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Information Technology Investment and Commercialized Innovation Performance:

Dynamic Adjustment Costs and Curvilinear Impacts

Prasanna P. Karhade

Department of Information Technology Management

Shidler College of Business

University of Hawaiʻi at Mānoa

2404 Maile Way, Honolulu, HI 96822, USA

Email: karhade@hawaii.edu

John Qi Dong

Trinity Business School

Trinity College Dublin, University of Dublin

Dublin 2, Ireland

Faculty of Economics and Business

University of Groningen

9747 AE Groningen, The Netherlands

Email: john.dong@tcd.ie

Prasanna P. Karhade is the Shidler College Faculty Fellow and a Faculty Fellow at the

Pacific Asian Center for Entrepreneurship (PACE) at the Shidler College of Business,

University of Hawai’i at Mānoa. He holds a Ph.D. in Business Administration from the

University of Illinois at Urbana-Champaign. He also earned a Master of Science degree

(Computer Science) from Georgia State University and a Bachelor of Engineering degree

(Computer Engineering) from Sardar Patel College of Engineering, University of Mumbai.

He worked as a software engineer for about thirty months before embarking upon his

academic journey. His research interests, which include IT governance, and the impact of IT

on firm innovation, are at the intersection of Management Information Systems and Strategic

Management. His research has been published in Information Systems Research, MIS

Quarterly and Journal of Management Information Systems.

John Qi Dong is Full Professor of Business Analytics at the Trinity Business School at

Trinity College Dublin, University of Dublin, and was an associate professor with tenure in

the Faculty of Economics and Business at University of Groningen. His research interests

include the value and management of business analytics, the strategy and impact of digital

innovation, as well as a variety of topics related to knowledge management and

organizational learning. His work has been published or is forthcoming in MIS Quarterly,

Strategic Management Journal, Journal of Management, Journal of the Association for

Information Systems, Journal of Product Innovation Management, European Journal of

Information Systems, and Journal of Strategic Information Systems, among others. He serves

as associate editor for Journal of the Association for Information Systems and Information

and Management, as well as editorial board member for Journal of Strategic Information

Systems.

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ACKNOWLEDGEMENTS

We thank the senior editor Indranil Bardhan, the associate editor and three anonymous

reviewers for their constant developmental feedback. We are very grateful to Jinyu He,

Yasemin Kor, Joseph Mahoney, Arun Rai, Danchi Tan and Sean Xu for their constant

support, guidance and encouragement. Also, thanks to the Center for European Economic

Research (ZEW) for providing the data used in this research. This research was partially

funded by the Hong Kong Research Grants Council (HKUST #641612). The Hong Kong

University of Science and Technology, the University of Groningen and the University of

Hong Kong also provided partial financial support for conducting this research.

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Information Technology Investment and Commercialized Innovation Performance:

Dynamic Adjustment Costs and Curvilinear Impacts

ABSTRACT

A firm’s investment in information technology (IT) has been widely considered as a key

enabler of innovation. In this study, we intend to integrate prior findings for augmenting

pathways (whereby IT investment supports innovation) with a new theory for suppressing

pathways (whereby dynamic adjustment costs associated with IT investment can be

detrimental to innovation) to propose an overall inverted U-shaped relationship between IT

investment and commercialized innovation performance (CIP). To test our theory, we

analyzed a unique panel dataset from the largest economy in Europe and discovered a

curvilinear relationship between IT investment and CIP for firms across a broad spectrum of

industries. Our research presents empirical evidence corroborating the augmenting and

suppressing pathways linking IT investment and CIP. Our findings can serve as a cautionary

signal to executives, discouraging overinvestment in IT.

Keywords: information technology investment; business value of information technology;

commercialized innovation performance; dynamic adjustment costs; overinvestment; digital

innovation; curvilinear relationships

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INTRODUCTION

Recent years have witnessed a proliferation of research on a positive relationship

between firms’ investment in information technology (IT) and various innovation outcomes

(e.g., Bardhan et al. 2013; Gómez et al. 2017; Huang et al. 2020; Karhade and Dong 2020;

Kleis et al. 2012; Ravichandran et al. 2017; Saldanha et al. 2017; Trantopoulos et al. 2017;

Xue et al. 2012). Although prior studies have documented various benefits stemming from IT

investment for innovation, there is also some evidence on the limits to these benefits (e.g.,

Chircu and Kauffman 2000; Sherif et al. 2006). In particular, Aral and Weill (2007) find no

impact of IT investment on commercialized innovation performance (CIP) — sales of new

products or services that are commercialized in the marketplace. It is unclear how to resolve

this empirical puzzle given the mixed findings in the digital innovation literature.

Not every investment for digital innovation is advantageous as it may expose firms to

unwarranted risks (Grover and Kohli 2013; Kohli and Grover 2019). For instance, more than

50% large IT projects have been found to experience significant failure and cost overrun

(Bloch et al. 2012), as such endeavors often require costly adjustments before any innovation

outcomes can be realized. Since large IT projects can require organization-wide adjustments

to business processes (Karimi et al. 2007), they tend to be significantly riskier (March and

Shapira 1987; McFarlan 1981). These discussions imply that a systematic understanding of

hindrances that curtail innovation benefits of IT investment is much needed. Our research

motivation is to reconcile the mixed findings in the prior research by theorizing the overall

relationship between IT investment and innovation with an integrative view incorporating

both the augmenting and suppressing pathways through which IT investment influences CIP.

As we discuss next, we theorize suppressing pathways linking IT investment and CIP by

introducing dynamic adjustment costs (DACs) to the IS literature.

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In prior strategic management research, Tan and Mahoney (2005) maintain that, from

a resource-based perspective, a firm that alters its resources for developing capabilities to

meet market change adaptively is likely to incur dynamic adjustment costs (DACs), because

adjustments of resources can often disrupt current operations. In our theory embedded in the

context of IT investment and innovation, we define DACs as the costs that firms incur in

making frequent and costly adjustments stemming from their investment in IT resources for

developing innovation capabilities to meet market changes adaptively. Although DACs

stemming from resource alterations such as investments in human resources and acquisitions

of external resources have been investigated in strategic management research (e.g., Argyres

et al. 2019; Kor et al. 2016; Penrose 19591; Tan and Mahoney 2005), understanding of such

costs in IS research continues to remain scant. While systematic theorization of DACs is

missing, prior IS literature has pinpointed some costs associated with IT investment that may

contribute to DACs. We discuss these potential sources of DACs next.

Costly adjustments may be required as new IT systems have to be integrated within

the existing ecosystem of IT systems. Firms incur DACs when systematically integrating new

IT systems with existing IT systems or upgrading incumbent IT systems to make them

compatible (Sikora and Shaw 1998). Similarly, as IT investment often leads to the

deployment of new IT systems or significant upgrades to incumbent IT systems, it could

require employees within the firm to redesign or reinvent their work routines and

corresponding social subsystems (Ryan and Harrison 2000) before IT investment yield the

intended benefits. Additionally, as the investment in IT increases, it requires firms to deal

with larger amounts of data. Data lifecycles can be difficult to manage, and thus the costs in

managing data can often swell up dynamically as firms not only have to manage data storage

1 Penrose (1959) uses DACs to examine the limits to the growth of the firm, a.k.a., the Penrose effect. However,

DACs-based theory as a broad theoretical perspective — which needs to be decoupled from the Penrose effect

— can be fruitfully leveraged to study various resource investments (e.g., Argyres et al. 2019; Kor et al. 2016).

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costs, but also have to deal with costs of conducting data backups and other information

management costs for ensuring business continuity (Goodhue et al. 1992; Tallon 2010). In

the age of big data, many firms are struggling with the problem of information overload as a

result of using IT (Karhade and Dong 2020). It might be tempting to leverage outsourcing as

a solution to cut costs; but the act of outsourcing IT itself involves a wide array of hidden

costs in transitioning to the vendor and re-transitioning back from IT vendors (Barthélemy

2005). When IT projects are large, it often requires complex coordination across multiple

stakeholders (Williams and Karahanna 2013). In summary, though sources of DACs have

been discussed, systematic theory development relying on DACs is still missing.

In the digital innovation literature, calls for research highlight the need to investigate

hindrances that plague innovation outcomes of IT investment (Kohli and Melville 2019).

Strategic management scholars highlight that DACs need to be in the foreground when

theorizing and understanding how investments in resources (in our case, IT) lead to the

hindrances to innovation (Argyres et al. 2019; Kor et al. 2016). Therefore, we integrate

research on digital innovation with theory on DACs by incorporating both the augmenting

and suppressing pathways linking IT investment and CIP. Since CIP captures the sales of

new products or services in the marketplace, theory on DACs is particularly suitable for our

purpose as it systematically examines resource investments for developing capabilities to

meet market change adaptively. We theorize that when firms make high IT investment, they

are likely to incur high DACs, and the overall relationship between IT investment and CIP is

likely to be of an inverted U-shape.

To test our theory, we construct a panel dataset consisting of 3129 firm-year

observations in Germany between 1997 and 2004. Analysis of this unique dataset

corroborates the hypothesized curvilinear impacts of IT investment on CIP. Since DACs are

latent and at best can be conjectured ex post (Kor et al. 2016), we develop a three-pronged ex

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post validation strategy to validate our DACs-based theory. First, when firms make high IT

investment, they incur high DACs as evident by high innovation costs. Second, when firms

make high IT investment, they incur high DACs as proxied by a summative index that we

create based on different sources of DACs. Third, we measure innovation cost savings in the

subsequent period after IT investment is made and find that innovation cost savings decrease

when firms make high IT investment, indirectly corroborating our DACs-based theory.

Overall, this study makes important contributions to the digital innovation literature by

deepening our knowledge of the link between IT and innovation by incorporating both

enablers and hindrances stemming from IT investment for CIP, and by unveiling the non-

linearities in the relationship between IT and innovation.

The rest of the paper is organized as follows. Next, we present the research

background and motivation, followed by theory development which brings DACs into the

foreground when investigating the relationship between IT investment and CIP. We then

report our empirical methodology and results. Further, we elaborate on a three-pronged

research design for ex post validation of our DACs-based theory. Finally, we discuss

implications and limitations of our study and outline directions for future research.

RESEARCH BACKGROUND AND MOTIVATION

Investigating how IT contributes or hinders the innovation activity is central to IS

research. Table 1 summarizes our research motivation to integrate prior research on the

augmenting and suppressing pathways linking IT and innovation. Next, we discuss what we

have learned about these two pathways and what we need to learn by integrating them.

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Table 1: Research Motivation: Integrating Augmenting and Suppressing Pathways

Research Main Findings from Notable Papers

The augmenting pathway: how IT investment supports innovation

IS Research Tambe et al. (2012): Digital extroversion, i.e., external focus enabled by IT investment, is beneficial for innovation Bardhan et al. (2013): IT and R&D investments are together beneficial for innovation Trantopuolos et al. (2017): IT investment for knowledge absorption in combination with external knowledge is beneficial for innovation

The suppressing pathway: how IT investment hurts innovation

Strategic Management Research Penrose (1959): Finite managerial capacity limits the rate of growth Tan and Mahoney (2005): Adjusting resources can be costly leading to inefficiencies Kor et al. (2016): Dynamic adjustment costs should be in the foreground when theorizing innovation

IS Research Ryan and Harrison (2000): IT payoffs are diminished owing to social subsystem costs Barthélemy (2001): Benefits of IT outsourcing are eaten away by hidden costs Tallon (2010): Firms incur high data management costs

Integrating augmenting and suppressing pathways

This Study Addresses calls for research on DACs (Kor et al. 2016) when examining the link between IT and innovation by 1) Theorizing curvilinear impacts of IT investment on innovation 2) Developing three-pronged ex post strategy for validating DACs-based theory

What We Learned: An Overview of Relevant Literature

The Augmenting Pathway: How IT Supports Innovation

IT investment can be beneficial for innovation and we group such prior literature to

represent augmenting pathways linking IT and innovation. IT plays a critical role in

coordinating various business processes (Chang and Gurbaxani 2012; Gurbaxani and Whang

1991) and assisting firms in the innovation activity (Bardhan et al. 2013; Nambisan et al.

2017; Sambamurthy et al. 2003) along three primary dimensions.

First, IT enables firms to better understand customer needs in developing desirable

new product features (Pavlou and El Sawy 2006; Saldanha et al. 2017). Second, IT assists

firms to better forecast market demand for effectively meeting the demand of new products or

services (Gómez et al. 2017; Rai et al. 2006). Third, IT investment helps firms to better

identify opportune times to launch their new product or service offerings (Joshi et al. 2010;

Tambe et al. 2012). In summary, there is empirical evidence supporting a positive linear

relationship between IT investment and innovation outcomes, such as patents (e.g., Gómez et

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al. 2017; Kleis et al. 2012; Ravichandran et al. 2017; Saldanha et al. 2017; Xue et al. 2012),

and new product development (e.g., Banker et al. 2006; Joshi et al. 2010; Pavlou and El Sawy

2006; Tambe et al. 2012). With regard to CIP, however, prior research documents no

significant impact of IT investment (Aral and Weill 2007).

The Suppressing Pathway: How IT Hurts Innovation

Although prior research highlights how IT augments innovation outcomes, findings

on how IT may hurt innovation continues to remain relatively scant in the literature. We

group these findings to represent suppressing pathways linking IT and innovation. Firms can

face various barriers when deploying new IT resources, which limit the benefits from IT

investment. In particular, large investments in IT may not always contribute to developing

stronger capabilities for innovation (Aral and Weill 2007). Costly interventions and

organization-wide adjustments are necessary for value creation from large IT projects (Sherif

et al. 2006), leading to disruptions in the innovation activity along the following dimensions.

First, firms may incur high integration costs of technological systems and social

systems, because large IT investment can disrupt existing operations in the innovation

activity and the broader social ecosystems within which new IT systems operate (Ryan and

Harrison 2000; Sikora and Shaw 1998). Second, firms can incur high costs as they grapple

with data management challenges when heavily investing in IT, leading to big data

challenges (Tallon 2010) and information overload problems (Karhade and Dong 2020) in the

innovation activity. Third, in the IT outsourcing context, hidden costs could also be

prohibitively high as firms outsource IT operations for innovation initiatives. They may be

forced to internalize IT operations with high costs if relationships with vendors’ breakdown

(Barthélemy 2001). Finally, when IT investment is large, multiple stakeholders are often

involved in the innovation activity, resulting in high costs of governance and coordination

(Williams and Karahanna 2013). All these findings suggest that large IT investment may

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require adjustments and cause disruptions in the innovation activity. Our motivation is to

investigate hindrances from IT investment for innovation and incorporate these insights as we

comprehensively study the relationship between IT investment and CIP.

What We Need to Learn: Integrating the Augmenting and Suppressing Pathways

Recent research suggests that DACs need to be in the foreground for theorizing and

understanding the hindrances for innovation (Kor et al. 2016). While prior research provides

some insights on the costs and disruptions due to IT investment as listed above, systematic

theorization of both enablers and hindrances from IT investment for innovation and a large-

scale econometric analysis of the overall relationship between IT investment and CIP are

missing. We address this gap by integrating the augmenting and suppressing pathways

through which IT investment influences CIP in our theory development. In addition to an

augmenting pathway, we theorize a suppressing pathway that suggests large IT investment is

likely to induce high DACs and reduce CIP.

THEORY DEVELOPMENT

The Augmenting Pathway: Moderate IT Investment Strengthens CIP

We propose that increasing IT investment to moderate levels can bring various

benefits for CIP — sales of new products or services that are commercialized in the

marketplace — for three reasons. First, firms aspire to develop new products or offer new

services with desirable set of features for consumers. IT investment enables firms to gather

and learn from market information about shifting consumer needs and develop new products

or services with desirable features (Pavlou and El Sawy 2006; Saldanha et al. 2017). More

desirable features can enhance sales of new products or services in the marketplace. Second,

succeeding in commercialization also requires firms to produce and offer new products or

services in appropriate quantities. IT investment can support a focal firm’s coordination with

suppliers and customers to jointly forecast market demand (Karhade and Dong 2020; Rai et

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al. 2006), allowing the firm to produce and deliver the optimal volume of new products or

services, thereby, meeting market demand and generating more sales. Third, market launch of

new products or services has a temporal dimension of commercialization. IT investment

enables firms to identify opportune times for introducing new products or services (Tambe et

al. 2012) — identifying and leveraging seasonal trends based on consumer and consumption

data — which promote sales of new products or services in the marketplace.

In summary, increasing IT investment to moderate levels enables the development of

innovation capabilities vital for developing and commercializing new products or services

(Joshi et al. 2010), leading to superior CIP. However, the innovation benefits are not likely to

increase proportionally to the increase of IT investment, because all resource investments are

subject to the law of diminishing returns in production (Samuelson and Nordhaus 2001).

Adding one more IT system may generate greater per-unit benefits when IT resources are

limited than when IT resources are in plenty, especially if new IT investment generates slack

and loose discipline of resource deployment for innovation (Nohria and Gulati 1996). Thus,

we expect that increasing IT investment strengthens CIP with diminishing marginal benefits.

The theoretical mechanism that shapes the overall impact of IT investment on CIP is

driven not only by the benefits for innovation but also the costs associated with IT investment

in the innovation activity. The costs of managing IT investment for innovation can often

increase with the size of investment (e.g., McFarlan 1981), as large IT investments are

inherently difficult and costly to coordinate. When firms don’t make high investment in IT,

disruptions associated with coordinating low to moderate levels of IT investment are not

high. Within moderate levels of IT investment, we expect that the aforementioned three

benefits from IT investment are likely to outweigh its DACs. In the absence of costly

adjustments, a firm’s IT investment is likely to strengthen its CIP.

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The Suppressing Pathway: High IT Investment Reduces CIP

From DACs-based perspective, knowledge workers involved in the innovation

activity are boundedly rational (Simon 1955; Penrose 1959). In other words, knowledge

workers have a limited ability to envision how the innovation activity will unfold over time,

because they only have access to limited information about the market as they engage in the

innovation activity. Furthermore, the outcomes of innovation activity are inherently uncertain

and unpredictable (Nelson and Winter 1982). As a consequence of bounded rationality,

knowledge workers are required to adjust their operations for innovation (Argyres et al.

2019). These adjustments can be frequent and costly (e.g., Kor et al. 2016). The nature of

adjustments that might need to be undertaken, and, the corresponding DACs, are not known

ex ante and can only manifest dynamically as the innovation activity unfolds over time.

We propose that, when firms heavily invest in IT, they are likely to incur high DACs

greater than the benefits from IT investment, reducing CIP, for three reasons. First, as IT

investment expands from moderate to high levels, the digital channels available to a firm and

the diversity of information a firm is exposed to rapidly increases (Gómez et al. 2017;

Trantopoulos et al. 2017). Knowledge workers are boundedly rational, given their limited

information processing ability and attention span, can find it increasingly difficult to utilize

abundant information for developing new products or services. High diversity of information

can be a source of high DACs and can distract the limited attention of knowledge workers,

who can be overwhelmed by information overload in the innovation activity (Karhade and

Dong 2020). Owing to the inability of boundedly rational knowledge workers in identifying

and processing market information for innovation, firms can develop new products or

services with lesser desirable features and inferior sales in the marketplace. As IT investment

increases to high levels, limits on cognitive capacity of knowledge workers make it costly

and even disruptive to adjust the innovation activity in an information-rich environment (e.g.,

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Tambe et al. 2012). As a result, high DACs stemming from high IT investment are likely to

outweigh its benefits for innovation, leading to lower CIP.

Second, when IT investment increases to high levels, firms can incur high costs as

new or upgraded IT systems from new investment need to be integrated into the ecosystem of

incumbent IT systems (e.g., Sikora and Shaw 1998). This can often require firms to redesign

their business processes, leading to inefficiencies in demand forecasting. Particularly when

firms invest in large IT systems, the broader socio-technical ecosystem in which the new

system operates can be disrupted, forcing firms to incur non-trivial adjustments of labor skills

and work practices (Ryan and Harrison 2000). The necessity of redesigning the organization-

wide as well as boundary-spanning business processes can be a source of high DACs. If the

new investment is not fully integrated within the existing socio-technical ecosystem, benefits

from IT investment can be curtailed and outweighed by its disruptions (Nevo and Wade

2010), resulting in lower CIP when benefits are eaten away by high DACs.

Third, when IT investment expands to high levels, costs of governing large IT

projects involving multiple stakeholders can rapidly increase (Williams and Karahanna

2013), as massive coordination of stakeholders in deployment of new IT resources can be

difficult to govern. Disruptions resulting from governance and coordination challenges in any

stage of the innovation activity can delay new product introduction. Given the strategic

necessity of well-timed launches for successful commercialization (Tambe et al. 2012),

delayed launches may force firms to slash their prices leading to lower CIP. Although the

importance of timing new product launch can vary across industries, well-timed launches

generally promote sales in most marketplaces. Costs of governing large IT investment is thus

another source of high DACs and can outweigh the benefits of IT investment. Delays in new

product launches due to the difficulty of governance can be detrimental to CIP, as late market

entrants cannot command high price markups.

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In summary, as firms make investment in IT from modest to high levels, they are

likely to incur high DACs, and CIP is likely to decrease as 1) leveraging market information

for developing desirable product features can be difficult, 2) integrating new IT systems to

the existing ecosystem can lead to inefficiencies in demand forecasting, and 3) governing and

coordinating the complex stakeholders can result in delays in new product launches. For all

these reasons, the benefits for CIP are likely to be eaten away by high DACs from IT

investment. It is worth noting that DACs are not likely to increase proportionally to the

increase of IT investment, as large investment in complex IT systems require costly

adjustments of the innovation activity and generate considerable disruptions to current

operations. Thus, when IT investment increases from moderate to high levels, DACs can

increase exponentially and become inevitably high.

Integrating the augmenting and suppressing pathways, we propose that CIP is likely

to increase with IT investment from low to moderate levels and is likely to decline with IT

investment from moderate to high levels. Two latent theoretical mechanisms with

diminishing benefits and exponentially increasing costs can additively lead to an inverted U-

shaped relationship (Haans et al. 2016). Therefore, we hypothesize that IT investment is likely

to have an inverted U-shaped relationship with CIP.

METHODOLOGY

Data

To test the relationship between IT investment and CIP, we construct a large-scale

panel dataset from the Center for European Economic Research’s (ZEW) Mannheim

Innovation Panel (MIP) database2. ZEW follows the Oslo Manual (OECD 2005) to annually

collect innovation data ensuring rigorous methods and valid instruments for data collection.

Germany, the largest national economy in Europe, is a suitable context because it is an

2 Available on http://doi.org/10.7806/zew.mip.2013.v1.suf.

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innovative country, ranked comparably to the U.S. (Hollanders and Arundel 2006). Every

year since 1993, a random sample of firms with at least 5 employees from a broad spectrum

of industries in Germany are surveyed for constructing the MIP. Table 2 describes our

representative sample distribution across different industries.

Table 2: Sample Distribution

NACE Rev. 1 Code

Industry Obs. Percentage

(%)

10-14 Mining and quarrying 32 1.02

15, 16 Food and tobacco 87 2.78

17-19 Textiles and clothing products 63 2.01

20-22 Wood and paper 104 3.32

23, 24 Chemical products 84 2.68

25 Rubber and plastic products 99 3.16

26 Glass and ceramics 59 1.89

27, 28 Metals 198 6.33

29 Machinery 167 5.34

30-32 Computers, electrical & communication equipment

111 3.55

33 Medical, precision and optical instruments 100 3.20

34, 35 Transport equipment 46 1.47

36 Furniture, sports equipment, games and toys 37 1.18

50, 52 Retail trade 220 7.03

51 Wholesale trade 251 8.02

60-63, 641 Transport 360 11.51

70, 71 Real estate activities 108 3.45

72, 642 Computer related activities and telecommunications

130 4.15

73, 742, 743 Scientific and technical activities 344 10.99

741, 744 Business related services 173 5.53

745-748, 90 Other services 356 11.38

Total 3129 100

Notes: NACE (Nomenclature of Economic Activities) is the European classification of economics activities, in line with the Standard Industrial Classification (SIC) in the United States.

Voluntary mail questionnaires were primary means of data collection. Questionnaires

were sent to firms in early spring with reminders later in the spring and early summer. An

advisory board monitored data collection ensuring high quality data. We are able to ascertain

the high quality of our data by replicating prior findings. For example, Bardhan et al. (2013)

focus on large U.S. firms and find a positive interaction effect of IT and R&D investment on

Tobin’s Q. We find a positive interaction effect of IT and R&D investments on CIP when

examining only large German firms. Gómez et al. (2017) report diminishing returns to R&D

for Spanish manufacturing firms. We also find diminishing returns to R&D investment for

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CIP when analyzing a subsample of manufacturing German firms. Overall, these replications

validate the high quality of our data.

Due to data availability for IT investment, we use the data from MIP database

between 1997 and 2004. This time span makes our study comparable to prior studies — for

instance, Bardhan et al. (2013) use data from the U.S. firms in 1997-2004. IT data and control

variables were collected in 1997, 1999, 2001 and 2003. Following a research design for

measuring outcomes with one-year time lag, CIP data were measured in the years of 1998,

2000, 2002 and 2004. For variables that were measured in interval scales, we convert the

values to the midpoints of intervals (e.g., Rai and Patnayakuni 1996). After removing missing

values, our dataset consists of a sample with 3129 firm-year observations from 2402 unique

firms spanning four years in 1997-2004.

Measures

CIP: We select a widely used measure for CIP — sales from new products or services

(Laursen and Salter 2006; Leiponen and Helfat 2010). A similar measure has also been used

in IS research (e.g., Aral and Weill 2007). We computed the natural logarithm to reduce the

skewness of this variable. Sales from new products or services is a fine-grained measure of

innovation performance, compared to a binary measure indicating whether new products or

services were launched to the market or a count measure indicating the number of patents or

new products (Gómez et al. 2017; Kleis et al. 2012). It is an appropriate measure as not all

industries have a high propensity for patenting. Table 3 shows descriptive statistics and

correlations. Our sampled firms had an average of 0.887 million Deutsche Mark (DM) sales

from new products or services, or 12% of total sales. Only 9 observations (0.3%) reported

that all their sales were attributed to new products or services in a specific year.

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Table 3: Descriptive Statistics and Correlations

(1) (2) (3) (4) (5) (6) (7) (8) (9)

(1) CIP

(2) ln(IT) 0.456

(3) ln(R&D) 0.593 0.500

(4) ln(Sales) 0.519 0.568 0.436

(5) Age -0.018 -0.032 -0.010 -0.069

(6) Location -0.118 -0.143 -0.083 -0.182 0.031

(7) Innovation Costs

0.071 0.016 0.059 0.013 0.005 -0.013

(8) Summative Index

0.133 0.069 0.069 0.097 -0.018 -0.111 0.691

(9) Innovation Cost Savings

0.230 0.105 0.115 0.049 0.053 -0.042 -0.004 0.017

Mean 0.635 0.147 0.159 2.128 0.031 0.434 0.443 0.658 0.017

SD 1.162 0.337 0.483 1.752 0.172 0.496 0.497 0.844 0.053

Min 0 0 0 -3.284 0 0 0 0 0

Max 6.412 3.767 5.101 9.934 1 1 1 2 0.620

Notes: Correlations in bold are significant with p < 0.05. IT investment is winsorized at 5%.

IT investment: We measure IT investment as the total expenditure in IT hardware,

software, services and personnel, which is widely used in past IS research (Bardhan et al.

2013, Gómez et al. 2017; Ravichandran et al. 2017). We computed the natural logarithm to

reduce the skewness of this variable. To address concerns stemming from outliers in IT

investment, we winsorized IT at 5% level. Winsorization reduces the average IT investment

of our sampled firms to 1% of total sales, with a maximum level of 9%.

Control variables: We control several factors that may influence CIP. First, we

control R&D investment which is measured by computing the natural logarithm of a firm’s

R&D expenditure (Bardhan et al. 2013). Second, we control firm size by computing the

natural logarithm of total sales (Kleis et al. 2012). Third, we control firm age by constructing

a binary variable indicating whether the firm was a new entrant three years prior to data

collection (0 = no, 1 = yes). Fourth, geographical location can offer advantages for

innovation (Lahiri 2010), so we control for this effect by including a binary variable

indicating whether a firm is located in East or West Germany (0 = West, 1 = East). Finally,

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we include industry dummies based on NACE Rev. 1 two-digit codes and year dummies to

control for industry and time fixed effects.

Analysis Strategy

To test the relationship between IT investment and CIP, we follow guidelines from

prior literature on testing curvilinear relationships (Haans et al. 2016; Lind and Mehlum

2010; Xue et al. 2011). Equation (1) presents our empirical model. To mitigate concerns

stemming from the endogeneity of IT investment, we use a fixed effects model to control for

time-invariant unobservable firm characteristics. We also use one-year time lag between IT

investment and CIP. In Equation (1), ln(Sales of New Products or Servicesit+1) indicates CIP

of firm i and year t+1, ln (ITit) indicates the IT investment made by firm i in year t, Xit

represents the vector of control variables, ui are time-invariant unobservable characteristics of

firm i, and eit represents the error term. If the hypothesized inverted U relationship does exist,

three criteria need to be fulfilled: 1) 𝛽1 > 0 and 𝛽2 < 0; 2) slope must be steep at both sides

before and after the turning point, and 3) the turning point needs to be within data range

(Haans et al. 2016, Lind and Mehlum 2010).

ln(𝑆𝑎𝑙𝑒𝑠𝑜𝑓𝑁𝑒𝑤𝑃𝑟𝑜𝑑𝑢𝑐𝑡𝑠𝑜𝑟𝑆𝑒𝑟𝑣𝑖𝑐𝑒𝑠𝑖𝑡+1) = 𝛽0 + 𝛽1ln(𝐼𝑇𝑖𝑡) + 𝛽2ln(𝐼𝑇𝑖𝑡)2 + 𝜇𝑋𝑖𝑡 +

𝑢𝑖 + 𝑒𝑖𝑡 (1).

To examine the first criterion, we estimate the fixed effects regression model as

shown in Equation (1). To examine the second criterion, we conduct the U test following

Lind and Mehlum (2010). To examine the third criterion, we calculate the turning point to

ascertain if it lies within the data range of IT investment (Haans et al. 2016).

RESULTS

Main Analysis

Table 4 reports fixed effects regression results. We first estimated a control model,

followed by a linear model with IT investment. We then added IT investment squared to

19

estimate a curvilinear model. To compare the model fit of different models, we used two

complementary information criteria — Akaike information criterion (AIC) and Bayesian

information criterion (BIC). AIC is often used to choose the most adequate model among

alternatives while BIC looks for the true model (Vrieze 2012). By comparing AIC and BIC

between control model and linear models, we found that the linear model demonstrated

greater AIC and BIC, indicating a worse model fit. However, the curvilinear model showed

smaller AIC and BIC, and therefore a better fit than the control and linear models. Thus, a

curvilinear model is the most suitable model to fit our data. In the linear model, we failed to

observe any significant effect of IT investment on CIP, consistent with prior finding (e.g.,

Aral and Weill 2007). In the curvilinear model, we found that IT investment had a

statistically significant and positive effect on CIP (𝛽1 = 0.847, p < 0.01) and IT investment

squared had a significant and negative effect on CIP (𝛽2 = -0.453, p < 0.01). Both these

findings meet the first criterion of testing an inverted U-shaped relationship

Table 4: Fixed Effects Regression Results

(1) (2) (3)

Control Model

Linear Model

Curvilinear Model

ln(IT) 0.113

(0.148) 0.847*** (0.290)

ln(IT)2 -0.453*** (0.154)

ln(R&D) 0.423*** (0.116)

0.397*** (0.121)

0.565*** (0.134)

ln(Sales) 0.143** (0.065)

0.139*** (0.066)

0.116* (0.066)

Age 0.036

(0.177) 0.035

(0.178) 0.028

(0.177)

Location -0.493 (0.608)

-0.489 (0.608)

-0.466 (0.605)

Industry Dummies Yes Yes Yes

Year Dummies Yes Yes Yes

Constant 1.033*** (0.397)

1.039*** (0.398)

1.009** (0.396)

R2 0.098 0.102 0.119

F 2.260*** 2.190*** 2.450***

AIC 1109.194 1109.608 1072.274

BIC 1266.454 1271.917 1241.631

Notes: N = 3129. * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is CIP in the subsequent year. IT investment is winsorized at 5%.

20

ln(Sales of New Products or Servicesit+1)

ln(ITit)

Figure 1: Plot of Curvilinear Relationships

We conducted the U test (Lind and Mehlum 2010), to examine whether the slope is

steep on both sides before and after the turning point. This test confirmed that at both sides

the positive and negative slopes were statistically significant (t = 2.780, p < 0.01), meeting

the second criterion for validating an inverted U-shaped relationship. Finally, we found that

the turning point appeared when IT investment was 0.935 within our data range (see Figure

1), fulfilling the third criterion. All these results support an inverted U-shaped relationship

between IT investment and CIP (Haans et al. 2016).

Robustness Checks

We conducted multiple tests to examine the robustness of our results. First, although

we controlled for time-invariant omitted variables by using a fixed effects model and

assuaged endogeneity concerns stemming from reverse causality by using one-year time lag

between IT investment and CIP, it does not address endogeneity caused by simultaneity. To

further address the endogeneity of IT investment, we used two complementary approaches.

First, we used instrumental variables available in our dataset to address endogeneity. We

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8

Plot

95% CI lowerbound

95% CI upperbound

21

consider 1) mergers and acquisitions (M&As) as a radical change of firm boundary that can

lead to significant shift in IT investment (Benitez et al. 2018), 2) geographical location in

East or West Germany that determines the overall levels of infrastructure and investment in

IT (Melville et al. 2004), 3) an industry dummy indicating high or low industry clock-speed

that drives firms’ IT investment (Mendelson and Pillai 1999), and 4) a Y2K dummy the year

before or after 2000 capturing the temporal change in firms’ IT investment (Anderson et al.

2006). 93 observations were dropped from this test due to missing values in the instrumental

variables. We found that our instruments are significantly correlated with IT investment

(Cragg-Donald Wald F = 7.864, p < 0.01), and more importantly, are valid instruments

(Hansen J = 4.345, p = 0.114). Two-stage least squares (2SLS) regression with clustering

standard errors by firm showed that the fitted value of IT investment had a significant and

positive effect on CIP and the fitted value of IT investment squared had a significant and

negative effect on CIP, supporting an inverted U-shaped relationship (see Table 5).

Second, we used an alternative approach to address endogeneity. Following

Bharadwaj et al. (2007), we used a two-stage Heckman model to control for the endogeneity.

In the first stage, we created a binary variable indicating 1 if a firm’s IT investment is high, as

greater than the mean in our sample, and 0 otherwise. We estimated a Probit model by

regressing this new binary variable on an exclusive restriction as the industry average IT

investment — which is expected to influence the firm’s IT investment (Mithas et al. 2013) —

as well as all control variables. The Probit model can explain 42% variation in the likelihood

that a firm’s IT investment is at a high or low level. Industry average IT investment had a

statistically significant and positive effect (𝛽= 2.452, p < 0.05). Based on the results of

Probit model, we then calculated the inverse Mills ratio (IMR), which captures the

endogenous variation in IT investment. In the second stage, we tested the relationship

between IT investment and CIP with the IMR as an additional control. Although the IMR was

22

statistically significant — implying the necessity of controlling for endogeneity — we still

found support for an inverted U-shaped relationship between IT investment and CIP after

controlling for endogeneity (see Table 5).

Table 5: Endogeneity Tests

(1) (2)

2SLS-IV Heckman

ln(IT) 5.789*** (1.285)

0.923*** (0.291)

ln(IT)2 -2.263*** (0.554)

-0.519*** (0.156)

Inverse Mills Ratio 0.700** (0.277)

ln(R&D) 0.847*** (0.198)

0.599*** (0.134)

ln(Sales) -0.050 (0.070)

0.519*** (0.173)

Age -0.042 (0.091)

0.066 (0.177)

Location Omitted for IV -0.511 (0.603)

Industry Dummies Omitted for IV Yes

Year Dummies Omitted for IV Yes

Constant 0.062

(0.055) -1.096 (0.922)

R2 0.336 0.133

F 81.290*** 2.610***

N 3036 3129

Notes: * p < 0.1; ** p < 0.05; *** p < 0.01. (Clustered) standard errors are in parentheses. Dependent variable is CIP in the subsequent year. IT investment is winsorized at 5%.

Furthermore, we examined the sensitivity of our results by using alternative measures.

First, we used an alternative ratio measure for CIP as the percentage of sales of new products

or services over total sales (e.g., Laursen and Salter 2006; Leiponen and Helfat 2010).

Correspondingly, we created a ratio measure for IT/R&D investment as IT/R&D expenditure

scaled by total sales (e.g., Bardhan et al. 2013; Mithas et al. 2013). We then estimated a

random effects double-censoring Tobit model and found consistent results, because 1) a ratio

measure of CIP has a lower bound of 0 and an upper bound of 1 is more appropriate to use a

Tobin model with censoring in both directions and 2) fixed effects are not available for Tobit

model (Laursen and Salter 2006; Leiponen and Helfat 2010). Again, the results supported an

inverted U-shaped relationship between IT intensity measure and CIP as a percentage of total

23

sales (see Table 6). Although we also found support for a linear model by using ratio

measures, AIC and BIC indicate a better model fit in favor of a curvilinear model.

Table 6: Random Effects Double-Censoring Tobit Regression Results with Ratio Measures

(1) (2) (3)

IT Intensity 1.409*** (0.463)

6.923*** (1.412)

IT Intensity2 -65.848*** (15.954)

R&D Intensity 3.953*** (0.241)

3.829*** (0.243)

3.738*** (0.243)

ln(Sales) 0.037*** (0.005)

0.038*** (0.005)

0.040*** (0.005)

Age 0.052

(0.044) 0.040

(0.044) 0.051

(0.044)

Location -0.010 (0.018)

-0.007 (0.018)

-0.003 (0.018)

Industry Dummies Yes Yes Yes

Year Dummies Yes Yes Yes

Constant -0.447*** (0.060)

-0.467*** (0.060)

-0.493*** (0.060)

Wald Chi-Square 650.240*** 658.680*** 673.770***

AIC 2876.294 2869.201 2854.067

BIC 3057.748 3056.703 3047.618

Notes: N = 3129. * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is the percentage of sales of new products or services over total sales in the subsequent year. IT/R&D intensity is the percentage of IT/R&D expenditure over total sales. IT investment is winsorized at 5%.

Second, we considered industry differences in the levels of IT investment3. Firms in

IT-intensive industries may systematically have higher IT investment, still below the

breakeven levels, than firms in other industries. To address this concern, we created an

alternative deviation measure for IT investment by subtracting from a firm’s IT investment its

industry average IT investment (e.g., Ho et al. 2017; Mithas et al. 2013). Therefore, our

alternative measure partials out industry differences and only captures a firm’s IT investment

deviating from the industry average level. We found support for an inverted U-shaped

relationship between this deviation measure of IT investment and CIP, corroborating our

theory (see Table 7). Also, a curvilinear model demonstrated the best model fit.

3 We thank one anonymous reviewer who suggested this test.

24

Table 7: Fixed Effects Regression Results with Deviation Measure of IT Investment

(1) (2) (3)

ln(IT – Industry IT) 0.123

(0.145) 0.640*** (0.237)

ln(IT – Industry IT)2 -0.411*** (0.149)

ln(R&D) 0.423*** (0.116)

0.394*** (0.121)

0.547*** (0.133)

ln(Sales) 0.143** (0.065)

0.139** (0.066)

0.120* (0.066)

Age 0.036

(0.177) 0.036

(0.178) 0.031

(0.177)

Location -0.493 (0.608)

-0.488 (0.608)

-0.458 (0.605)

Industry Dummies Yes Yes Yes

Year Dummies Yes Yes Yes

Constant 1.033*** (0.397)

1.052*** (0.398)

1.083*** (0.396)

R2 0.098 0.103 0.121

F 2.260*** 2.200*** 2.420***

AIC 1109.194 1107.994 1076.210

BIC 1266.454 1271.303 1245.568

Notes: N = 3129. * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is CIP in the subsequent year. IT investment is winsorized at 5% and subtracted from industry IT investment.

VALIDATIONS OF THEORETICAL MECHANISM

We conducted three tests to comprehensively validate our theory. Table 8 maps our

DACs measurements, in relationship with prior research. First, we use a binary variable

indicating the presence of DACs in the innovation activity experienced by a firm based on

whether the firm incurs very high costs for innovation (0 = no, 1 = yes) throughout the same

year when IT investment is made. Second, we create a summative index using both internal

and external sources of DACs based on whether the firm has internal organizational

problems for innovation and whether the firm was operating on missing external market

information for innovation (0 = no at all, 1 = yes for either question, 2 = yes for both

questions) throughout the same year when IT investment is made. This concurrent

measurement of DACs is appropriate as recalling hindrances after time lapses can be

difficult.

25

Table 8: Research Design for Ex Post Validation of DACs-Based Theoretical Mechanism

Measures of DACs in Prior Research

Measures of DACs in This Study

Key Features of Our Research Design

Innovation Costs

Tan and Mahoney (2005) capture DACs at the industry level

Overall measure for DACs: if innovation costs are very high

Firm-level data for capturing overall DACs

Internal Sources of DACs

Tambe et al. (2012) use a cross-sectional measure of organizational adjustments

Measure of internal sources of DACs: internal organizational problems for innovation

Longitudinal data for capturing internal sources of DACs

External Sources of DACs

No measures in the prior research

Measure of external sources of DACs: if organizations are operating on missing market information

Critical to capture external sources of DACs over time

Subsequent Innovation Cost Savings

No measures in the prior research

Ratio measure of reduction of DACs: cost savings related to innovation in the subsequent year after IT investment is made

Continuous scale to capture DACs using a panel structure with time lag

Finally, we measure innovation cost savings as the percentage of reduced costs related

to innovation over total costs in the subsequent year after IT investment is made. This

continuous measure of innovation cost savings in the subsequent time period is in alignment

with our cost-based theorization. The implicit argument is that if innovation cost savings

decline over time as firms make high investment in IT, that would indirectly support our

theoretical explanation that firms incurred higher DACs as they made high investment in IT.

A limitation of this measure is that we don’t have access to the total costs and are able to

validate our DACs-based theory only be relying on a ratio measure available to us. The three

tests for validating our theory are complementary as the first test uses a cross-sectional,

overall measure of DACs (i.e., high costs for innovation). The second test uses a summative

index based on both internal and external sources of DACs. The third test uses a time-lagged

measure of innovation cost savings — firms arguably incur higher DACs if subsequent

reduction of costs related to innovation is small.

First, we had a sample of 2907 firm-year observations as 222 observations have

missing values in the first proxy of DACs. 43.74% sampled firms experienced high DACs as

they incurred very high innovation costs. Since our first measure of DACs has a binary scale,

26

we conducted variance analysis by using ANOVA and t-test. We examine if firms making

high IT investment incur greater DACs compared to firms making low IT investment. We use

both mean and median of winsorized IT investment as a threshold to divide our sample into

two groups: 1) firms making high IT investment above the mean/median IT investment and

2) firms making low investment in IT below or equal to the mean/median.

As can be seen in Table 9, using mean of IT investment as the threshold, ANOVA

results show DACs incurred by firms making high IT investment were significantly different

compared to DACs incurred by firms making low IT investment (F = 4.990, p < 0.05). A t-

test indicates that DACs incurred by firms making high IT investment were significantly

higher compared to DACs incurred by firms making low investment in IT (t = 2.233, p <

0.05). Using median of IT as the threshold, ANOVA results show that DACs incurred by

firms making high IT investment were significantly different to DACs incurred by firms

making low IT investment (F = 6.490, p < 0.05). A t-test indicates that DACs incurred by

firms making high IT investment were significantly higher compared to DACs incurred by

firms that make low investment in IT (t = 2.548, p < 0.05).

Table 9: ANOVA and T-Test Results for Innovation Costs

Group N Mean SD F t

Mean DACs of firms with high IT investment 409 0.494 0.500

4.990** 2.233** DACs of firms with low IT investment 2498 0.435 0.496

Median DACs of firms with high IT investment 1442 0.467 0.499

6.490** 2.548** DACs of firms with low IT investment 1465 0.420 0.494

Notes: N = 2907. * p < 0.1; ** p < 0.05; *** p < 0.01.

Second, we had a sample of 2857 firm-year observations after excluding 272

observations with missing values in the second proxy of DACs. 41.16% sampled firms

experienced DACs — 1) internal organizational problems for innovation or 2) operating on

missing external market information for innovation. Correlation between our summative

index of DACs and IT is significant and positive (r = 0.069, p < 0.001). We then conducted

ANOVA and t-test based on the mean and median of winsorized IT investment. Table 10

reports the results. Using mean of IT as the threshold, ANOVA results show that DACs

27

incurred by firms making high IT investment were significantly different from those incurred

by firms making low IT investment (F = 20.980, p < 0.01). A t-test indicates that DACs

incurred by firms making high IT investment were higher than those incurred by firms that

make low IT investment (t = 4.580, p < 0.01). Using median of IT investment, ANOVA

results show that DACs incurred by firms making high IT investment were different from

DACs incurred by firms making low investment in IT (F = 28.620, p < 0.01). A t-test

indicates that DACs incurred by firms making high IT investment were significantly higher

compared to DACs incurred by firms that make low IT investment (t = 5.350, p < 0.01).

Table 10: ANOVA and T-Test Results for Summative Index

Group N Mean SD F t

Mean DACs of firms with high IT investment 403 0.836 0.868

20.980*** 4.580*** DACs of firms with low IT investment 2454 0.629 0.837

Median DACs of firms with high IT investment 1420 0.743 0.859

28.620*** 5.350*** DACs of firms with low IT investment 1437 0.575 0.821

Notes: N = 2857. * p < 0.1; ** p < 0.05; *** p < 0.01.

Furthermore, we conducted regression analysis to examine the impact of IT

investment, in comparison to the impact of R&D investment, on the first and second proxies

of DACs. We used a conditional fixed effects logit model to regress the first binary proxy on

IT and R&D investments. Due to all positive or negative outcomes in estimation, 2207

observations were dropped leading to a sample of 700 observations in this analysis. We found

that the 2207 dropped observations and the 700 observations are not significantly different in

all variables included in our model (overall DACs: t = 0.506, p = 0.613; IT investment: t =

1.456, p = 0.146; R&D investment: t = 0.402, p = 0.688; firm size: t = 0.051, p = 0.959; firm

age: t = 1.580, p = 0.114)4. We controlled firm size measured by the natural logarithm of total

sales and firm age. We also included industry and year dummies to control for

heterogeneities across industries and temporal progressions in firms’ innovation activity. As

Model (1) in Table 11 shows, IT investment had a statistically significant and positive effect

4 We thank one anonymous reviewer who suggested this test.

28

on the likelihood of very high costs for innovation, suggesting that as firms make greater

investment in IT, there is a higher likelihood that they incur high DACs. However, R&D

investment did not have a statistically significant effect on the likelihood that firms incur high

DACs. Taken together, these results indicate IT investment, rather than R&D investment, is

likely to induce higher DACs in the innovation activity.

We used an ordered logit model to regress the second proxy for DACs — a

summative index for DACs from both internal and external sources — on IT and R&D

investments, because the summative index has an ordinal scale from 0 to 2. As fixed effects

are not feasible for ordered logit model, we used a random effects model in this analysis.

Again, we controlled for firm size, firm age, industry and year dummies. As Model (2) in

Table 11 shows, IT investment had a statistically significant and positive effect on the

summative index, suggesting that as firms make greater IT investment, there is a higher

likelihood that they incur high DACs from internal and external sources. Again, R&D

investment did not have a statistically significant effect on the summative index. Consistent

with previous results for the first proxy for DACs, these results also indicate IT investment,

rather than R&D investment, is likely to induce higher DACs.

Table 11: Logistic Regression Results

(1) (2)

Conditional FE Logit Model RE Ordered Logit Model

DV: Overall DACs DV: Internal & External

Sources of DACs

ln(IT) 8.550*** (3.287)

0.440** (0.198)

ln(R&D) 13.745

(13.990) -0.122 (0.135)

ln(Sales) -1.225 (1.262)

0.142*** (0.041)

Age 1.745

(1.507) -0.265 (0.303)

Industry Dummies Yes Yes

Year Dummies Yes Yes

Likelihood-Ratio/Wald Chi-Square 446.790*** 274.180***

N 700 2857

Notes: * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. IT investment is winsorized at 5%.

29

In the third test, we measured innovation cost savings as the percentage of reduced

costs related to innovation over total costs in one year later than when IT investment is made.

Thus, complementing concurrent measurements in the previous two tests, we indirectly

captured DACs in the subsequent year after IT investment is made. Our third proxy of DACs

has a continuous scale that is superior to the binary or categorical scale used in the first two

tests. We followed prior research (e.g., Laursen and Salter 2006; Leiponen and Helfat 2010)

to use a random effects Tobit model with double-censoring to examine the relationship

between IT investment and innovation cost savings with a ratio scale varying between 0 and

1. The missing values in innovation cost savings decreased our sample size to 2073

observations. Since innovation cost savings could be correlated with CIP, we further

controlled for a firm’s prior CIP. Since prior CIP and R&D investment are highly correlated

(r = 0.5, p < 0.01), we excluded R&D investment from the model to avoid multicollinearity

issues. To corroborate our theory, we expect an inverted U-shaped relationship between IT

investment and innovation cost savings — as IT investment increases, innovation cost

savings are likely to be eaten away as firms incur higher DACs in the innovation activity.

As Table 12 shows, AIC suggests that the curvilinear model has the best fit while BIC

indicates the best model fit with control variables only; both information criteria are not in

favor of the linear model. Since AIC selects the model that most adequately describes the

unknown reality and BIC tries to find the true model among alternatives, AIC could be more

appropriate to indicate model fit when the analysis is exploratory like ours (Brunham and

Anderson 2002). IT investment had a statistically significant and positive effect on

subsequent innovation cost savings (𝛽 = 0.080, p < 0.1) and IT investment squared had a

statistically significant and negative effect on subsequent innovation cost savings (𝛽 = -0.036,

p < 0.05). We also found that at both sides the positive and negative slopes were statistically

significant (t = 2.170, p < 0.05) and the turning point appeared when IT investment was 1.111

30

within our data range. These findings corroborate our theory that as firms make high IT

investment, they incur higher DACs, associated with subsequent reduced innovation cost

savings.

Table 12: Random Effects Double-Censoring Tobit Regression Results for Innovation Cost Savings

(1) (2) (3)

Control Model Linear Model Curvilinear Model

ln(IT) -0.002 (0.020)

0.080* (0.043)

ln(IT)2 -0.038** (0.018)

Prior CIP 0.049*** (0.007)

0.049*** (0.007)

0.048*** (0.007)

ln(Sales) 0.000

(0.005) 0.000

(0.005) -0.003 (0.005)

Age 0.030

(0.032) 0.030

(0.032) 0.031

(0.032)

Location -0.005 (0.013)

-0.005 (0.013)

-0.004 (0.013)

Industry Dummies Yes Yes Yes

Year Dummies Yes Yes Yes

Constant -0.295*** (0.053)

-0.295*** (0.053)

-0.294*** (0.053)

Wald Chi-Square 169.310*** 169.290*** 172.710***

AIC 683.325 685.318 682.577

BIC 846.791 854.421 857.317

Notes: N = 2073. * p < 0.1; ** p < 0.05; *** p < 0.01. Standard errors are in parentheses. Dependent variable is innovation cost savings in the subsequent year. IT investment is winsorized at 5%.

DISCUSSION AND CONCLUSION

Theoretical and Managerial Implications

Two key theoretical implications follow from our research. First, our study integrates

research on digital innovation (e.g., Kohli and Melville 2019; Nambisan et al. 2017) with

theory on DACs (e.g., Kor et al. 2016; Tan and Mahoney 2005), to deepen our understanding

of the complex impacts of IT on innovation. Along with the key role of enablers for

innovation, our research theorizes the important role of hindrances stemming from

adjustments associated with IT investment for innovation. Accounting for both the

augmenting and suppressing pathways can deepen our understanding of the dual latent

mechanisms through which IT influences innovation. In particular, theory on DACs is a

31

powerful theoretical lens to understand suppressing pathways and should be considered when

examining the impacts of IT investment on innovation.

Second, our research augments prior findings on a positive linear relationship of IT

and innovation (Gómez et al. 2017; Ravichandran et al. 2017; Saldanha et al. 2017;

Trantopoulos et al. 2017), by demonstrating a curvilinear relationship between IT investment

and CIP. Consistent with prior finding (Aral and Weill 2007), we also do not find support for

a positive linear relationship between IT investment and CIP. Instead, strong empirical

evidence corroborates a curvilinear relationship. Inspired by this line of finding, discovering

limits to returns to IT investment for innovation can reconcile the mixed findings in existent

digital innovation literature (e.g., Johns 2006) and more broadly, deepen our understanding of

the non-literalities underlying the IT-innovation relationship.

Our research also provides important managerial implications. IT payoffs are central

to IS research and practice, and the attention to IT payoffs has been recently shifted from

financial performance to innovation performance (e.g., Bardhan et al. 2013). Our empirical

findings on IT payoffs in innovation can guide managers in their decision making for IT

investment. First, given the advancements in IT, there could be a tendency for managers to

think of IT as the panacea for all performance problems. Undeterred by high failure rate of

large IT projects, managers continue to heavily invest in IT (e.g., Anderson et al. 2003; Ho et

al. 2017). Our research that reports evidence on possible detrimental effects of IT investment

on innovation should serve as a cautionary signal, encouraging managers to curb

overinvestment in IT.

Second, we analyze data from a large number of firms in Germany — the largest

national economy in Europe — and investigate the impacts of IT investment on CIP across a

broad spectrum of industries in this national setting. Our findings thus serve as a sound basis

for managers in many industries to benchmark their IT investment decisions for innovation

32

purposes. Furthermore, our finding on curvilinear impacts of IT investment on CIP suggests

that firms need to first experiment with small pilot projects before they make large IT

investment. Such a stepwise, experimental and incremental approach to making progressively

larger IT investment can arguably smoothen the need for costly adjustments and minimize

disruptions to current operations as the innovation activity proceeds.

Limitations and Future Research

Contributions of our research must be viewed in light of limitations. First, we analyze

a large-scale panel dataset from firms in a particular country. Future research can collect data

from other innovative countries to examine the generalizability of our findings. Second, our

research design employs an aggregate indicator of IT investment and does not allow us to

tease out the impacts of IT at the system level. While we are interested in examining the

entire IT resource bundle in line with the resource-based perspective in which DACs are

grounded (which are arguably incurred as new IT systems are required to be integrated into

the existing ecosystem with incumbent IT systems), future research may look into specific IT

systems (e.g., customer relationship management/CRM or supply chain management/SCM

systems) to refine our findings.

To assuage DACs associated with IT investment for innovation, firms can adopt one

or a combination of the two strategies that we discuss next. First, firms may consider

investing in their internal agility by training their employees for replenishing their digital

skills. This internally focused strategy could prove beneficial as knowledge workers with

replenished skills are likely to adjust the innovation activity more efficiently. Second, skilled

employees could also be brought in from the external environment to increase the flexibility

of an organization. However, skilled employees might still need time, to acclimatize

themselves to the routines of a focal firm, before they are productive for innovation (e.g., Tan

and Mahoney 2005). Future study on digital innovation can investigate the comparative

33

efficacy of internally and externally focused strategies outlined above. Research exploring

boundary conditions for these strategies could also yield interesting insights for assuaging

DACs associated with IT investment for innovation.

Conclusion

We investigate curvilinear impacts of IT investment on innovation by accounting for

both enablers and hindrances associated with IT investment for innovation. We hypothesize

an inverted U-shaped relationship between IT investment and CIP owing to high DACs

incurred by firms as they make large investment in IT. We analyze a large-scale panel dataset

collected from Germany and discover non-linearities in the relationship between IT

investment and CIP across a broad spectrum of industries. Our findings on the curvilinear

relationship between IT and innovation should serve as a cautionary signal to managers and

discourage them from overinvesting in IT.

34

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