DISENTANGLING THE RELATIONSHIP BETWEEN THE ADOPTION OF IN-MEMORY COMPUTING AND FIRM PERFORMANCE

Abstract

Recent growth in data volume, variety, and velocity led to an increased demand for high-performance data processing and analytics solutions. In-memory computing (IMC) enables organizations to boost their information processing capacity, and is widely acknowledged to be one of the leading strategic technologies in the field of enterprise systems. The majority of technology vendors now have IMC technologies in their portfolio, and the interest of companies in adopting such solutions in order to benefit from big data is increasing. Although there is first research on the business value of IMC in the form of case studies, there is a lack of large-scale quantitative evidence on the positive effect of such solutions on firm performance. Based on a unique panel data set of IMC adoption information and financial firm performance data for a sample of companies from the Fortune 500 list this study aims at explaining the relationship between the adoption of IMC solutions and firm performance. In this research in progress paper we discuss the theoretical background of our work, describe the proposed research design, and develop five hypotheses for later testing. Our work aims at contributing to the research streams on IT business value and business analytics by helping to better understand the nature of the interdependencies between IMC adoption and firm performance.

Full text

Twenty-Fourth European Conference on Information Systems (ECIS), İstanbul, Turkey, 2016 1
DISENTANGLING THE RELATIONSHIP BETWEEN THE
ADOPTION OF IN-MEMORY COMPUTING AND FIRM
PERFORMANCE
Research in Progress
Fay, Maria, University of Liechtenstein, Vaduz, Liechtenstein, maria.fay@uni.li
Müller, Oliver, IT University of Copenhagen, Copenhagen, Denmark, oliver.mueller@itu.dk
vom Brocke, Jan, University of Liechtenstein, Vaduz, Liechtenstein, jan.vom.brocke@uni.li
Abstract
Recent growth in data volume, variety, and velocity led to an increased demand for high-performance
data processing and analytics solutions. In-memory computing (IMC) enables organizations to boost
their information processing capacity, and is widely acknowledged to be one of the leading strategic
technologies in the field of enterprise systems. The majority of technology vendors now have IMC
technologies in their portfolio, and the interest of companies in adopting such solutions in order to
benefit from big data is increasing. Although there is first research on the business value of IMC in the
form of case studies, there is a lack of large-scale quantitative evidence on the positive effect of such
solutions on firm performance. Based on a unique panel data set of IMC adoption information and
financial firm performance data for a sample of companies from the Fortune 500 list this study aims at
explaining the relationship between the adoption of IMC solutions and firm performance. In this re-
search-in-progress paper we discuss the theoretical background of our work, describe the proposed
research design, and develop five hypotheses for later testing. Our work aims at contributing to the
research streams on IT business value and business analytics by helping to better understand the na-
ture of the interdependencies between IMC adoption and firm performance.
Keywords: In-memory computing, Business analytics, Business value of IT, Firm performance

Fay et al. /In-Memory Computing Adoption and Firm Performance
Twenty-Fourth European Conference on Information Systems (ECIS), İstanbul, Turkey, 2016 2
1 Motivation
Data management and analysis requirements of enterprises have changed over the past several years.
While traditionally analytical applications operated on historical data that has been updated in periodic
batch runs (e.g., weekly, monthly), companies now increasingly strive to gain insights from operation-
al data in order to make data-driven decisions in near real-time (Färber et al., 2012; Janiesch et al.
2012; vom Brocke et al., 2014).
A recent technological innovation addressing these new data processing demands is in-memory com-
puting (IMC). The key idea behind IMC is to keep all data and applications in a computer’s main
memory in order to avoid expensive mechanical hard-drive I/O access (Chaudhuri, Dayal, & Nara-
sayya, 2011), which can result in enormous increases in data processing speed. For example, SAP re-
ports improvements in transaction processing speed by factors up to 100,000 for its in-memory com-
puting appliance HANA (High-performance ANalytic Appliance) (SAP, 2012).
The potential of IMC for setting new performance standards in the area of data management and ana-
lytics is recognized by both academics (e.g., vom Brocke et al., 2014; Plattner & Zeier, 2012) and ana-
lysts (e.g., Deloitte, 2013; Gartner, 2015). Gartner (2011), for example, identified IMC as one of the
top ten strategic technologies in the field of enterprise systems, and estimated that it is likely to be
adopted by at least 35% of midsize and large organizations through 2015 (Gartner, 2013). Plattner and
Zeier (2011) even state that IMC “marks an inflection point for enterprise applications” (p. xvi) and
that it “will lead to fundamentally improved business processes, better decision-making, and new per-
formance standards for enterprise applications” (p. xxxii). And a look at SAP’s earnings report shows
that sales in SAP HANA exceeded $200 million in 2011, which confirms that companies are willing to
invest into IMC (Woods, 2012).
Scientific research on application scenarios for IMC and its business value is scarce, especially in
terms of quantitative studies. In fact, anecdotal evidence and case studies indicate that companies are
struggling to find valuable use cases for in-memory computing and are asking whether in-memory
technologies are “only enabling very specific scenarios, or will […] have a positive impact on the en-
tire IT architecture” (Bärenfänger et al., 2014, p. 1397). Bärenfänger et al. (2014) also point out the
importance of analyzing the monetary value contribution of IMC, and Manyika et al. (2011) note that
the expertise required to realize its business value seems to not be in place yet. The same applies to the
advanced analytics that IMC enables: While LaValle et al. (2013) show that financially successful
companies are characterized by high use analytics, they also observe that the main obstacle for ad-
vanced analytics adoption is a “lack of understanding how to leverage analytics for business value” (p.
24).
Given the scarcity of empirical research on IMC’s business value, we aim at explaining the relation-
ship between its adoption and firm performance by following an econometric approach. In this re-
search-in-progress paper we present our research design and hypotheses development. Our work
builds upon a unique multi-year panel data set of IMC adoption information and financial firm per-
formance for a sample of Fortune 500 companies.
The remainder of this research-in-progress paper is structured as follows. First, we present the research
background, discussing theoretical and empirical arguments related to the business value of IMC, and
challenges of measuring the performance impacts of enterprise systems. Subsequently, we derive our
hypotheses on the relationship between IMC adoption and firm performance. We then provide details
on our research design, introducing both our unique data set and the econometric specifications for
data analysis. We conclude by outlining our next steps and expected contributions.

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2 Research Background
2.1 The Business Value of In-Memory Computing
Academic literature on the business value of analytics, and the impact of IMC in particular, is scarce.
Nonetheless, our research can be informed by at least three streams of literature.
The first stream of literature is theoretical. As summarized by Brynjolfsson et al. (2011), it builds on
arguments stemming from information theory (Blackwell, 1953) and the information-processing view
of the firm (Galbraith, 1974). One of their key arguments suggests that the availability of information
that is more fine-grained, less noisy, better distributed and available in greater volumes increases its
usage in decision making and, in turn, triggers higher firm performance. So, at least in theory analyt-
ics-enabling technologies like IMC, which aim at improving the information processing capacity of an
organization, should contribute to increased firm performance.
The second stream of literature consists of case studies that explicitly look at the use and impact of
IMC (e.g., Piller and Hagedorn, 2011, 2012; Wessel et al., 2013; vom Brocke et al., 2014; Bärenfänger
et al., 2014). For example, based on an in-depth study of potential IMC application scenarios at a
global tool manufacturer vom Brocke et al. (2014) propose a framework of business value generation
through in-memory technology. Their framework combines technological characteristics of IMC (e.g.,
in-memory data storage and processing), first-order effects (e.g., latency time reduction), second-order
effects (e.g., advanced business analytics), and the resulting business value effects (productivity in-
crease). Similarly, Bärenfänger et al. (2014) conducted a multiple-case study and derived a process
model of business value creation through IMC, comprising the following elements: (1) implementa-
tion drivers (e.g., insufficient performance of current IT solutions), (2) technological enablers (e.g.,
fast processing of large data volumes), (3) realization conditions (e.g., adaptation of operational busi-
ness processes), (4) business benefits (e.g., deeper and faster insights into data), and (5) contribution to
strategic goals (e.g., data-driven decision making). The authors conclude that the „technical ad-
vantages of in-memory computing can lead to business value in various applications across industries,
albeit not always in a measurable monetary way“ (Bärenfänger et al., 2014, p.1403).
The third stream of literature consists of quantitative studies focusing on the connection between the
adoption of analytics in general and firm performance. Over the last years several large-scale surveys
have been conducted in this field, often in collaboration between industry and academia. For example,
a study by IBM (LaValle et al., 2011, 2013) found that top-performing organizations use analytics five
times more often than lower performers do. Another survey conducted by Brynjolfsson et al. (2011) in
conjunction with McKinsey and Company investigated the relationship between decision making
based on data and business analytics (called “data driven decision making” or DDD) and firm perfor-
mance. They showed “that firms that adopt DDD have output and productivity that is 5-6% higher
than what would be expected given their other investments and information technology usage” (p. 1).
In total, we can summarize that there are theoretical and empirical arguments for the positive effect of
general analytical information systems on firm performance. Yet, the thesis that in-memory computing
leads to the positive effect on firm performance requires further investigation. Examining in-memory
computing in relation to various industries, business processes, and use cases as well as various per-
formance indicators is required in order to explain and assess IMC business value, and determine the
most promising areas of IMC adoption in organizations.

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2.2 Measuring the Performance Impact of Enterprise Systems
The problem of quantifying the financial impact of IT on firm performance has been widely discussed
in the literature over the decades (Bakos, 1987, Brynjofsson & Hitt, 1996; Bharadwaj et al., 1999;
Brynjofsson & Hitt, 2000; McAfee, 2002; Brynjofsson & Hitt, 2003; Melville, Kraemer & Gurbaxani,
2004; Ranganathan & Brown, 2006; Hendricks, Singhal & Stratman, 2007; Aral & Weill, 2007;
Tambe & Hitt, 2012). In general, quantitative studies on IT adoption and firm performance face one
common dilemma in their quest to show the business value of IT: Do companies become successful
because of adopting IT, or do successful companies adopt IT?
Various methods (e.g., lagged variables, instrumental variables) have been proposed to address this
issue of causality. A promising approach proposed by Aral, Brynjolfsson & Wu (2006) is based on the
availability of detailed information about the IT adoption process. While most studies measure IT
adoption as a single event, Aral et al. (2006) suggest distinguishing between license purchase events
and go-live events (in the context of enterprise systems there are typically one or two years passing
between these two events). This allows for measuring the performance effects of license purchases and
go-lives separately. In addition, Aral et al. (2006) examine the impact of specific IT applications sepa-
rately. In particular, they distinguished between the impact of Enterprise Resource Planning (ERP),
Customer Relationships Management (CRM), and Supply Chain Management (SCM) systems. Based
on this differentiation between license purchase and go-live as well as between different types of en-
terprise systems the authors suggest a framework of five possible causal relationships between IT and
firm performance:
1. No relationship: If there is neither a positive relationship between enterprise system license
purchases and firm performance, nor between go-live events and firm performance, it can be
assumed that the adoption of enterprise systems has no measurable financial impact on firm
performance.
2. Performance-led IT investment: If there is a positive relationship between enterprise system
license purchases and firm performance, and no relationship between system go-lives and firm
performance, it can be assumed that financially successful companies invest in enterprise sys-
tems.
3. IT-driven performance: If there is a positive relationship between enterprise system go-live
events and firm performance, and no relationship between license purchases and firm perfor-
mance, it can be assumed that the use of enterprise systems has a positive impact on firm per-
formance.
4. Classic simultaneity: If there is a both a positive relationship between enterprise system li-
cense purchases and firm performance, and between go-live events and firm performance, no
further insights into the direction of the causality between enterprise system adoption and firm
performance can be gained.
5. Virtuous cycle: If there is (a) no relationship between the purchase of ERP systems and firm
performance, and (b) a positive relationship between the go-live of ERP systems and firm per-
formance, and (c) a positive relationship between the purchase of SCM/CRM systems and
firm performance, and (d) a positive relationship between the go-live of SCM/CRM systems
and firm performance (beyond the performance gains from ERP), it can be assumed that the
initial investments in ERP drive performance gains, encouraging further investments in com-
plementary enterprise systems (CRM, SCM), which in turn further improve firm performance.

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Twenty-Fourth European Conference on Information Systems (ECIS), İstanbul, Turkey, 2016 5
The above described five possible causal interpretations of the relationship between the adoption of
enterprise systems and firm performance will build the foundation for our empirical investigation of
the impact of IMC on firm performance, as will be outlined in the following section.
3 Hypothesis Development
Discussing the general value of information in management, Brynjolfsson et al. (2011), building on
the seminal theoretical works of Blackwell (1953) and Galbraith (1974), summarize that “technologies
that enable greater collection of information, or facilitate more efficient distribution of information
within an organization should lower costs and improve performance” (p. 7), and also find first empiri-
cal evidence for a positive effect of analytical information systems on firm performance. IMC and the
analytics it enables can without doubt be regarded as such information processing technologies. As
Bärenfänger et al. (2014) argue, IMC supports „data-depending processes in ways impossible in the
past“ (p. 1397) through (1) faster data processing, (2) more flexible data access, (3) more up-to-date
data, (4) less aggregates and deeper drill-downs, (5) simpler data models, and the (6) convergence of
OLAP and OLTP. Hence, we hypothesize:
H1: Companies adopting in-memory computing experience greater performance than those that do
not.
The notion of “information intensity”, that is, the degree to which a company’s products, services and
operations depend on the information collected and processed (Lee, 2008), has been discussed by sev-
eral researchers, such as, Zhu (1999) and Bhatt & Stump (2001). Thong (1999) argued that businesses
in more information-intensive industries have higher information-processing needs and are more likely
to adopt information systems than those in less information-intensive industries. Lee (2008) has ana-
lyzed numerous IT investment studies and found that studies relying on samples from high infor-
mation-intensive industries (e.g., financial services, insurance, retail, healthcare) found positive effects
of IT investments, or at least mixed results, as opposed to studies in low information-intensive indus-
tries (e.g., construction, specific manufacturing industries). Finally, in the context of IMC, Bärenfäng-
er et al. (2014) derived a list of critical success factors of IMC implementation. Among other factors
they mentioned IMC adoption in a business process where “time is a direct cost driver” and “the value
of data processing velocity is high”. Hence, we hypothesize:
H2: Firms from high information-intensive industries that adopt in-memory computing experience
greater performance than adopters from low information-intensive industries.
Various surveys on business analytics and its underlying technologies (e.g., big data) emphasize the
value of analytics for customer-facing business processes. For example, a study by Schroeck et al.
(2012) found that customer-centric outcomes are the number one functional objective for big data ana-
lytics, with almost half of respondents ranking it as their top priority. Likewise, a recent survey by
BARC (Bange et al., 2015) showed that marketing and sales are the areas in which companies use big
data analytics the most, and also plan the most investments. Focusing on IMC, Acker et al. (2011) em-
phasize the value of IMC for customer relationship management, suggesting customer-related applica-
tion scenarios for various industries (e.g. in telecommunications: value-add services, subscriber data-
base consolidation, fraud management). Their study argues that the real-time business process support
provided by IMC for operations, customer relationship management, and business intelligence intro-
duces a new level of customer experience. And Booz & Company (2011) predicts that the shift to-
wards in-memory technology will be driven by business-side demand for real-time customer and oper-
ational information. Hence, we hypothesize:

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H3: Companies that adopt in-memory computing to support customer-facing business processes ex-
perience greater performance than those that adopt it to support non customer-facing business pro-
cesses.
With regards to the business process benefits of IMC Bärenfänger et al. (2014) distinguish between
transactional use cases and analytical use cases. While transactional IMC use cases primarily aim at
time savings (e.g., reduced lead times of business processes), the goal of analytical IMC use cases is to
gain deeper business insights (e.g., extracting new and useful knowledge about customers from histor-
ical transaction data). This differentiation is in line with the organizational ambidexterity theory that
distinguishes between exploiting existing capabilities and exploring new opportunities (March, 1991).
In the IMC context exploiting would mean running the same business processes, but faster; while ex-
ploring would mean enabling completely new business processes, or even business models. It has been
argued that “refining exploitation more rapidly than exploration” (March, 1991, p. 71) is likely to be
effective in the short run, but self-destructive in the long run. Hence, we hypothesize:
H4: Companies that adopt in-memory computing to implement analytical use cases experience greater
performance than those that adopt it to implement transactional use cases.
Provided that IMC adoption is associated with greater performance, we suggest that its effect on firm
performance is best explained by the “virtuous cycle” effect, as described in Section 2.2. Specifically,
we assume that a positive relationship between go-live of an initial IMC solution and firm perfor-
mance encourages companies to invest more into IMC, indicated by a positive relationship between
firm performance and purchase of further IMC solutions, which, in turn, will trigger further perfor-
mance gains, indicated by a positive relationship between go-live of follow-up solutions and firm per-
formance. This theoretical argument is supported by the findings from Bärenfänger et al. (2014), who
found in their multiple-case study that gaining experience with big data is an important strategic objec-
tive of adopting IMC solutions. In addition, vom Brocke et al. (2014) emphasize that value creation
through IMC is restricted by the capabilities of the overall socio-technical structures and processes of
an organization and its IT landscape, and that a single project is unlikely to deliver sustainable busi-
ness value. And Seddon et al. (2012) found empirical evidence that the pursuit of multiple ongoing
business analytics projects is an important antecedent of deriving greater benefits from business ana-
lytics. Hence, we hypothesize:
H5: For companies adopting multiple in-memory solutions, the firm performance effects of in-
memory computing are best described by the “virtuous cycle” interpretation.
4 Research Method
4.1 Data
Following the approach of Aral et al. (2006), we collected detailed information on IMC purchase and
go-live events of more than 200 companies in the time frame from 2009 to 2015. In particular, our
data set comprises information about Fortune 500 companies that during this time period have con-
cluded license agreements for IMC solutions with one of the largest vendors. The data set, which has
been retrieved from the vendor’s project database, includes the name of the company, the year of the
purchase, the year of the go-live, and the so-called use case. The use case describes the functional
(e.g., customer relationship management, supply chain management, financial reporting) and technical
(e.g., database, data warehouse, cloud solution, business application) scope of the purchased IMC so-

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lution. In total there were more than 100 use cases, which we grouped into four functional categories
(inbound, operations, outbound, and support), utilizing the framework suggested by Dehning et al.
(2007), two broad technical categories (database-tier, e.g. database management system, data ware-
house, versus application-tier, e.g. sales pipeline analysis, smart meter analytics), and two general in-
formation processing goals (transactional processing versus analytical processing). The distinctions
between these categories will be used to investigate hypothesis H3 (“customer-facing business pro-
cesses”), H4 (“exploitation vs. exploration”), and hypothesis H5 (“virtuous cycle”). Regarding the vir-
tuous cycle hypothesis, we assume that database-tier IMC solutions play the role of ERP systems, ini-
tially triggering the virtuous cycle, and that application-tier IMC solutions are follow-up investments,
which will generate additional firm performance gains (comparable to CRM and SCM systems in the
original formulation of the virtuous cycle hypothesis).
For each firm from our list of IMC adopters we queried the CRSP/Compustat Merged Database to re-
trieve their industry classification and financial performance data for the years from 2009 to 2015. As
some companies from our list of IMC adopters are not publicly traded on U.S. stock exchanges, we
had to remove a small number of cases from our panel data. Subsequently, we constructed the same
financial performance measures that have been used in prior research on the performance effects of IT
adoption (Table 1).
Ratio
Definition
Interpretation
Labor productivity
Sales/number of em-
ployees
High ration indicates more productivity per employee
Return on assets
Pretax income/assets
High ratio indicates efficient operation of firm without
regard to its financial structure
Inventory turnover
Cost of goods
sold/inventory
High ratio indicates more efficient inventory manage-
ment
Return on equity
Pretax income/equity
High ratio indicates higher returns accruing to the
common shareholders
Profit margin
Pretax income/sales
High ratio indicates high profit generated by sales
Asset utilization
Sales/assets
High ratio indicates high level of sales generated by total
assets
Collection efficiency
Sales/accounts receivable
High ratio indicates effective management of customer
payment
Leverage
Debt/equity
The higher the ratio, the more leveraged the firm
Table 1. Definitions and Interpretations of Financial Performance Measures (Aral et al., 2006;
Hitt et al., 2002).
4.2 Econometric Methods
We plan to analyze our panel data set applying two commonly used econometric approaches.
First, we replicate the approach of Hitt et al. (2002) and Aral et al. (2006) in order to ensure the com-
parability of our results with their findings. We will use the following general specification of a pooled
regression model to examine the relationship between IMC and various measures of financial perfor-
mance:
(1) log(Performance Numeratorfy) = βfy0 + βfy1 log(Performance Denominatorfy) + βfy2 Purchasefy
+ βfy3 Go-Livefy + βfy4 Controlsfy + εfy,

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where f and y indicate the firm and year of an observation. Performance Numerator and Performance
Denominator represent the elements of the financial performance ratios, as defined in Table 1. Pur-
chase represents dummy variables indicating whether a company has purchased IMC licenses, Go-
Live represents dummy variables indicating whether a company has performed a go-live of an IMC
solution, and Controls represents a set of control variables. For some analyses multiple Purchase and
Go-Live variables will be used in order to separately model the purchases and go-lives of IMC solu-
tions of various functional and technical scopes, and to distinguish between the purchase and go-live
of an initial IMC solution and follow-up solutions. In order to isolate economy-wide shocks and other
potential reasons for performance variations between companies we apply a set of control variables,
namely: year, industry (2-digit SIC level), industry capital intensity, company size (based on annual
firm revenue), advertising expenditure per employee (Mithas et al., 2012).
Second, in addition to the above pooled model we plan to estimate panel data models that account for
the panel structure of our data set, that is, the fact that we have both cross-sectional (multiple firms)
and longitudinal (multiple years for each firm) data. In particular, it is planned to use random-effects,
fixed-effects, and mixed models to estimate the effects of IMC on firm performance (Wooldridge,
2012).
5 Outlook
This research-in-progress aims at contributing to our understanding of the business value of IMC by
quantifying its effect on firm performance by applying standard econometric methods. Furthermore,
we aim at disentangling the interdependences between IMC adoption and business value by building
on a causal framework explaining the effect of IT on firm performance, which was first outlined by
Aral et al. (2006). By analyzing a unique data set we can contribute to research on the business value
of IMC, uncovering the nature of IMC adoption effects for various industries, business processes, and
use cases. Investigating this will shed light on the mediating factors of the relationship between IMC
adoption and firm performance gains (for a more detailed discussion see, e.g., Sharma et al. (2014)).
From a practitioner perspective these results will allow to analyze potential IMC investments and de-
termine whether they will be effective in a certain business case.
As with all econometric studies, our research design is not without limitations. For example, while
differentiating between purchase and go-live events allows us to make detailed claims about causality,
we also have to acknowledge that in our data set there are far more purchase events than go-live
events. Most IMC solutions were purchased and implemented after 2012, and a great portion of these
projects is still ongoing. This can undermine the statistical power of our analysis. We should also keep
in mind that our findings can be undermined by the fact that some companies might have implemented
IMC solutions from other vendors than the one we are focusing on in this research. Likewise, some
companies might have implemented technological alternatives to IMC, such as Apache Hadoop.
In future research, we plan to include IMC solutions from other vendors into our analysis and to com-
pare the business value of IMC solutions with the value of alternative big data analytics technologies.
We also intend to complement our econometric approach with case studies about the companies show-
ing outstanding results in our sample in order to gain deeper insights into the process of deriving busi-
ness value from IMC solutions and the key mediating factors. Finally, future research could explore
first- and second-order effects of IMC adoption in-depth, focusing on both financial and non-financial
indicators, to better support companies in their technology adoption decisions. Sharma et al. (2014)
suggest, for example, that factors such as decision-making processes, organizational capabilities, and
governance structures play moderating roles on the path from adopting business analytics solutions to
superior organizational performance.

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