arena (e.g., external device partners for iPhone, Blackberry, and other high end mobile devices) (Ziv, 2009). Even so, a few large news-paper companies (e.g., New York Times, Washington Post) have started to change their business models, made a move on the Internet, and learned to sellnew advertising products to new advertisers to take advantage of the ad revenues of the new categories (American Press Institute, 2008; Gilbert, 2003;Ziv, 2009). These have allowed them to discover major opportunities for growth and to race ahead in developing and testing new products and businessmodels while reducing the risks and costs of innovation (American Press Institute, 2008).

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http://jxpaton.wordpress.com/2011/06/08/wan_ifra/

http://jxpaton.wordpress.com/2010/12/02/presentation-by-john-paton-at-inma-transformation-of-news-summit-in-cambridge-mass/

http://jxpaton.wordpress.com/2011/06/08/wan_ifra/

http://jxpaton.wordpress.com/2012/09/05/another-tough-step/

Measurement development and scales

Measurement scales geared towards the newspaper industry were developed through a 4-step process. In step 1, initialitems were generated by 1) adapting scales found in the literature to the extent possible for CE attributes, BMI adoption,and business model performance; and 2) developing new items through coding industry reports, N2 project case studies,and white papers published by various consulting firms targeting newspaper companies. Appendix 1 gives more details onsources of initial items. In step 2, the initial items were validated through interviewing three local newspaper senior ex-ecutives and coding the interview transcripts. In step 3, items were further refined through item sorting and categorizationby four “judges”(i.e., domain experts recruited for the task). In step 4, a pilot study was conducted through a web survey.Newspaper executives were solicited to participate through newspaper forums and newsletters to newspaper executives.A total of eighty responses were received. Many controls were put in place to ensure data quality and reliability as detailedin Appendix 1. As part of the control for data validity, we collected data for job titles and newspapers worked for. Table 1shows the job titles of respondents, which indicates all respondents were well-qualified to participate in the survey.

After analyzing construct validity and reliability using the pilot data, we replaced one item and reworded two othersbefore the final data collection. The complete final list of items for the six constructs and additional items for the depen-dent variable appear in Appendix 2, with * denoting items dropped in the final data analysis. Detailed discussions on theseitems follow.

A minimum of three indicators are used to measure each reflective construct (Bollen, 1989). In the CE scales, autonomy(AUTO) is measured with three reflective items that tap into the extent to which the growth group has discretion and controlover the selection of “new-to-the-firm” noncore products, necessary resources for them, and their development processes.As mentioned earlier, noncore products have emerged as newspaper companies’ response to disruptive innovations (i.e.,Internet and digitization), as such the questionnaire items are geared towards noncore products whenever it is called for.Innovativeness (INNO) is measured by the extent to which a newspaper company has the tendency to generate and adoptinnovations (Damanpour and Wischnevsky, 2006). Three reflective items are used to tap into the extent to which a news-paper company has innovative culture, encourages people to look beyond the current practice for new-to-the-firm productdevelopment, and is open to adoption of new innovations. In contrast to the traditional three items for measuring innovativenessin Covin and Slevin (1989), which reflects the company’s inclination to buy or market products or services that are new tothe world, the three items used in our scale measure the company’s tendency towards the generation and implementationof new-to-the-firm new products or business practices. Risk-taking (RISK) behavior is measured by three reflective items tosize the extent to which a newspaper company is willing to 1) venture into the unknown and take risks based on gut feel-ings when hard data are not available; 2) fund new-to-the-firm noncore products when future growth is uncertain; and 3)develop and commercialize new-to-the-firm noncore products even when they may cannibalize the core business. INNOand RISK are all measured using a 5-point Likert scale anchored from “strongly disagree” to “strongly agree”. Proactiveness(PROA) is measured by four reflective items that tap into the propensity of top management to think ahead and anticipatefuture organizational needs and to introduce new-to-the-firm products and services ahead of the competitions. PROA is mea-sured using numerical scales with opposing phrases anchored on the two ends.

The extent of disruptive BMI adoption (BMIA) is measured using six items that tap into the extent to which a newspapercompany’s new business model for noncore products is novel and different from its existing business model for its core prod-ucts. The six items measure the extent to which the BMIA has resulted in changes over the last three years in the revenuemodel, value proposition, pricing structure, cost structure, resources for selling noncore products, and new formal or in-formal arrangement for information exchange with partners. Since these items may vary independently and they do notnecessarily correlate highly with each other, it is suitable to conceptualize BMIA as a formative construct. As such, BMIAitems describe different facets of the construct and are not conceptually interchangeable. Further, removing any of the itemswould alter the conceptual meaning of the extent of BMIA at the aggregate level. Similar to PROA, BMIA is measured usingnumerical scales with opposing phrases anchored on the two ends. Therefore, Business model performance (BMP) is mea-sured using four items that assess a company’s performance toward a diverse business portfolio of core and noncore products.More specifically, the four items for BMP measure the number of noncore products on a monthly basis, revenue generatedfrom online sources, audience reached on a weekly basis, and percentage change in total number of advertisers from threeyears ago. These dimensions are not expected to correlate with each other. Further, they cause BMP and determine the extent

Table 1Job Titles of Respondents for Pilot Data

Publisher, CEO, or President 24Vice President, General Manager, or Associate President 11Director 23Editor or Editor-in-Chief 9Supervisor, CFO, consultant, 3Manager 8undisclosed 2Total 80

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of BMP. Therefore, BMP is measured as a formative construct with four formative indicators. Collectively, they measure thesuccess of BMIA in commercializing and deriving value from innovative noncore products.

Five control variables are used: weekday circulation, weekend circulation, number of employees exclusively for web op-erations, total number of employees, and annual revenue. All responses for the control variables are ranges that respondentschoose from. The control variables are used as reflective indicators of SIZE, which is used as the control variable for bothBMIA and BMP.

Data collection procedure for the main study

For our main study, the survey website was modified to reflect changes in the final questionnaire after analyzing con-struct validity and reliability of the pilot data. Potential respondents were contacted through email solicitations. To collectemail addresses, we used a website that had a comprehensive listing and URLs of newspapers in all fifty states and the Dis-trict of Columbia. Names, email addresses, and job titles of senior executives of each newspaper were collected, if its websitelisted such information. The email addresses were divided into four groups according to time zones. Solicitations were sentto all email addresses between 8:30 am and 9:30 am local time on weekdays. To improve the response rate, the solicitationemail was sent individually to each email address. Each potential respondent received a total of three solicitations within aone-month period. The same controls as in the pilot study were used for the main data collection. After eliminating dupli-cates, a total of 158 responses were collected. However, due to a server problem, ten data points were lost, resulting in atotal of 148 responses. All were usable. Our sample represented approximately 9% of all US newspapers since various websources put the number of all US daily newspapers at 1500–1700 (Countries Compared by Media > Newspapers and peri-odicals > Number of titles > Daily. International Statistics at Nationmaster.com, 2012; Journalism.org, 2012). Data for jobtitles and newspapers worked for were again collected to ensure data validity. Job titles of respondents for the final dataset were found to be similar to those in the pilot data. Comparing job titles and newspapers worked for data between thepilot and the main study showed no overlapping respondents between the two data collections. Descriptive statistics onthe responses for the main study are listed in Table 2.

Data analysis and results

To test for potential non-response bias, first, following the technique used in prior research (e.g., Mani et al., 2010), wedivided our sample into two groups and compared the demographics of the groups. No significant difference was found.Table 3 lists distributions in first year of online publishing. Other demographic distributions are similarly comparable. Thisgives reasonable assurance that non-response bias is not a concern. Second, we compared demographics of our sample asseen in Table 2 18 with demographics of a large scale NAA survey shown in Table 3 (NAA, 2007). We found that they weresimilar. The similarity between our data in Table 2 and NAA data in Table 3 signals that non-response bias is not a concern.This is because the NAA newspaper survey was large in scale and over 33% of all U.S. newspapers participated. Further, thedemographic distributions shown in Table 3 are consistent across multiple NAA newspaper surveys that we have exam-ined and are also consistent with other published figures that gave a complete breakdown of all US newspapers by circulationsize (e.g., Press Reference, 2001), lending to the conclusion that demographic distributions in NAA surveys were represen-tative of all US newspapers. Therefore, our sample was representative of all US newspapers.

To test for common method bias, first, we performed Harman’s single factor test twice, once including all independentvariables and the other time including all independent variables and the dependent variable. Exploratory factor analysiswas performed with no rotation and with number of factors fixed at 1. Percent of variance explained by the single-factormodel was 31.94% when the dependent variable was not included and 27.02% when the dependent variable was included.Since those percentages are not high (<50%), one cannot conclude that common method variance is a concern. Second, weadded a common method factor to our model to evaluate the size of common method variance, as recommended in theliterature (Podsakoff et al., 2003; Williams et al., 2010). The procedure we followed was developed by Liang et al. (2007)and used by other authors (e.g., Furneaux and Wade, 2011). Testing results show that indicator variance attributable to thecommon method factor ranges from <0.01% to 3%, whereas indicator variance attributable to the underlying construct rangesfrom 22% to 92%, with a median of 70%. The negligibly small variance attributable to the common method factor comparedwith that attributable to the underlying construct indicates that variance explained due to common method is very smalland not a concern at all.

Measurement model analysis results

For the main research data set, a structural equation model (SEM) was specified. The measurement and path modelswere tested using a nonlinear Partial Least Squares (PLS) path modeling software, WarpPLS. We chose PLS-based SEM overcovariance-based SEM because multi-level formative constructs are used in our model. WarpPLS identifies and estimates

18 Over half of the respondents represented newspapers that were the smallest — below 25,000 in circulation. This is, in fact, representative of news-paper size distribution and is typical of survey participants in any large-scale newspaper survey.

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both nonlinear and linear relationships among the latent variables. It first attempts to fit an S-shaped curve to the data;failing that, it then attempts to fit a U-shaped curve to the data; failing that, it lastly fits the data to a linear relationship.Since relationships in nature and in human behavior are rarely in straight lines, WarpPLS is able to give a more accuratedepiction of relationships between latent variables if it finds an S- or a U-shaped curve. If not, WarpPLS gives the same linearestimates as in linear PLS Path modeling tools such as SmartPLS and PLS-Graph. Original data were fed into WarpPLS, which

Table 2Descriptive statistics for final data set

Number ofEmployees

Number ofResponses

Percentage

<100 84 56.76%100–400 45 30.41%401–700 9 6.08%701–1000 7 4.79%1001–2000 2 1.35%>2000 1 0.68%Grand Total 148 100.00%

First Year PublishingOnline

Number ofResponses

Percent

Before 1990 12 8.11%1990–1996 38 25.68%1996–2000 61 41.22%After 2000 37 25.00%Grand Total 148 100.00%

Number of WebEmployees

Number ofResponses

Percent

<10 133 89.86%11–50 14 9.46%51–100 1 0.68%Grand Total 148 100.00%

Circulation Number of Responses (%)

WeekendCirculation

WeekdayCirculation

Under 25,000 86 (58.78%) 88 (59.46%)25,000-under 50,000 30 (20.27%) 29 (19.59%)50,000 to under 100,000 13 (8.78%) 14 (9.46%)100,000 to under 250,000 9 (6.76%) 10 (6.76%)250,000 to under 500,000 6 (4.05%) 6 (4.05%)500,000 or over 2 (1.35%) 1 (0.68%)Grand Total 148 (100%) 148 (100%)

Revenue Number ofResponses

Percent

<$5M 58 39.19%$5M-$10M 29 19.59%$10M-$20M 31 20.95%$20M-$70M 15 10.14%$70M-$150M 4 2.70%$150M-$300M 9 6.08%>$300M 2 1.35%Total 148 100%

Table 3Descriptive Statistics of Survey Respondents from a NAA NationalSurvey. Source: NAA, 2007

Weekend Circulation Number ofResponses (%)

Under 25,000 375 (56.73%)25,000-under 50,000 141 (21.33%)50,000 to under 100,000 72 (10.89%)100,000 to under 250,000 55 (8.32%)250,000 to under 500,000 11 (1.66%)500,000 or over 7 (1.06%)Grand Total 661 (100%)

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automatically standardized all data to a mean of 0 and a variance of 1. Final research data were analyzed first by evaluatingthe measurement model and then by evaluating the path model.

Psychometric properties of reflective latent variables for the final data set were assessed by examining internal consis-tency, convergent validity, and discriminant validity. Internal consistency was evaluated by examining Cronbach’s Alpha andcomposite reliability scores produced by WarpPLS. Table 5 shows that these values indicate internal consistency for eachof the four reflective constructs. Table 5 also lists mean and standard deviation of each item.

Convergent and discriminant validities were verified through confirmatory factor analysis by examining factor struc-ture, average variance extracted (AVE), and inter-construct correlations. Table 5 shows that all factor loadings are statisticallysignificant (p < 0.001) and above the cut-off value of 0.70. All AVE values are above the cut-off value of 0.5. Table 6 showsthat all loadings are greater than all cross-loadings. Table 7 shows that all square roots of AVEs are greater than all latentvariable correlations. The three tables together show strong convergent and discriminant validities for latent reflective constructs.

Construct reliability of formative constructs was assessed by examining all path coefficients from predictor constructsto the dependent construct, all weights, and indicator multicollinearity. Table 8 shows that three formative indicators for

Table 4Comparison of First Year of Publishing to Verify No Non-Response Bias

First Half of Responses Second Half of Responses

YEAR Number ofRespondents

Percentage ofRespondents

Number ofRespondents

Percentage ofRespondents

Before 1990 6 8.11% 6 8.11%1990–1996 20 27.03% 18 24.32%1996–2000 31 41.89% 30 40.54%After 2000 17 22.97% 20 27.03%Grand Total 74 100% 74 100%

Table 5Psychometric Properties for CE Constructs

Construct Item Mean STD Loading P Value CR Alpha AVE

Autonomy (AUTO)AUTO1 3.01 1.12 0.94 <0.001 0.96 0.94 0.89AUTO2 2.82 1.07 0.94 <0.001AUTO3 3.03 1.10 0.95 <0.001

Proactiveness (PROA) 0.83 0.70 0.62PROA1 3.00 1.02 0.77 <0.001PROA2 3.00 0.98 0.84 <0.001PROA3 3.13 0.92 0.75 <0.001

Innovativeness (INNO) 0.89 0.82 0.74INNO1 3.56 0.97 0.90 <0.001INNO2 4.03 0.89 0.74 <0.001INNO3 3.72 0.91 0.92 <0.001

Risk taking (RISK) 0.86 0.76 0.67RISK1 3.23 1.08 0.85 <0.001RISK2 3.18 0.99 0.87 <0.001RISK3 2.76 1.07 0.74 <0.001

Table 6Loadings and Cross-Loadings for CE Constructs

AUTO INNO PROA RISK

AUTO1 0.94 −0.004 0.01 0.02AUTO2 0.94 0.04 0.01 −0.07AUTO3 0.95 −0.04 −0.02 0.05INNO1 0.09 0.90 0.06 −0.20INNO2 −0.23 0.74 −0.17 0.20INNO3 0.10 0.92 0.07 0.03PROA1 0.16 −0.23 0.77 0.11PROA2 −0.15 0.17 0.84 −0.11PROA3 0.0001 0.05 0.75 0.001RISK1 −0.18 0.05 −0.003 0.85RISK2 0.06 0.02 −0.17 0.87RISK3 0.13 −0.09 0.20 0.74

AUTO = Autonomy; INNO = Innovativeness; PROA = Proactiveness; RISK = Risk taking.

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BMIA are not statistically significant at p < 0.05 level. To ensure content validity, all were retained for path model testinglater. The other three BMIA weights are sizable and significant. The dependent variable, BMP, was conceptualized as a latentvariable with four formative indicators. All four path coefficients for BMP are sizable, significant, and with the right signs.

Discriminant validity of formative constructs was assessed by examining inter-correlations, which was found to be alllower than 0.2 for all inter-correlations between BMIA items and between BMP items, satisfying the condition that thosecorrelations need to be less than 0.70, thereby establishing discriminant validity for both BMIA and BMP.

Structural model testing results

As shown in Figure 2, SIZE was used as a control variable for both BMIA and BMP in the path model. We used SIZE as alatent control variable with five reflective indictors instead of using five separate control variables because all five itemsare indicators for SIZE, their bivariate correlations are very significant (ranged from 0.51 to 0.92, p < 0.01), and their Cronbach’sAlpha has a high value of 0.88. A Jackknifing resampling procedure was carried out to estimate p-values of path coeffi-cients. Structural model testing results show that AUTO and RISK each has a significant, positive relationship with BMIA at

Table 7Intercorrelations and AVE of Latent Variables

AUTO INNO PROA RISK

AUTO 0.94INNO 0.61 0.86PROA 0.29 0.31 0.79RISK 0.53 0.62 0.43 0.82

AUTO = Autonomy; INNO = Innovativeness; PROA = Proactiveness; RISK = Risk taking;Diagonal values are square roots of AVEs.Off diagonal values are correlations. All correlations are significant at p < 0.01 (2-tailed) level.

Table 8Indicator Validity for Formative Constructs

IndicatorWeight

P Value

BMIA1→ BMI 0.16 0.13BMIA2→ BMI 0.15 0.19BMIA3→ BMI 0.39 <0.001BMIA4→ BMI 0.39 <0.001BMIA5→ BMI 0.53 <0.001BMIA6→ BMI −0.04 0.34BMP1→ BMP 0.37 0.002BMP2→ BMP 0.48 <0.001BMP3→ BMP 0.41 0.01BMP4→ BMP 0.40 0.03

BMIA = Business Model Innovation Adoption; BMP = Busi-ness Model Performance.

BMIAR2=25%

Autonomy SIZE

BMPR2=27%

Risk-taking

Innovativeness

Proactiveness

0.17*

0.12*

-0.03ns

0.25++

0.23+ 0.37++

0.28++

*: significant at 1-tailed 0.05 level+: significant at 2-tailed 0.05 level++: significant at 2-tailed 0.01 levelNs: not significant

Figure 2. Hypothesis testing using nonlinear PLS path modeling tool, WarpPLS. Business Model Innovation Adoption

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one-tailed19, p < 0.05 level (AUTO: β = 0.17, 1-tailed p < 0.05; RISK: β = 0.12, 1-tailed p < 0.05), supporting H1 and H2. On theother hand, the relationship between INNO and BMIA is very small and insignificant (β = −0.03, 1-tailed p > 0.05); there-fore, H3 is not supported. PROA has a significant positive relationship (β = 0.25, 1-tailed p < 0.005) with BMIA, supportingH4. BMIA has a strong and significant positive relationship with BMP (β = 0.28, 1-tailed p < 0.005), supporting H5. The controlvariable SIZE has a strong and significant positive relationship with BMIA (β = 0.22, 1-tailed p < 0.025) and with BMP (β = 0.37,1-tailed p < 0.005). In terms of nonlinear curves, WarpPLS found nonlinear curves for all hypothesized relationships. Thecurve for BMIA and BMP relationship (Figure 3) indicates that when BMIA value is high, its impact on BMP is most prom-inent. When BMIA value is low, its impact on BMP is less prominent. When BMIA value is in the middle range, its impacton BMP is barely noticeable, meaning when BMIA is in the middle range, higher levels of BMIA produces only marginallyhigher BMP.

WarpPLS reported three model fit indices: average path coefficient (APC) with p-value, average R-squared (ARS) withp-value, and average variance inflation factor (AVIF). APC and ARS for the nonlinear model were 0.205 (p < 0.001) and 0.260(p < 0.001), respectively. AVIF was 1.385. All indicated good fit. We also calculated a global fit measure, GoF, computed as

communality R∗ 2 , where communality is average of communality and R2 is average R2 for endogenous variables(Tenenhaus et al., 2005; Wetzels et al., 2009). GoF was computed to be 0.38, indicating large effect (Wetzels et al., 2009).

To find out whether the nonlinear PLS path modeling obtained different path coefficient estimates from those obtainedby a linear PLS path modeling tool, we tested the measurement and path models using SmartPLS 2.0 Beta (Ringle et al.,2005), which also automatically standardized all data to a mean of 0 and a variance of 1. Bootstrapping re-sampling pro-cedure with 500 samples was used to estimate the significance levels, since SmartPLS did not provide Jackknifing procedure.Figure 4 shows the results of the path model estimation. The main difference between the linear and nonlinear models liesin the significance levels of paths from RISK to BMIA and from AUTO to BMIA. In the nonlinear model, both are significantat 1-tailed p < 0.05 level, with PROA to BMIA having the largest coefficient (β = 0.26, 1-tailed p < 0.005). In the linear model,the path from RISK to BMIA has a non-significant path coefficient whereas the path from AUTO to BMIA has the largest co-efficient that is highly significant (β = 0.26, 1-tailed p < 0.005). The R2s for BMP are similar, 27% for both. R2s for BMIA are27.8% and 25% for the linear and the nonlinear models, respectively. Considering that the benefits of CE activities for orga-nization rejuvenation often take many years to fruition (Dess et al., 1999; Zahra and Covin, 1995), a medium level of impacton BMP is expected. The model indices for the linear model are 0.204 for APC (p < 0.001), 0.244 for ARS (p = 0.004), and1.432 for AVIF, all slightly worse than the corresponding numbers for the nonlinear model.

19 1-tailed statistics are used in all hypotheses testing reporting. They are sufficient because directions of hypothesized relationships are theoreticallysupported.

Figure 3. Demonstration of nonlinear relationship between BMIA and BMP. *: Significant at 0.05 level (2-tailed); **: Significant at 0.01 level (2-tailed); +:Significant at 0.001 level (2-tailed)

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Discussion and conclusions

This study examined the extent to which CE has an impact on disruptive BMI adoption in response to digital disruptionand the impact of disruptive BMI adoption on business model performance. We developed five research hypotheses by drawingon CE theoretical framework. Using survey data from a sample of newspaper companies, we found significant evidence fordirect association of autonomy (H1), risk-taking (H2), and proactiveness (H4) with the extent of disruptive BMI adoption, asexpected. However, no such evidence was found for direct association of innovativeness with the extent of disruptive BMIadoption (H3). Regarding H5, our results show that at low or high levels of disruptive BMI adoption, its impact on businessmodel performance is high whereas at medium levels of disruptive BMI adoption, its impact on business model perfor-mance is marginal.

Theoretical implications

Prior literature reviews on business model have frequently pointed to the fragmentation of business model definitionsand the lack of well-defined theoretical constructs for business model and business model performance (George and Bock,2011; Teece, 2010). These have prevented rigorous and integrated research on business models and have led to inconsis-tent empirical findings regarding the effects of disruptive BMI adoption on business model performance, especially withinan entrepreneurial context (Dewald and Bowen, 2010; George and Bock, 2011). This study has re-conceptualized the con-cepts of business model and BMI innovation from CE perspective and investigated the roles of prominent CE attributes indisruptive BMI adoption and the impact of disruptive BMI adoption on business model performance. It has advanced CEtheory by refining the conceptualization and measurement of disruptive BMI adoption, by illustrating how prominent CEattributes can predict the extent of disruptive BMI adoption, and by demonstrating the impact of disruptive BMI adoptionon business model performance. By linking prominent CE attributes to the extent of disruptive BMI adoption and by linkingdisruptive BMI to business model performance, this study has extended the research on business model beyond productand transaction characteristics for measuring a firm’s long-term performance. Using the newspaper industry as an example,we developed 1) new measurement scales for prominent CE attributes that clearly reflect new-to-the-firm innovation adop-tion; 2) new measurement scale for the extent of disruptive BMI adoption; and 3) new measurement scale for business modelperformance. For innovation-adopting organization, the critical innovation issue is how to manage innovation adoption inorder to achieve adaptive organizational change (Damanpour and Wischnevsky, 2006). However, prior literature has foundno relationship between proactiveness or risk-taking and new-to-the-firm innovation adoption (Pérez-Luño et al., 2011).Our findings suggest that while autonomy, risk-taking and proactiveness have direct significant associations with the extentof disruptive BMI adoption, innovativeness has no such association. Innovativeness is more related to a firm’s inclination toadopt new-to-the-firm products and services or to create new to the world products and services than to its ability to gen-erate and implement new-to-the-firm ideas. As such, it is not necessarily directly related to the firm’s extent of disruptiveBMI adoption. These insights are obvious only in hindsight.

Using empirical data, this study has extended prior literature on business model dynamics, by showing that the linkagefrom extent of disruptive BMI adoption to performance is high at both low and high levels of adoption and is weak at themedium level when adoption is at extension or revision stages. This explains why most survivors of technological disruption(e.g., Apple, IBM, and Mozilla) are rarely able to completely abandon their existing business models to adopt new ones; insteadthey change their foci to pursue new lines of business while deemphasizing their old business models (Lucas, 2012). Further,the results also show that SIZE has a significant impact on the extent of disruptive BMI adoption and on business modelperformance. This suggests that abundant organizational resources and capabilities in large newspaper companies are ad-vantageous in disruptive BMI adoption. This study further extends prior research on innovation by suggesting that, while

BMIA

AutonomySIZE

BMPR2=27.3%

Risk-taking

Innovativeness

Proactiveness

0.26**

0.11

-0.11

0.22*

0.30+ 0.35+

0.28**

Figure 4. Hypothesis testing using linear PLS path modeling tool, SmartPLS

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smaller organizations have advantage over larger ones in pursuing entrepreneurial opportunities and in generating newnovel innovations (Damanpour and Wischnevsky, 2006), larger organizations have advantage over smaller ones in adopt-ing disruptive BMI. Future research needs to investigate how to bridge the development of prominent CE attributes withdisruptive BMI adoption in small firms and to identify characteristics of business models that compel or hamper the de-velopment of routines for prominent CE attributes in small firms.

Managerial implications

In an era of digitization, free content and citizen journalists, newspaper companies are struggling to find sustainable busi-ness models. The challenge before newspaper companies is to find ways to change their inflexible and costly print businessmodel by taking advantage of Internet, digitization, and digital newspaper publishing in such a way that people can notonly get any information anytime and anyplace but also publish their own content at will (Gilbert and Bower, 2002). Theresults of this study suggest that size, autonomy, risk-taking, and proactiveness have significant direct impact on disrup-tive BMI adoption in responding to digital disruption. Although the key dimensions of autonomy relate to resources, processes,and values rather than geographic separation or ownership structure (Christensen and Raynor, 2003), the results show thathaving autonomous and proactive growth group is essential in helping large firms pursue disruptive BMI adoption. To succeedin responding to digital disruption, newspaper companies need to give substantial autonomy to their growth groups andtheir noncore operations. They need to serve new customers, rethink content models, accept different margin structures,identify and develop noncore business concepts, and grant growth groups secure budgets and decision-making autonomyto create new revenue streams (Anthony et al., 2008). Without autonomous growth groups, force-fitting the existing busi-ness model onto the new opportunity often occurs, which rarely works because disruptive technologies normally target anew market that differs in almost all aspects from the market that the existing business model is optimized for (Christensenand Raynor, 2003). Under the condition of high CE activities, the spin-off of a proactive growth group is necessary for dis-ruptive BMI adoption20. However, such a growth group needs to maintain the same innovative and risk-taking culture ofthe mainstream organization so as to carefully balance the “portfolio” of low-risk opportunities in the core and new, higher-risk growth opportunities for noncore products. Instead of risking big investments on uncertain strategies, newspaper companiesneed to “invest a little to learn a lot” about different business models (American Press Institute, 2008)21. The results of thisstudy also show that the more newspaper companies act in an entrepreneurial manner, the more likely they are able tosucceed in adopting disruptive BMI in response to digital disruption. It suggests that firms with a higher level of CE activi-ties are in a better position to analyze their surrounding networks of actors and to determine how adopting disruptive BMIimpacts each actor. Consequently, they are able to reduce the challenges related to commercializing such innovations. There-fore, it is important for newspaper companies to design organizational systems to monitor and assess their CE attributesand to develop and reinforce each attribute as necessary. For example, do their organizational systems reward autonomy,risk-taking, innovativeness, and proactiveness? To what extend do they allow individuals or growth groups freedom to cham-pion new ideas or to commit significant resources to new innovative projects to ensure high returns? To what extent aretheir performance metrics, incentives, and innovative culture for their noncore operations different from those for their coreoperations? Have their employees proactively created new offerings and discovered new business models, or have they mostlymade reactive decisions? How separate should their new business models be from their existing business models? Whatare some of the unique challenges for simultaneously pursuing both their new disruptive business models and their corebusiness models? What digital products do customers consider and what objectives do they use in their evaluations? Wealso encourage future researchers to examine and extend our findings to other organizational contexts to evaluate thegeneralizability of our results.

Limitations

Like any empirical research effort, this study contains a number of methodological strengths and limitations, and we rec-ognize specific issues that warrant caution in interpreting our results. The breadth of the sample included in this study suggeststhat the findings are fairly generalizable to many newspaper companies. However, the findings are limited in some impor-tant ways. Although respondents possessed a high degree of relevant knowledge, all of the measures are self-reported, and

20 However, there are “solid arguments for and against a spinout organization, and more refined insights into contingencies are emerging,” (Danneels,2004, 256). For example, prior research (Iansiti et al., 2003) has suggested that the advantage of separated approaches is agility, but the value decreasesover time. The advantage of integrated approaches is efficiency; and the value generally increases over time; when resource complementarities betweenthe new venture and the mainstream business are critical, and these complementarities require intra-company coordination, a more integrated approachmay be advised.

21 API’s N2 project report (American Press Institute, 2008) highlights twenty-four case studies of companies that have moved proactively to create newofferings for consumers and discover new business models to serve businesses, in order to generate desperately needed new revenues. These productsincluded print, Web, email and combination products; niche products; products targeting moms, parents, Latinos, young adults, upscale demographic groups,newcomers and businesses of various kinds, and one newsroom transformation project. Most of the new products mentioned in this followed the prin-ciple of “invest a little, learn a lot,” by creating fairly simple, first-generation products that are being tweaked and improved as the companies learn whatneeds to be improved.

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therefore subject to hindsight and other biases. Another limitation is that we captured only a snapshot of newspaper com-panies’ adoption of disruptive BMI. Similar to prior research on CE, this study measured CE activities at a point in time. Acompany’s CE activities, however, can evolve dynamically, resulting in changes in behaviors and activities of internal stake-holders and in relationships with external stakeholders.

Conclusions

This study focused on the links among prominent CE attributes, disruptive BMI adoption, and business model perfor-mance in order to clarify the interrelatedness among these constructs. Grounded in CE theoretical framework, it has developed1) new measurement scales for prominent CE attributes that clearly reflect new-to-the-firm innovation adoption; 2) newmeasurement scale for the extent of disruptive BMI adoption; and 3) new measurement scale for business model perfor-mance. Using empirical data from the newspaper industry, it has demonstrated that autonomy, risk-taking, and proactivenesshave unique contributions to the extent of disruptive BMI adoption. It has also demonstrated that the extent of disruptiveBMI adoption has a nonlinear impact on business model performance. Given that CE activities often lie at the core of re-sponding to disruptive innovations and that their importance in adopting disruptive BMI for such a response has not beendemonstrated in prior research, we hope our research has provided some directions for future research in this area.

Acknowledgements

The authors thank the LRP Editor-in-Chief and two anonymous reviewers for their constructive comments throughoutthe review process.

Appendix 1. Measurement development and refinement

Measurement development proceeded in four steps: 1) initial item generation through an academic literature reviewand coding of newspaper industry reports; 2) item validation through interviews with newspaper executives; 3) item re-finement through an item sorting and categorization task by four “judges” (i.e., domain experts recruited for the task); and4) scale testing through a pilot study.

Initial Measurement Generation

To develop measurement items, we adapted scales found in the literature to the extent possible for CE attributes, BMIadoption, and business model performance. However, as discussed below, either the existing scales were not suitable orthere were no existing scales for the majority of the measures. We therefore searched the literature on innovation man-agement, industry reports, N2 project case studies and white papers published by various consulting firms targeting newspapercompanies to identify underlying domains for the new constructs. In this process, we used domain sampling to create itemsand measurement scales with adequate content validity (Nunnally, 1978).

To develop measurements for CE attributes, we first looked at the existing measurement scale for EO attributes. However,these were geared towards “new to the world” innovation generation and were not directly suitable for “new to the firm”innovation adoption in the context of the newspaper industry. Innovation adoption is distinct from innovation generationbecause it is facilitated by different organizational factors and poses different organizational challenges (Damanpour andWischnevsky, 2006). In addition, new-to-the-firm innovation adoption is far more common because firms can adopt many“new to the firm” products regardless of their level of EO (Pérez-Luño et al., 2011). Existing measurement scale of EO is anine-item standard scale that measures innovativeness, proactiveness, and risk-taking (Covin and Wales, 2012; George andMarino, 2011). The scale was developed by Covin and Slevin (1989), who built on the work of Miller and Friesen (1982).However, most EO studies using this scale have not distinguished among types of entrepreneurial initiatives or “new entry”(Miller, 2011). Further, a recent review of prior empirical studies of EO by George and Marino (2011) suggested “it is timeto revisit these items” because of low reliability associated with one item measuring innovativeness and the ambiguity as-sociated with the definition of risk by not including “venturing into the unknown” (p. 1004). Consequently, new items haveto be generated to measure innovative, proactive, and risk-taking behaviors. These items need to clearly reflect engage-ment in CE activities necessary for new-to-the-firm innovation adoption. Items for the autonomy dimension were adaptedfrom Walter and Lopez (2008).

Our reviewing of prior literature on BMI suggests that disruptive BMI needs to include 1) value propositions and the op-portunities to satisfy customer needs at a profit; 2) cost structure, revenue model, processes, resources; or 3) the interactionsbetween the firm and its key stakeholders in order to measure resource, transactive, and value structures dimensions ofthe new business model (George and Bock, 2011; Johnson et al., 2008; Hwang and Christensen, 2008; Osterwalder et al.,2005). As suggested by Damanpour and Wischnevsky (2006), while the earliness and the rate of adoption (speed) are ap-propriate measures of initiation of innovation and are suitable measures for innovation generation, the extent of implementationis a suitable measure for innovation-adopting organizations and for implementation of innovation, because it representsthe prevalence of the implementation across organizational units. However, no existing items were found for measuring

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the extent of adoption of disruptive BMI or business model performance, as discussed above. Therefore, we again codednewspaper industry reports to generate initial items that covered the entire domain of each construct.

Measurement Item Validation and Refinement

Following initial item selection, we conducted interviews with senior executives from two newspapers to ensure content andface validity (Churchill, 1979) for all new items that we developed as the result of the coding process. Interview transcripts withthree local newspaper senior executives were then coded to verify our earlier set of items. The above process resulted in a set oftwenty-five final items, plus additional items for the control variables. Next, we recruited three business school faculty and onedoctoral student for a sorting and categorization task for all constructs. The resulting set of items was then given to doctoral stu-dents enrolled in a doctoral level research methods class for the second round of sorting and categorization.

Scale Testing Through a Pilot Study

Following measurement development, we conducted a pilot study through a web survey to test the scale. Participantswere solicited through newspaper forums and newsletters to newspaper executives. The weekly newsletter was a free serviceto all paid members belonging to the Digital Media chapter of NAA. The paid membership directory of the Digital Mediachapter at the time showed that almost all members had job responsibilities related to digital media at a newspaper. Fordata control purposes, the web survey recorded IP addresses, date/time survey started, date/time survey submitted, and asession ID for each survey. The session ID was used to allow a respondent to return to an incomplete (not yet submitted)survey at a later time. Time started and time submitted were used to eliminate surveys that were completed too fast (lessthan five minutes). Session IDs and IP addresses were used to discern possible multiple entries from the same person. Wealso requested job title and newspaper name for further data control.

After data were collected, we sorted all responses based on IP addresses and found five groups of responses sharing thesame IP addresses within each group, for a total of eight suspect entries. We eliminated two data points that were evi-dently duplicates (same session ID and same answers across all questions as existing data). We eliminated two more datapoints because each had the same session ID, job title, and newspaper name as another data point, although answers variedslightly. The eliminated ones were submitted a few days earlier than the ones retained and presumed from the same person.No submitted survey took less than five minutes. This resulted in eighty valid responses. Analyses of job titles indicatedthat all respondents were well-qualified to participate in the survey. After analyzing construct validity and reliability usingthe pilot data, we finalized the measurement scale by replacing one item and rewording two others.

Appendix 2. Survey instrument

Note: Autonomy, Innovativeness, and Risk-taking are on a 5-point Likert scale, labeled with the words “Strongly Dis-agree,” “Disagree,” “Neutral,” “Agree,” and “Strongly Agree.”

Please indicate the extent to which you agree or disagree with each of the following statements about the growth group’s au-tonomy with respect to developing noncore products at your newspaper company:

Autonomy (AUTO)

Our growth groups have substantial discretion over which noncore products to pursue.Our growth groups have control over resources necessary for developing noncore products.Our growth groups have control over development processes of noncore products.Please indicate the extent to which you agree or disagree with each of the following statements about your newspaper comp-

any’s tendency towards noncore products:

Innovativeness (INNO)

Our culture encourages people to look beyond the boundaries of our current business practices and our normal busi-ness model.

We can accept and run with ideas that were “not invented here.”Our culture encourages the development of new, innovative products.

Risk-taking (RISK)

We are willing to pursue ideas for new noncore products based on gut feel, even if hard data (e.g., market research) issketchy or unavailable.

We are willing to fund new noncore products even if the initial projection of earnings maybe low compared to the corebusiness and future growth is uncertain.

We are willing to develop and commercialize new noncore products even if they are likely to cannibalize our core business.

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Proactiveness (PROA)

In general, top management of our newspaper tends to focus on …improving the existing businesses. 1 2 3 4 5 developing new businesses independent from existing businesses.

In general, top management of our newspaper favors a strong emphasis on developing products that lie …within the boundaries of current business practice 1 2 3 4 5 outside the boundaries of current business practice.

When it comes to new product development, top management of our newspaper tends to go with products …That our current lead customers would want 1 2 3 4 5 That would cater to potential new customers

*When it comes to utilizing capabilities of the digital media, top management of our newspaper tends to…Focus on delivering news in innovative way 1 2 3 4 5 Focus on developing new, innovative noncore products

Business Model Innovation Adoption (BMIA)

How much of revenue from noncore products are generated through traditional revenue sources such as circulation, display advertising, andclassified advertising?Almost all 1 2 3 4 5 Almost none

How do you sell your noncore products?Existing sales force sells both core and noncore products. 1 2 3 4 5 Noncore products are exclusively sold through digital media sales force.

How many new formal or informal arrangements for information exchange with your partners have been created in the past 3 years?No new arrangements 1 2 3 4 5 Very many new arrangements

In the last 3 years, have you changed your pricing structure for print or online products?We have made no changes to our pricing structure. 1 2 3 4 5 We have completely changed our pricing structure.

Please compare the value propositions offered by your products/services now with those offered 3 years ago.They are pretty much the same 1 2 3 4 5 They are dramatically different

Please compare the cost structure of means employed to produce the noncore products with that employed to produce the core products.Cost structure for noncore product is much higher 1 2 3 4 5 Cost structure for noncore products is much lower

Business Model Performance (BMP)

Please estimate the percentage change in total number of advertisers from 3 years ago:Increase Decrease Not Much Change

If Decrease or Increase, please select a percentage: about 5%, about 10%, about 15%, about 20%, about 25%, about 30%, about 35%, about 40%, above40% Please estimate the percentage of annual revenue from all online sources. Please select:under 1%, 1%–5%, 6%–10%, 11%–15%, 16%–20%, 21%–25%, 26%–30%, 31%–35%, over 35%

On average, how many noncore products do you have on a monthly basis? Please select:0; 1–5; 6–10; 11–15; 16–20; 21–25; 26–30; 31–50; 51–99; 100 or more

Please estimate total audience reached by all your products on a weekly basis. Please select:under 10%; 10%–19%; 20%–29%; 30%–39%; 40%–49%; 50%–59%; 60%–69%; 70%–79%; 80% or over

Note: * The initial results of the measurement model indicated it was necessary to drop this indicator.

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Biographies

Jahangir Karimi received the Ph.D. degree in Management Information Systems from the University of Arizona. He serves as the discipline director forInformation Systems Program at the School of Business, University of Colorado Denver. His research interests include information technology manage-ment in national and international environments, IT-enabled E-business transformation, and new E-business models. Dr. Karimi has published in MIS Quarterly,Information Systems Research, Communications of the ACM, Journal of Management Information Systems, IEEE Transactions on Software Engineering, IEEE Trans-actions on Engineering Management, Decision Sciences, Journal of Systems and Software, Information and Software Technology, Concurrency: Practice and Experience,several books and conference proceedings. He is on the editorial board of International Journal of Electronic Commerce, and is a member of the Associ-ation for Information Systems. E-mail: [email protected]

Zhiping Walter received the Ph.D. degree in Business Administration, specializing in Management Information Systems, from the Simon School of Busi-ness, University of Rochester. She is currently an Associate Professor of Management Information Systems at the Business School, University of ColoradoDenver. Dr. Walter’s research interests are in the areas of online information seeking, online consumer behavior, and disruptive innovations. Her researchhas appeared in Communications of the ACM, Decision Support Systems, International Journal of Electronic Commerce, and European Journal of Operational Re-search, among others. E-mail: [email protected]

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ARTICLE IN PRESS - freescholar.yolasite.com · Inthisstudy,wecontributetoCEliteraturebyinvestigatingtheeffectsofCEondisruptiveBMIadoptionandtheeffects ofdisruptiveBMIadoptiononbusinessmodelperformance