A Literature-based Derivation of a Meta-framework for

IT Business Value

Sarah Seufert

, Tobias Wulfert

, Jan Wernsd

orfer

and Reinhard Sch

utte

Institute for Computer Science and Business Information Systems, University of Duisburg-Essen,

Universit

atsstraße 2, Essen, Germany

Keywords:

IT Business Value, IT Value Framework, Impact of IT, IT Investments, Literature Review, Meta-framework.

Abstract:

The business value of IT in companies is a highly discussed topic in information systems research. While

the IT business value is an agreed upon term, its decomposition and assessment on a more detailed level is

ambiguous in literature and practice. However, assessing the IT business value is pivotal for goal-oriented IT

management. Therefore, we suggest a hierarchical decomposition of the IT business value along aggregated

impacts and atomic impacts. We introduce a taxonomy to gain a better understanding of what types of atomic

impacts may be caused by IT investments. With the help of the taxonomy, we classify a total of 957 values

from existing value catalogs and derive 29 archetypal IT impacts grouped by a company’s business units.

Bundling this grouping with exemplary impacts for the IT value assessment, we ﬁnally propose an IT value

meta-framework for the structured business value assessment.

1 INTRODUCTION

The business value of IT in companies is a highly

disputable topic in information systems (IS) research

(Melville et al., 2004; Brynjolfsson and Hitt, 2003;

Mirani and Lederer, 1998; M

uller et al., 2018; Pathak

et al., 2019). Without an understanding of the busi-

ness value of IT investments within a company, no

goal-oriented IT management is possible (Sch

utte

et al., 2019). The “naive” expectations about only

positive economic effects of IT have been discussed

for a long period in literature as IT productivity para-

dox, focusing on the value contribution of IT and

its contribution to the success of companies in the

1980s and 1990s (Brynjolfsson, 1993; Brynjolfsson

and Hitt, 1998). More recently, the opinion has be-

come accepted that IT in general has a positive inﬂu-

ence on a company’s productivity (Brynjolfsson and

Hitt, 2003). But the problem still exists that the vari-

ance in the return (positive and negative) on IT invest-

ments is still high and the IT business value, deﬁned

as extent of the contribution of IT investments to the

productivity or success of a company (Cao, 2010), is

therefore still difﬁcult to determine. Despite these un-

https://orcid.org/0000-0003-3231-2853

https://orcid.org/0000-0002-5504-0718

https://orcid.org/0000-0002-1152-5043

https://orcid.org/0000-0002-1535-9038

certainties in the IT business value, companies extend

their IT budgets for investments in digitalization en-

deavors (Fersht and Snowdon, 2020). Nevertheless,

the assessment of IT business values prior to an IT

investment is especially important in times of crises

such as the American subprime mortgage crisis, the

European debt crisis and currently the coronavirus

disease in which IT budgets are typically reduced.

With reduced IT budgets, decisions demand for more

elaborated business cases and cost-beneﬁt analysis to

approve IT investments (Hajli et al., 2015). Never-

theless, despite the crises, investments in IT are still

necessary, for example to be able to react more ﬂexi-

bly to changes or to adapt the current business model

to changes caused by the crises. However, the IT busi-

ness value of single IT investments is often assessed

by a decision maker’s rule of thumb (Schniederjans

et al., 2010).

To better deal with IT investment decisions, IS

researchers so far have proposed approaches to as-

sess the IT business value of an IT investment in a

company (Mooney et al., 1996; Tallon and Kraemer,

2006) and have introduced a number of value catalogs

including speciﬁc impacts and expected business val-

ues of IT (Melville et al., 2004; Samulat, 2015; Porter,

2001a; Farbey et al., 1992; Gregor et al., 2006; Kur-

niawan et al., 2016). A value catalog is a reference

list of positive and negative effects (Schulze, 2009)

that can be attributed to the launch or productive op-

Seufert, S., Wulfert, T., Wernsdörfer, J. and Schütte, R.

A Literature-based Derivation of a Meta-framework for IT Business Value.

DOI: 10.5220/0010447702910302

In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 291-302

ISBN: 978-989-758-509-8

291

eration of an IT system (Sch

utte et al., 2019). These

catalogs consist of a set of either speciﬁc or abstract

possible impacts for IT systems in general or speciﬁc

industry or system constellations with the downside

that these impacts are not always directly quantiﬁ-

able in an arbitrary company (Bartsch, 2015). Nev-

ertheless, value catalogs indicate possible IT business

values and allow decision makers – at least for one

catalog - to fully assess the business value of a fu-

ture IT investment and avoiding formally defective

decision models that cannot be solved (Adam, 1996).

The literature already contains a few value catalogs

for the identiﬁcation of the IT business value that are

intended to serve this purpose. However, the lists

vary in the number, the deﬁnitions and the granularity

of categories (Schryen, 2013). Also, many existing

catalogs are prone to problems regarding a compre-

hensive and customizable IT value assessment (exem-

plary IT value catalogs in brackets). Oftentimes, cata-

logs do not sufﬁciently indicate atomic effects and are

rather abstract (Mirani and Lederer, 1998). Related

to this issue are missing or insufﬁcient hierarchiza-

tions of impacts. While the majority of catalogs aims

to assess the IT business value as a whole (Baum

and Ickler, 2008), those approaches are oftentimes in-

complete due to abstractions from context speciﬁc as-

pects. Speciﬁc catalogs on the other hand (Schulze,

2009) appear to dismiss important aspects mentioned

in the taxonomy of IT impacts (Figure 2). In general,

IT value catalogs are also not designed to be conﬁg-

ured for a speciﬁc application context, missing nec-

essary mechanisms and methods for such customiza-

tions (Porter, 2001a).

Conducting a structured literature review (Web-

ster and Watson, 2002), we have identiﬁed 32 catalogs

including 957 impacts. These impacts both contain

several duplicates and vary in their scope and mean-

ing. Thus, we aim at providing archetypal IT im-

pacts for the assessment of arbitrary IT investments.

Based on these archetypes we will propose a meta-

framework for the IT business value assessment. Our

research enables practitioners to extend their IT busi-

ness value assessment with our archetypal impacts or

build IT value catalogs themselves. Researchers may

build on our archetypal IT impacts for further quanti-

tative analysis of the IT business value.

The remainder of this research is structured as

follows: ﬁrstly, we will set the foundations for the

meta-framework development involving IT business

value and value catalogs in general and characteris-

tics of atomic IT values in particular. Secondly, we

will sketch our scientiﬁc approach followed by a pre-

sentation of our IT value archetypes within the meta

IT value framework. Finally, we discuss our ﬁndings

and brieﬂy summarize our main results and provide

an outlook on future research.

2 FOUNDATIONS

2.1 IT Business Value Decomposition

The IT business value can be deﬁned as the impact

of IT on organizational performance (Melville et al.,

2004; Mooney et al., 1996; Devaraj and Kohli, 2003),

which is widely established in the literature (Pathak

et al., 2019). Melville et al. complement this gen-

eral deﬁnition with an indication of the level and also

the type of impacts: “at both the intermediate pro-

cess level and the organization-wide level, and com-

prising both efﬁciency impacts and competitive im-

pacts” (Melville et al., 2004). While efﬁciency refers

to internal impacts such as productivity enhancement

(Tallon and Kraemer, 2003), product quality (Barua

et al., 1995), proﬁtability improvements (Melville

et al., 2004) or cost reduction (Tallon and Kraemer,

2003), competitive refers to external impacts such as

competitive advantage (Parsons, 1983), product dif-

ferentiation (Belleﬂamme, 2001) or market expansion

(Tallon and Kraemer, 2003). Although there is a gen-

eral agreement about what an IT business value can

be and the topic has been discussed for many years,

“the relation between IT investments and ﬁrm per-

formance remain elusive” (Masli et al., 2011). It is

still not clear what are the returns and the concrete

values generated by IT investments in particular, and

the decomposition of the IT business value in general

(Pathak et al., 2019; Wang et al., 2012).

Some authors have already addressed this gap in

research and decomposed the IT business value to

possibly observable impacts. We propose value cat-

alogs as an important starting point for the identiﬁ-

cation of the IT business value in a speciﬁc organi-

zation (Sch

utte et al., 2019). An optimal value cata-

log hierarchically decomposes the IT business value

(level 1) into aggregated values (level 2), observable,

atomic impacts (level 3) and guides the assessment

providing key questions (level 4) for the impact iden-

tiﬁcation (Figure 1). The hierarchical decomposition

avoids formally defective decision problems (Adam,

1996).

IT Business Value

Aggregated Value n Aggregated Value 1

Atomic Impact n Atomic Impact 1

Question 1 Question 2

Atomic Impact 2

…

Question n

…

Decomposition of IT

Business Value

Level 1

Level 2

Level 3

Level 4

Figure 1: Decomposition of IT Business Value.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

292

2.2 IT Value Dimensions and

Characteristics

The variety of atomic IT impacts can be classiﬁed ac-

cording to the following dimensions and characteris-

tics (Nickerson et al., 2013) presented in the taxon-

omy in ﬁgure 2. The proposed taxonomy consists

of six dimensions each consisting of several mutu-

ally exclusive and collectively exhaustive characteris-

tics (Nickerson et al., 2013; Bailey, 1994). The Busi-

ness Unit (1) characteristics are based on the value

chain introduced by Porter which dis-aggregates a

ﬁrm in strategically relevant primary and supporting

activities (Porter, 2001a). Logistics (Log) activities

are originally divided in inbound and outbound lo-

gistics (Porter, 2001a). Under logistics we comprise

all activities associated with receiving, storing, and

disseminating inputs to the product and with collect-

ing, storing, and physically distributing the product

to buyers. Under Operations (Ops) we subsume all

activities associated with transforming inputs into the

ﬁnal product. Due to the original focus of (Porter,

2001b) on industrial enterprises with mainly physi-

cal products, we retrospectively also assign the cre-

ation and provision of services to this activity in or-

der represent today’s business environment (Parasur-

aman et al., 2005). Marketing and Sales (M&S) refers

to activities that make the product or service appeal-

ing to customers and also to activities that are nec-

essary for the buyer to purchase the product. Ser-

vices (Ser) includes activities to maintain or increase

the value of the product or service. Thus, this activ-

ity contains the delivery of services created by op-

erations and services accompanying physical goods.

Procurement (Proc) includes all activities that are nec-

essary to purchase resources and necessary material

used in the operations and other business unites of

the company. Technological Development (TD) is

understood to comprise of a variety of activities that

deal with the improvement of a product or service

and in particular with the process associated with it.

Human Resources Management (HR) consists of the

activities related to the recruitment, training, devel-

opment and remuneration of all types of personnel

(Porter, 2001b). For coding impacts that have an in-

ﬂuence on several business units we introduce Cross-

Organizational-Activities (COA) as further character-

istic. It includes activities that involve general man-

agement, planning, ﬁnance, accounting, legal, gover-

nance affairs, and quality management which usually

supports the entire chain and not individual activities.

The Tangibility (2) of an IT impact is concerned

with the extent to which it can be measured and eval-

uated in economic terms (Lucas, 2000). The literature

generally distinguishes between tangible and intangi-

ble impacts. Tangible impacts represent impacts that

can be measured and quantiﬁed economically (Mi-

rani and Lederer, 1998). Intangible impacts on the

other hand are very hard to quantify, oftentimes not al-

lowing for such economic evaluations (Lucas, 2000).

Thus, a qualitative assessment of the impact is neces-

sary (Kesten et al., 2007).

A widespread distinction in the Level of Exami-

nation (3) is the individual level, the ﬁrm level and

the industry level (Bakos, 1987; Chau et al., 2007;

Kauffman and Weill, 1989). We are following this

view, especially since this distinction plays an impor-

tant role in explaining the productivity paradox. The

individual level effects of the IS affecting employ-

ees on an individual level, such as improving skills

or increasing the employee’s satisfaction (Chau et al.,

2007). Firm level is concerned with IT impacts which

have an inﬂuence on the whole organization includ-

ing cross-organizational processes. For example, pro-

cess improvements or increased organizational per-

formance (Soh and Markus, 1995). This level of ex-

amination also refers to the value chain of the orga-

nization, thus customer and supplier related activities

or processes. The industry level contains IT impacts

going beyond the organizational boundaries and its

value chain, e.g. on the entire industry or the national

economy (Chau et al., 2007).

Figure 2: Taxonomy of IT Impacts.

The deﬁnition of IT business value already empha-

sizes that the Performance Focus (4) is a central as-

pect, which should also be considered when identify-

ing impacts. Operation process performance is usu-

ally created by tangible impacts of the IT. They repre-

sent automatizations of activities or processes which

constitute the regular day-to-day business, thus affect-

ing the performance of the organization. However,

they also oftentimes form the basis for intangible im-

pacts that build on them (Mooney et al., 1996). Man-

agement process performance increases the availabil-

ity and quality of information, allowing for better co-

ordination, control and a decision making by the man-

agement.

IT systems produce a (potentially continuous)

stream of net beneﬁts. Thus, this dimension focuses

A Literature-based Derivation of a Meta-framework for IT Business Value

293

on the Time of Occurrence (5) of the impacts. Con-

ducting an a priori assessment of the impact of the IT

at the time of the investment decision, the impacts un-

til and exploitable immediately at the go-live (imme-

diate impacts) of the IT can be determined and proba-

ble future impacts (anticipated impacts), by accessing

the quality of the IS as a proxy measure, can be antic-

ipated (Figure 3) (Gable et al., 2008).

Time

Time of occurence

Immediate Impacts

Investment Decision

Go-live

Anticipated Impacts

…

Figure 3: Time of Occurence.

Investments in IT systems do not only provide posi-

tive impacts to the organization. To assess the overall

beneﬁts of an IT, it is necessary to analyze the Direc-

tion (6) of these impacts (Schumann, 1992). As the

majority of the literature focuses on positive beneﬁts

of IT, our presupposition is to regard IT impacts as

positive, if a negative character of an impact is not ex-

plicitly stated. Positive impacts have contributed to

an overall corporate objective, to justify the IS invest-

ment (Schulze, 2010). The implementation and the

operation of an IT system may also cause direct (one-

time) and indirect (ongoing) negative impacts such as

costs for the organization (Schulze, 2010).

3 RESEARCH APPROACH

To analyze possible IT impacts and derive a proﬁle

of aggregated clusters of impacts, we conduct a struc-

tured literature review and cluster identiﬁed IT im-

pacts accordingly. Our analysis begins (1) with the

identiﬁcation of IT impacts already discussed in the

literature (Figure 4) to build upon existing knowledge

regarding IT-impacts (Webster and Watson, 2002).

The review starts by identifying and selecting qual-

iﬁed sources upon which the relevant data can then

be extracted. This data, primarily the individual IT

impacts, builds the foundation for the subsequent cre-

ation of the IT value framework. By limiting the key-

word search (Urbach et al., 2009) to leading jour-

nals (AIS Senior Scholars’ Basket), the quality of

the results should be ensured (Webster and Watson,

2002). Based on an initial screening of the literature

we formulated the following generic search string that

is customized for EBSCOHost: (Information Tech-

nology OR Information System* OR IT OR IS) AND

(Value Catalog* OR Impact* Catalog* OR Value Cat-

alog*)

To broaden the possible literature pool, German

publications where also recognized by translating the

search string. After an initial screening based on the

criteria according to Fink (2019, p. 53) (e.g., lan-

guage: German/English; research subject: IT sys-

tems; etc.), a total of 57 contributions could be iden-

tiﬁed. Due to the lack of a common generic term,

the keyword search yielded rather imprecise results,

putting special importance on the subsequent forward

and backward search (Webster and Watson, 2002).

The identiﬁed contributions were subjected to a thor-

ough selection process deﬁned by the following selec-

tion criteria: (a) the content must meet the previously

established deﬁnition of IT impacts, (b) the catalog

must be based on some form of categorization, and

tal of 32 sources were deemed suitable for extracting

and analyzing their contents. Key sources for devel-

oping our meta-framework are illustrated with exem-

plary impacts in ﬁgure 6.

(1) Literature Review

String

Generation

Keyword

Estab-

lishment

Database

Literature

Screening

Back-/

Forward

(2) Impact Preprocessing

Catalogue

Details

Documentation

(Atomic)

Impacts

Extraction

Dimension

Derivation

Charac-

teristics

Derivation

(3) Impact Clustering and Hierarchisation

Dimension

Update

Test

Coding

Manual

Coding

Intercoder

Calculation

Clustering &

Hierar-

chisation

Figure 4: Research Approach.

We then (2) extracted all impacts from the identiﬁed

sources with focus on the atomic impacts. Following

a systematic selection process (Schwarz et al., 2007),

all sources were read to document relevant data in

a structured form. Speciﬁcally, atomic impacts and

their respective categories were collected in an Excel

spreadsheet, while additional aspects of each IT value

catalog were documented separately. This selection

process resulted in a collection of 957 IT impacts, of

which 682 can be designated as atomic (level 3). Ag-

gregating the impacts in a concept matrix (Webster

and Watson, 2002) led to the overarching dimensions

and characteristics describing the variety of impacts

identiﬁed (Section 2.2).

Using this data-set of 682 atomic IT impacts, we

ﬁrstly conducted a qualitative content analysis involv-

ing each (hierarchy of) impacts and their respective

research paper and codiﬁed (3) each IT impact re-

garding the presented dimensions and characteristics.

After an initial test coding with a set of 50 impacts,

we updated the dimensions and their respective char-

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

294

acteristics. For example, it appeared that the dimen-

sion Form of Investment investigating the type of IT

and related staff to be invested in could not be coded

reliably and therefore had to be dismissed. Also,

the characteristic All had to be removed from the di-

mension Performance Focus due to its distorting ef-

fect. Having the dimensions and characteristics estab-

lished, ﬁve independent coders knowledgeable about

IT business value in general and IT value catalogs in

particular manually assessed the selected IT impacts.

As suggested in the literature (Nili et al., 2017), those

coders where trained in the coding procedure and pro-

vided with a coding guide stating the deﬁnitions of

each code. Due to the large number of IT values, the

coding was done in multiple stages in order to pre-

vent coder fatigue (Jourdan et al., 2008; Neuendorf,

2002). We used Microsoft Excel and VBA to sup-

port the coding process. If information for an IT im-

pact is missing, we considered supplementary mate-

rial such as referenced papers. If an IT impact is as-

signed with diverging characteristics during the cod-

ing process, the characteristic used by the majority of

the coders is assigned. Cases in which an unambigu-

ous decision cannot be made as the coders all assigned

different characteristics are discussed among the ﬁve

coders via online communication media, referring to

the respective reliability measures (Weber, 1990). To

assess the coding quality, we calculated Fleiss’ Kappa

as suggested indicator (Landis and Koch, 1977) for

the intercoder reliability (Fleiss, 1971) for each di-

mension. This method allows for determining the

agreement between more than two coders while ac-

counting for agreements by chance (Nili et al., 2017).

The calculated kappa values are benchmarked against

a set of ﬁxed agreement measures in order to access

the reliability of our coding (Landis and Koch, 1977).

The intercoder reliability for the dimension Direction

is almost perfect (0.98) and substantial for Business

Unit (0.61). The dimensions Tangibility (0.47) and

Level of Examination (0.54) both constitute a mod-

erate level of agreement between the coders. For

Performance Focus (0.35) and Time of occurrence

(0.40) a fair strength of agreement could be measured.

The coding processes leads to a codiﬁed table of 682

IT impacts. After qualitatively assessing the IT im-

pacts, the clusters are established by statistical meth-

ods (Denscombe, 2008). We cluster the hand-coded

IT impacts to derive archetypal IT impacts general-

izable for arbitrary IT investments. The clustering is

used to abstract from the diverging connotations of

the IT impacts used by the authors of the value cata-

logs under consideration. The meta-framework pro-

vides an overview on the existing IT impacts as previ-

ously described in literature. We applied hierarchical

clustering on the set of 682 IT impacts with their re-

spective coding. For deriving the number of clusters,

we visually examined the dendrogram resulting in a

number of 29 clusters (Ketchen and Shook, 1996).

Aiming at maximizing the homogeneity within each

cluster, we apply the Ward method with squared Eu-

clidean distances (T

auscher and Laudien, 2018). The

clusters as archetypal IT impacts form level 3 in our

decomposition of the IT business value (Figure 1). In

order to aggregate the impacts we used the business

unit dimension as it includes the highest number of

characteristics, thus allowing for the highest degree

of differentiation. This dimension also seemed to be

a key differentiator among the derived clusters. Ad-

ditionally, the business unit dimension is perceived to

be the most relevant for the internal organization of

a company, regarding a practical application of the

framework. This aggregation follows Porter et al.,

taking the perspective of key organizational activities

(Porter, 2001b), also building upon previous classiﬁ-

cations of IT impacts (Anselstetter, 1984). The result-

ing meta IT value framework is presented in the fol-

lowing chapter indicating the number of IT impacts

summarized in each cluster following the cluster ID

in brackets (cluster ID - number of impacts).

4 IT VALUE FRAMEWORK

By applying the hierarchical cluster analysis to the IT

impacts, we identify 29 distinct archetypal IT impacts

for the third level of the IT business value decomposi-

tion. These impacts can be aggregated (level 2) to the

business units (section 2.2). For exemplary archety-

pal IT impacts, we propose exemplary IT impacts and

leading references allowing practitioners to better as-

sess the IT business value of their respective IT in-

vestment. Further guiding questions can be derived

based on the following descriptions of the archety-

pal IT impacts and mentioned literature. This also al-

lows for a customization of the framework to the spe-

ciﬁc IT investment decision regarding organization,

IT system, and other environmental factors (Brynjolf-

sson and Hitt, 2003). An extract of the developed IT

value framework is depicted in ﬁgure 5 illustrating the

structure and design of the framework (level 1-3). The

extract is detailed in ﬁgure 6 presenting the clustered

archetypal IT impacts as level 3 impacts and exem-

plary impacts for level 4. We used the characteristics

of the business unit dimension (level 2) for aggregat-

ing the clusters of IT impacts (level 3) and developing

a hierarchical IT value framework.

The Log aggregated value consists of two impacts.

Better inventory management (Log 1 - 32) which

A Literature-based Derivation of a Meta-framework for IT Business Value

295

leads to cost reductions in this domain. This can be

achieved by impacts reducing the inventory (O’Leary,

2004), e.g. delivering products electronically (Schu-

mann, 1992), increasing the turnover (Vanlommel and

De Brabander, 1975), or reducing the storage require-

ments (Andresen et al., 2002). IT systems can also

improve the incoming goods inspection (Log 2 - 9)

e.g. by impacts decreasing reclamation and spoilage

risks (Anselstetter, 1984).

By clustering the Ops aggregated values two dis-

tinct, archetypal impacts can be identiﬁed. The

ﬁrst cluster involves improvements to production pro-

cesses (Ops 1 - 27). Those production related efﬁ-

ciency and effectivity beneﬁts can materialize in var-

ious immediate impacts. Some examples are an IT-

based increased throughput (Mooney et al., 1996),

optimized capacity utilization (Schulze, 2009), re-

duced operational costs, or higher production relia-

bility (Anselstetter, 1984). Another cluster is consti-

tuted by impacts which improve the product quality

(Ops 2 - 8). This can be achieved by providing lean

production (Shang and Seddon, 2002) or a higher de-

gree of standardization (Vanlommel and De Braban-

der, 1975).

Six archetypal clusters are identiﬁed for the M&S

aggregated value. Impacts of the IT system can

improve the M&S capabilities (M&S 1 - 8) of

the organization. Examples are the ability to pro-

vide instant price quotations to clients (Andresen

et al., 2002), analyzing ordering behaviors (Schu-

mann, 1992), or adding multi-currency capabilities in

IT systems (Shang and Seddon, 2002). Another as-

pect of the M&S business unit is represented by the

cluster which improved customer retention (M&S 2

- 12). Those impacts can improve the overall cus-

tomer relations (Gammelg

ard et al., 2006; Mirani and

Lederer, 1998) or by saving customer requests and

utilizing such data in order to provide personalized

offers (Schumann, 1992). Some impacts are specif-

ically directed towards increasing sales (M&S 3 - 7)

and the respective business unit. Possible approaches

are to implement ordering systems in order to develop

new sales areas (Schumann, 1992) and to increase

responsiveness to customers (O’Leary, 2004). The

sales management (M&S 4 - 6) cluster contains im-

pacts that support decision makers in this domain, e.g.

by providing faster and cheaper information about the

success of marketing measures (Schumann, 1992) or

more elaborated product range analysis (Anselstet-

ter, 1984). Besides increasing and managing sales,

IT systems can also provide time savings in market-

ing, sales, and product delivery (M&S 5 - 23). By

introducing sales automation (O’Leary, 2004), faster

billing (Gable et al., 2008), or immediate price and

availability information (Schumann, 1992) can, for

example, result in decreased capital commitment or

less delayed deliveries (Anselstetter, 1984). By De-

veloping competitive sales capabilities (M&S 6 - 10)

consist of impacts which improve the company image

and public relations (Andresen et al., 2002). Addi-

tionally, through superior marketing (Vanlommel and

De Brabander, 1975), market (Anselstetter, 1984) and

sales analyses (Schumann, 1992) , the company can

improve its competitive position (Anselstetter, 1984).

IT Business Value

Ser 1

LogisticService

Cross Organizational Activities

COA 1Log 1 Log 2 COA 2

…

Figure 5: Extract from the IT Value Framework.

All impacts assigned to Ser can be represented by the

archetype improved customer services (Ser 1 - 18)

that contains operational, cross organizational activ-

ities which improve the quality and delivery of cus-

tomer services (Gammelg

ard et al., 2006). Improve-

ments can be achieved, for example, by impacts accel-

erating responses to such enquiries and a faster, bet-

ter delivery of the requested service (Andresen et al.,

2002), or by reducing the overall need for services

(e.g. maintenance) (O’Leary, 2004). The customer

interaction can also be impacted by interactive and

customizable services (Shang and Seddon, 2002) or

24/7 service availability (Riggins, 1999).

The Proc cluster analysis resulted in two archety-

pal impacts. On an operational level, IT can con-

tribute to a more efﬁcient procurement of resources

(Proc 1 - 15). This refers to faster (Anselstetter, 1984)

and cheaper (Vanlommel and De Brabander, 1975)

procurement by e.g. improving the order manage-

ment (O’Leary, 2004) or faster responses to supplier

quotations (Andresen et al., 2002). Improved bar-

gaining against suppliers (Proc 2 - 6) involves IT im-

pacts improving the supplier selection resulting in im-

proved supplier identiﬁcations and assessments (An-

dresen et al., 2002) as well as improvements to the

order planning, control, and monitoring (Anselstetter,

1984).

The aggregation of the TD impacts can be decom-

posed in to two IT value archetypes. Improved IT

infrastructure support (TD 1 - 18) constitutes impacts

which provide immediate beneﬁts by improving upon

the IT infrastructure of the company. Those can ma-

terialize in improvements to the data security (Kesten

et al., 2007), quicker, easier, and cheaper incorpora-

tion of product features (Porter and Millar, 1985), or

reduced communication costs (Mirani and Lederer,

1998). Impacts assigned to the improved R&D (TD

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

296

2 - 16) cluster allow a company to make product,

service and business process innovations and to alter

their product life cycles (Mooney et al., 1996), thus

possibly utilizing IT as an competitive weapon (Par-

sons, 1983). Exemplary impacts consist in an faster

application development (Mirani and Lederer, 1998)

and the ability to apply previously unfeasible business

technology (Porter and Millar, 1985).

The clustering revealed two impacts for the aggre-

gated HR values. Staff reductions (HR 1 - 22) consti-

tutes e.g. impacts to increase employee productivity

(Gable et al., 2008) in order to avoid the need to in-

crease the work force (Anselstetter, 1984) or decrease

the current number of employees (Petrovic, 1994).

Another approach is to reduce the staff requirements

(Andresen et al., 2002). Impacts which improve the

employee’s skills (HR 2 - 17) focus on learning us-

ing IT. Those skills can materialize in a broadened

skill level (Shang and Seddon, 2002) and enhanced

recall of job-related information (Gable et al., 2008)

as well as social skills such as the ability to work au-

tonomously (Shang and Seddon, 2002) and improved

human relations (Anselstetter, 1984).

The aggregated COA values can be decomposed

into twelve archetypal impacts. Operational time and

cost savings (COA 1 - 109) at ﬁrm level constitutes

the largest cluster. Those impacts represent clas-

sic effectiveness and efﬁciency beneﬁts which can

be achieved through investment in IT. Examples are

various forms of cost reductions (e.g. staff, transac-

tions, etc.) (Andresen et al., 2002; Shang and Seddon,

2002) and process improvements (Riggins, 1999).

Those impacts are mostly tangible and occur imme-

diately after the implementation of the IT system.

The improvements in management process (COA 2

- 62) cluster is primarily concerned with immediate

information-related impacts of the IT investment and

how they affect the management. For example, IT

investments can increase the availability and accu-

racy (Gregor et al., 2006) of information, enabling

faster, and more efﬁcient decision making (Parker

et al., 1988). Impacts enabling the development of

new business areas (COA 3 - 6) constitute a relatively

small cluster. This can be done by new products and

applications (Bartsch, 2015) or amendments to the

workforce, policies, and procedures (Shang and Sed-

don, 2002). Impacts clustered in the improve market

position (COA 4 - 63) group are cross-organizational,

mostly anticipated and directed towards management

processes. To name a few, IT systems may support

strategic goals of the company (Baum

ol and Ickler,

2008), constitute a competitive advantage (Weill and

Broadbent, 1998), or enable changes to the business

model (Schulze, 2009). The cluster improved corpo-

rate growth (COA 5 - 11) contains impacts which en-

able the company to grow or ones that minimize re-

lated risks. IT can create such growth by increasing

the operational readiness (Anselstetter, 1984), allow-

ing for new services (Shang and Seddon, 2002), or

minimizing the risk of new business ventures (An-

dresen et al., 2002). IT systems can also increase

the company ﬂexibility (COA 6 - 53) allowing the

company to adapt to future changes. Exemplary im-

pacts for this cluster are an increase level of stan-

dardization (O’Leary, 2004) and an improved change

management (Gammelg

ard et al., 2006). The growth

management (COA 7 - 11) cluster contains impacts

IT can have on the highest company level to gen-

erate growth. Examples are building a cost leader-

ship (Shang and Seddon, 2002), leveraging the com-

panies size (O’Leary, 2004) and to increase the mar-

ket share (Andresen et al., 2002). Impacts of IT

systems can also create and defend competitive ad-

vantages (COA 8 - 16) by developing new markets

and inﬂuencing the relationship to competitors. For

example, impacts creating or removing barriers for

market entry (Schulze, 2009), allowing expanding to

e-markets (Shang and Seddon, 2002), and improv-

ing relations to external parties (Gammelg

ard et al.,

2006). By improving the integration and information

ﬂow (COA 9 - 66) across the company, IT impacts

can provide various positive, but oftentimes intangi-

ble beneﬁts. This allows to perform tasks separated

in time and space or to increase idea and information

sharing among project teams (Andresen et al., 2002).

The cluster improved employee satisfaction and per-

formance (COA 10 - 12) shows how IT impacts can

directly affect the workforce. Those impacts usually

apply to all business units and are difﬁcult to quantify

in monetary terms. For example, a better perform-

ing system can increase the moral or the interpersonal

communication (Shang and Seddon, 2002), some im-

pacts may also boost employee’s creativity (Mooney

et al., 1996). Besides those primarily positive impacts

of IT, such investments also cause considerable costs.

The IT investment costs (COA 11 – 12) represent di-

rect and indirect costs associated with the IT invest-

ment. For example, acquisition and personnel costs

as well as an increased dependence on the IT (Ansel-

stetter, 1984). In the cluster time savings in daily busi-

ness operations (COA 12 - 5) IT systems contribute to

such savings by reducing calls and mails (Anselstet-

ter, 1984) as well as changes to the individual work-

place (Schumann, 1992).

A Literature-based Derivation of a Meta-framework for IT Business Value

297

Aggregated Values Examples of Impacts of the Aggregated Values

COA 1

Operational time and

cost savings at firm-

level

Labor cost reduction (Shang und Sheddon, 2002; Mooney et al., 1996), Cost reductions (Porter and Millar, 1985; Parsons, 1983; O'Leary, 2004;

Gammelgard et al., 2006; Gable et al., 2008), Productivity Improvements (O'Leary, 2004; Gable et al., 2008; Andresen et al., 2002; Parson, 1983), Overall

operation efficiency and effectiveness (Shang und Sheddon, 2002; Mooney et al., 1996; Bamöl und Ickler, 2008), Speed up transactions or shorten product

cycles (Mirani und Lederer, 1998), Reduced planning times (Andresen et al., 2002), Enabling faster access to information (Gregor et al., 2006), ...

COA 2

Immediate

improvements in

management process

Improving information accuracy (Gregor et al., 2006), Availability of new, better or more information providing opportunity to compete morc effectively

(Parker et al., 1988), New Reports/Reporting Capability (O'Leary, 2004), Improved ability to coordinate and integrate (Gammelgard et al., 2006), Increase

the flexibility of information requests (Mirani und Lederer, 1998), Better asset management (Shang und Sheddon, 2002), ...

COA 3

Development of new

business fields

Business growth with increased employees, new policies and procedures (Shang und Sheddon, 2002), Improved capture of design and construction

decisions (Andresen et al., 2002), Development of new business fields (Baumöl and Ickler, 2008; Bartsch, 2015), Better research/development planning

(Anselstetter, 1984), ...

COA 4

Improved market

positioning of the

company

Enable new market strategy (Shang und Sheddon, 2002), Help establish useful linkages with other organizations (Mirani und Lederer, 1988; Andresen et

al., 2002; Gregor et al., 2006), Improved strategy formulation and planning (Gammelgard et al., 2006), Strategic competitive advantage (Andresen et al.,

2002; Weill and Boradbent, 1998), ...

COA 5

Improved corporate

growth (and

reporting)

Business growth in transaction volume, processing capacity and capability (Shang und Sheddon, 2002), Reporting (Mooney et al., 1996), Business growth

in new markets (Shang and Sheddon, 2002), …

COA 6

Increased flexibility

to addapt to future

changes

Global resource management (Shang und Sheddon, 2002), Expandable to a range of applications (Shang und Sheddon, 2002), Improved organizational

culture (Gammelgard et al., 2006), Improved change management (Gammelgard et al., 2006), Increased business flexibility (Andresen et al., 2002; O'Leary,

2004), Reduced technology risks (Andresen et al., 2002), ...

COA 7

Growth management

Build cost leadership (Shang und Sheddon, 2002), Increased market share (Andresen et al., 2002), Leverage Size (O'Leary, 2004), Revenue increases

through product differentiation (Schumann, 1992), ...

COA 8

Creating/defending

competitive

advantages

Enable the organization to catch up with competitors (Mirani and Lederer, 1998), Improved relations with external parties that are neither customers,

competitors nor suppliers (Gammelgard et al., 2006), Negating existing entry barriers (Parsons, 1983; Schulze, 2009), Creating new entry barriers (Parsons,

1983; Schulze, 2009), ...

COA 9

Improved integration

and information flow

Improved communication (Gammelgard et al., 2006), Make use of extensive user feedback (Riggins, 1999), Fewer information bottlenecks (Andresen et al.,

2002), Enabling easier access to information (Gregor et al., 2006), Smoother work flow (Vanlommel and Brabander, 1975), Business integration (Weill and

Broadbent, 1998), Information processing efficiency (Parker et al., 1988), ...

COA 10

Impoved employee

satisfaction and

performance

Greater employee involvement in business management (Shang and Seddon, 2002), Increased employee satisfaction with better decision making tools

(Shang and Sheddon, 2002), Satisfied employees for better employee service (Shang and Sheddon, 2002), Creativity (Mooney et al., 1996), ...

COA 11

IT-Investment costs

Acquisition and implementation costs (Anselstetter, 1985), Personnel costs for training and instruction (Anselstetter, 1985), indirect investment costs

(Schulze, 2010), …

COA 12

Time savings in

daily business

operations

Labour time saving (Kesten et al., 2007), Fewer phone calls (Anselstetter, 1985), Fewer letters (Anselstetter, 1985), …

HR 1

Staff reductions

Save money by avoiding the need to increase the work force (Mirani and Lederer, 1998; Gregor et al., 2006), enhances effectiveness in the job (Gable et al.,

2008), Reduced staff requirement (Andresen et al., 2002), Personnel Reduction (O'Leary, 2004; Petrovic, 1994; Anselstetter, 1984), …

HR 2

Improving employee

skills

Shorten learning time (Shang and Sheddon, 2002), Improved learning and/or increased knowledge of persons in the organization (Gammelgard et al., 2006;

Gregor et al., 2006), learning throug the presence of IS (Gable et al., 2008), Enabling of cross-functional teams (Andresen et al., 2002), ...

Log 1

Reduced inventory

and better inventory

management

Inventory Reduction (O'Leary, 2004; Schumann, 1992), Higher turnover inventory (Vanlommel and Brabander, 1975; Anselstetter, 1984), Íncreasing the

speed of distribution (Parsons, 1983), Improved delivery scheduling (Andresen et al., 2002), …

Log 2

Improved inventory

control

Better inventory management (Shang and Sheddon, 2002), More precise production planning, control and monitoring (Anselstetter, 1984), Improved

operational decisions (Shang and Sheddon, 2002), …

M&S 1

Improved Marketing

& Sales capabilities

Multi-currency capability (Shang and Sheddon, 2002), Improving external access to stock levels and price information (Andresen et al., 2002), Ability to

provide instant price quotations to clients (Andresen et al., 2002), …

M&S 2

Improved customer

retention

Improve customer relations (Gammelgard et al., 2006; Gregor et al., 2006), Customer loyalty (Schulze, 2009; Kesten et al., 2007), ...

M&S 3

Increased Sales

Provide new products or services to customers (Mirani and Lederer, 1998), Increased Sales (Andresen et al., 2002, Weill and Broadbent, 1989), Customer

Responsiveness (O'Leary, 2004), …

M&S 4

Time savings in

Marketing & Sales

and product delivery

Sales Automation (O'Leary, 2004), Faster invoicing (Andresen et al., 2002), Easily find the best offer (Schumann, 1992), Faster and more secure checkout

processing (Anselstetter, 1984), …

M&S 5

Leveraging

marketing and sales

capabilities as

competitive

advantages

Improved company image (Andresen et al., 2002), Easier decision making for buyers due to improved evaluation of sources of materials (Porter and Millar,

1985), Better marketing information (Vanlommel and Brabander, 1975), More detailed market analyses (Anselstetter, 1984), …

M&S 6

Improved sales

management

More precise sales planning, control and monitoring (Anselstetter, 1984), More precise assortment analysis (Anselstetter, 1984), Faster and more cost-

effective information on the success of marketing measures (Schumann, 1992), ...

Ops 1

Improved production

processes

Reduced construction time (Andresen et al., 2002), Manufacturing performance (Shang and Sheddon, 2002), improved outcomes or outputs (Gable et al.,

2008), Reducing operating costs (Gregor et al., 2006), Throughput (Mooney et al., 1996), …

Ops 2

Improved product

and production

quality

Quality improvement (Shang and Sheddon, 2002; Kesten et al., 2007), Higher degree of standardization of operations (Vanlommel and Brabander, 1975),

Contribute to high quality (Parsons, 1983), …

Proc 1

More efficient

procurement of

materials

Improved supplier relations (Gammelgard et al., 2006), Procurement Cost Reduction (O'Leary, 2004), Faster response to supplier quotations (Andresen et

al., 2002), Cost reduction in the area of raw materials (Vanlommel and Brabander, 1975; Anselstetter, 1984), …

Proc 2

Strengthening the

companies position

towards suppliers

Better supplier selection (Anselstetter, 1984), Strengthening negotiating power with suppliers (Bartsch, 2015), …

Ser 1

Improved quality

and delivery of

customer services

Faster delivery of services (Andresen et al., 2002), Improved delivery of products/services (Gammelgard et al., 2006), Improved quality of products/services

(Gammelgard et al., 2006), Better customer service (Anselstetter, 1984), Providing customized product or services (Shang and Sheddon, 2002), Improved

focus on client requirements (Andresen et al., 2002), Better service to customers (Vanlommel and Brabander, 1975; Anselstetter, 1984), Establish 24 × 7

customer service (Riggins, 1999), Contribute to superior customer service (Parsons, 1983; Shang and Sheddon, 2002; Schumann, 1992)

TD 1

Improved IT-

Infrastrucutre

support

Save money by reducing system modification or enhancement costs (Mirani and Lederer, 1998), Mainframe or hardware replacing (Shang and Sheddon,

2002), Provide the ability to perform maintenance faster (Mirani and Lederer, 1998), Integration of new functions (Baumöl and Ickler, 2008), Increasing

system stability (Kesten et al., 2007), ...

TD 2

Imporved R&D and

Life Cycles

Continuous improvement in system process and technology (Shang and Sheddon, 2002),

Allow other applications to be developed faster (Mirani and

Lederer, 1998), Speeded up by product life cycle by shortening the development process (Parsons, 1983; Mooney et al., 1996), Making new businesses

technologically feasible (Porter and Millar, 1985), ...

Figure 6: IT Value Framework.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

298

5 DISCUSSION AND

IMPLICATIONS

Although, the meaning of the IT business value is

agreed upon in literature (Melville et al., 2004; De-

varaj and Kohli, 2003; Mooney et al., 1996), its de-

composition to detailed and measurable atomic im-

pacts of the IT system remains either undescribed or

varies strongly across existing literature. Hence, we

provide decision makers with an IT value framework

with possible IT system related impacts and exem-

plary references for the structured assessment of IT

impacts. The framework is customizable to a speciﬁc

IT investment situation. A proper IT business value

assessment requires for IT- and company-speciﬁc im-

pacts (Brynjolfsson and Hitt, 2003). However, the IT

value framework is neither IT- nor company-speciﬁc

so that it requires further customization. Neverthe-

less, the IT value framework allows for a more com-

prehensive IT business value assessment as it contains

more information in a (more) structured form as de-

cision makers usually attempt when applying rules

of thumb (Schniederjans et al., 2010). While exist-

ing IT value catalogs only incorporate impacts from a

single existing catalog (Samulat, 2015; Gregor et al.,

2006; Riggins, 1999; Bartsch, 2015), we draw on 32

value catalogs with a total of 957 impacts and pro-

pose a hierarchy to aggregate the impacts to a single

root value (level 1). With our IT value framework

we aim at aligning the different connotations of the

IT impacts in existing value catalogs (Melville et al.,

2004). During the coding we deviate from the au-

thor’s classiﬁcation of impacts. O’Leary for example,

categorizes some impacts as intangible (e.g. customer

responsiveness, cost reduction) (O’Leary, 2004) that

we coded as tangible as KPIs exist to economically

measure them. There exist further alternatives for the

aggregated values besides the business units. Below

the business unit level, several clusters are concerned

with the competitive positioning (e.g. Proc 2, COA

4, COA 8) or the organizational capabilities to ex-

ploit future potentials (M&S 1, COA 6). These aggre-

gations may also serve as aggregated values in cus-

tomized versions of the framework. We provide an

overview of the existing literature on IT impacts and

provide decision makers with additional exemplary IT

impacts and references. The framework can be used

both for the identiﬁcation of impacts a priori to an

IT investment decision and during the project imple-

mentation for the controlling of impact achievement

(Sch

utte et al., 2019).

Due to the changing role of IT and the progress

(DeLone and McLean, 2003) from the early 1970s

until today, existing catalogs must be viewed criti-

cally. While (Anselstetter, 1984) for example high-

lights the reduction of paperwork as a main IT busi-

ness value, more recent catalogs focus on capabilities

enabled by introducing future IT systems (Melville

et al., 2004; Kurniawan et al., 2016). IT systems have

developed from the support of human actors to a high

level of automation in many industries. This develop-

ment needs to be considered when dealing with gen-

eral impacts extracted from IT value catalogs. Ap-

plying our IT value framework or IT value catalogs

in general, decision makers must pay attention that

speciﬁc atomic impact are only included in one im-

pact category. Otherwise, a possible double account-

ing would distort the IT business value assessment

(Bartsch, 2015). Although our sample of 32 IT value

catalogs results in a total of 957 impacts, we do not

raise a claim for completeness, as our systematic liter-

ature review was rather narrow compared to vague ter-

minology on value catalogs. Thus, we started our lit-

erature research on major IT business value (and Ger-

man equivalent) literature and focused on forward and

backward search (Webster and Watson, 2002). Be-

cause of our focus on scientiﬁc literature, we excluded

practitioner contributions on IT value catalogs and did

not include additional impacts not mentioned in prior

research. As indicated in the scientiﬁc approach sec-

tion (Section 3), we reached substantial results for the

coding of the business unit and direction dimension

but only achieved fair results for the performance fo-

cus and time of occurrence. The coders disagreed on

the performance focus of impacts such as “Creating

competitive advantage” (Gregor et al., 2006), “Create

service excellence” (Gregor et al., 2006), or “Alter-

ing the product lifecycle” (Parsons, 1983). It seems

that these dimensions require for further characteris-

tics as a differentiation between operational and man-

agement level is difﬁcult. For the dimension time of

occurrence an additional characteristic indicating the

quality of the IT system as enabler for future impacts

may be more comprehensive (Gable et al., 2008).

Nevertheless, we argue that the independent coding

of ﬁve experts with a Fleiss’ Kappa of 0.35 is still a

valid result (Landis and Koch, 1977). While recent

research generally suggest Krippendorff’s alpha (Nili

et al., 2017), Fleiss’ Kappa is expected to provide sim-

ilar results for the speciﬁcations of our coding (Landis

and Koch, 1977) while being much more adaptable to

the tools for the statistic evaluation (VBA) (Nili et al.,

2017). Kappa statistics may also be subject to a para-

dox in which a strong agreement between the coders

is reﬂected by a disproportionately low index, which

has to be taken into account during the analysis. The

rather complex coding scheme and the large amount

of codes increases the probability for coding errors

A Literature-based Derivation of a Meta-framework for IT Business Value

299

(Campbell et al., 2013). We tried to minimize the cog-

nitive difﬁculty for the coders by training, providing

a coding manual (Nili et al., 2017), and conducting

the coding in multiple stages (Jourdan et al., 2008;

Neuendorf, 2002). However, this circumstance must

also be considered during our analysis. Once the in-

tercoder reliability has been calculated, the question

arises as to what constitutes an acceptable reliabil-

ity level. While most reliability measures, e.g. per-

center agreement, require a high level of agreement,

Kappa values can be accessed by more liberal criteria

due to their relatively conservative indices (Lombard

et al., 2002). Thus, we adopted the agreement mea-

sures by Landis and Koch, which of cause represent

an arbitrary division (Landis and Koch, 1977). How-

ever, they provide ﬁxed measures against which we

can benchmark our Kappa values to access the reli-

ability of our coding. Even more importantly those

agreement measures allow us to better identify deviat-

ing understandings of the IT impacts and analyze such

coding variation (Olson et al., 2016). Those ﬁndings

could then be integrated into our IT value framework

to account for different perspectives on IT impacts.

6 CONCLUSION

The IT business value on the highest level of abstrac-

tion is an agreed upon term in literature. However, its

decomposition and assessment on a detailed level of

atomic impacts is often not described. We propose to

hierarchically decompose the IT business value to an

assessable and atomic level via aggregated impacts,

atomic impacts and questions. For assessing the ex-

pected IT business value of an IT investment there al-

ready exist 32 IT value catalogs including 957 possi-

ble impacts for IT that partially differ in denomina-

tion and deﬁnition. The classiﬁcation of 682 atomic

IT impacts results in 29 clusters of atomic impacts

that can be aggregated to a company’s business units

as aggregated values. The meta IT value framework

further provides exemplary impacts and further liter-

ature for assessing each of the atomic impacts more

speciﬁcally. The contribution of this paper is to pro-

vide a comprehensive meta-framework of IT impacts

that takes into account all the key aspects identiﬁed

earlier. This allows a holistic IT impact assessment to

be performed in any practical context, which was not

possible with previous frameworks. We have deliber-

ately chosen the perspective of key business functions

to guide practitioners, but other perspectives may be

considered in future papers.

Another important avenue for future research is

the further operationalization of atomic and exem-

plary impacts with guiding questions, making them

conﬁgurable for speciﬁc IT investments decisions and

industries. As this research focuses on the atomic

impacts within existing values catalogs ignoring the

speciﬁcs and peculiarities of the catalogs itself, fu-

ture research may investigate the IT system- and

company-speciﬁcs of these catalogs. Additionally, as

we solely rely on impacts from existing IT value cat-

alogs taken from the literature, future research should

also integrate practitioner sources and may also in-

corporate additional impacts derived from IT projects.

These additional impacts can reﬂect current trends in

IT system development and incorporate state of the art

processes. In addition, the IT value framework should

be applied in practice to get insights into, for exam-

ple, whether the abstraction of IT business values is

sufﬁcient. This evaluation will be the next step in our

research.

REFERENCES

Adam, D. (1996). Planung und Entscheidung: Modelle-

Ziele-Methoden. Gabler, Wiesbaden.

Andresen, J., Baldwin, A., Betts, M., Carter, C., Hamilton,

A., Stokes, E., and Thorpe, T. (2002). A framework

for measuring IT innovation beneﬁts. Journal of In-

formation Technology in Construction, 5(4):57–72.

Anselstetter, R. (1984). Betriebswirtschaftliche Nutzeffekte

der Datenverarbeitung: Anhaltspunkte f

ur Nutzen-

Kosten-Sch

atzungen. Springer, Berlin Heidelberg.

Bailey, K. D. (1994). Typologies and Taxonomies. An In-

troduction to Classiﬁcation Techniques. Sage publi-

cations, Thousand Oaks, California.

Bakos, J. Y. (1987). Dependent Variables for the Study of

Firm and Industry-Level Impacts of Information Tech-

nology. In Proceedings of the 8th International Con-

ference on Information Systems (ICIS 1987), pages 1–

25, Pittsburgh.

Bartsch, S. (2015). Ein Referenzmodell zum Wertbeitrag der

IT. Springer Fachmedien, Wiesbaden.

Barua, A., Kriebel, C. H., and Mukhopadhyay, T. (1995).

Information technologies and business value: An ana-

lytic and empirical investigation. Information Systems

Research, 6(1):3–23.

Baum

ol, U. and Ickler, H. (2008). Wertorientiertes

IT-Controlling Projektselektion als Werkzeug der

Projektportfolio-Steuerung. In Bichler, M., Hess,

T., Krcmar, H., U, L., Matthes, F., Picot, A.,

Speitkamp, B., and Wolf, P., editors, Multikonferenz

Wirtschaftsinformatik, pages 975–987, Berlin. GITO.

Belleﬂamme, P. (2001). Oligopolistic competition, IT use

for product differentiation and the productivity para-

dox. International Journal of Industrial Organization,

19(1-2):227–248.

Brynjolfsson, E. (1993). The Productivity Paradox of Infor-

mation Technology: Review and Assessment. Com-

munications of the ACM, 36(12):67–77.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

300

Brynjolfsson, E. and Hitt, L. M. (1998). Beyond the

Productivity Paradox. Communications of the ACM,

41(8):49–55.

Brynjolfsson, E. and Hitt, L. M. (2003). Computing pro-

ductivity: Firm-level evidence. Review of economics

and statistics, 85(4):793–808.

Campbell, J. L., Quincy, C., Osserman, J., and Pedersen,

O. K. (2013). Coding in-depth semistructured inter-

views: Problems of unitization and intercoder reliabil-

ity and agreement. Sociological Methods & Research,

42(3):294–320.

Cao, G. (2010). A four-dimensional view of IT business

value. Systems Research and Behavioral Science,

27(3):267–284.

Chau, P. Y., Kuan, K. K., and Liang, T. P. (2007). Research

on IT value: what we have done in Asia and Europe.

European Journal of Information Systems, 16(3):196–

201.

DeLone, W. H. and McLean, E. R. (2003). The DeLone

and McLean model of information systems success: A

ten-year update. Journal of Management Information

Systems, 19(4):9–30.

Denscombe, M. (2008). Communities of practice: A re-

search paradigm for the mixed methods approach.

Journal of mixed methods research, 2(3):270–283.

Devaraj, S. and Kohli, R. (2003). Performance impacts of

information technology: Is actual usage the missing

link? Management Science, 49(3):273–289.

Farbey, B., Land, F., and Targett, D. (1992). Evaluating

investments in IT. Journal of Information Technology,

7(2):109–122.

Fersht, P. and Snowdon, J. (2020). RPA isn’t having a

renaissance, it’s catalyzing broader White Collar au-

tomation.

Fleiss, J. L. (1971). Measuring nominal scale agree-

ment among many raters. Psychological bulletin,

76(5):395–382.

Gable, G. G., Sedera, D., and Chan, T. (2008). Re-

conceptualizing information system success: The IS-

impact measurement model. Journal of the associa-

tion for information systems, 9(7):377–408.

Gammelg

ard, M., Ekstedt, M., and Gustafsson, P. (2006).

A categorization of beneﬁts from IS/IT investments.

In proceedings of the 13th European Conference on

Information Technology Evaluation, ECITE.

Gregor, S., Martin, M., Fernandez, W., Stern, S., and Vi-

tale, M. (2006). The transformational dimension in the

realization of business value from information tech-

nology. Journal of Strategic Information Systems,

15(3):249–270.

Hajli, M., Sims, J. M., and Ibragimov, V. (2015). Infor-

mation technology (IT) productivity paradox in the

21st century. International Journal of Productivity

and Performance Management, 64(4):457–478.

Jourdan, Z., Rainer, R. K., and Marshall, T. E. (2008). Busi-

ness Intelligence: An Analysis of the Literature. In-

formation systems management, 25(2):121–131.

Kauffman, R. J. and Weill, P. (1989). An evaluative frame-

work for research on the performance effects of in-

formation technology investment. In Proceedings of

the 10th International Conference on Information Sys-

tems (ICIS 1989), pages 1–18, Boston.

Kesten, R., M

uller, A., and Schr

oder, H. (2007). IT-

Controlling. Messung und Steuerung des Wertbeitrags

der IT. Vahlen, M

unchen.

Ketchen, D. J. and Shook, C. L. (1996). The application of

cluster analysis in strategic management research: an

analysis and critique. Strategic management journal,

17(6):441–458.

Kurniawan, N. B., Putro, B. L., and Yustianto, P. (2016).

IT value model driven by information and services: A

conceptual approach. In Proceedings of the Interna-

tional Conference on Information Technology Systems

and Innovation (ICITSI 2016), pages 1–5. IEEE.

Landis, J. R. and Koch, G. G. (1977). The measurement of

observer agreement for categorical data. Biometrics,

33(1):159–174.

Lombard, M., Snyder-Duch, J., and Bracken, C. C. (2002).

Content analysis in mass communication: Assessment

and reporting of intercoder reliability. Human commu-

nication research, 28(4):587–604.

Lucas, H. C. (2000). Information technology and the pro-

ductivity paradox: Assessing the value of investing in

IT. Oxford University Press.

Masli, A., Richardson, V. J., Sanchez, J. M., and Smith,

R. E. (2011). The business value of IT: A synthesis

and framework of archival research. Journal of Infor-

mation Systems, 25(2):81–116.

Melville, N., Kraemer, K., and Gurbaxani, V. (2004). Re-

view: Information technology and organizational per-

formance: An integrative model of it business value.

MIS Quarterly, 28(2):283–322.

Mirani, R. and Lederer, A. L. (1998). An instrument for as-

sessing the organizational beneﬁts of IS projects. De-

cision Sciences, 29(4):803–838.

Mooney, J. G., Gurbaxani, V., and Kraemer, K. L. (1996). A

Process Oriented Framework for Assessing the Busi-

ness Value of Information Technology. Data Base for

Advances in Information Systems, 27(2):68–81.

uller, O., Fay, M., and vom Brocke, J. (2018). The Ef-

fect of Big Data and Analytics on Firm Performance:

An Econometric Analysis Considering Industry Char-

acteristics. Journal of Management Information Sys-

tems, 35(2):488–509.

Neuendorf, K. A. (2002). The Content Analysis Guidebook.

SAGE, Thousand Oaks, CA.

Nickerson, R. C., Varshney, U., and Muntermann, J. (2013).

A method for taxonomy development and its applica-

tion in information systems. European Journal of In-

formation Systems, 22(3):336–359.

Nili, A., Tate, M., and Barros, A. (2017). A Critical Anal-

ysis of Inter-Coder Reliability Methods in Informa-

tion Systems Research. In Proceedings of the 28th

Australasian Conference on Information Systems, vol-

ume 1, pages 1–11.

O’Leary, D. E. (2004). Enterprise Resource Planning (ERP)

Systems: An Empirical Analysis of Beneﬁts. Journal

of Emerging Technologies in Accounting, 1(1):63–72.

Olson, J. D., McAllister, C., Grinnell, L. D., Gehrke Wal-

ters, K., and Appunn, F. (2016). Applying Constant

Comparative Method with Multiple Investigators and

A Literature-based Derivation of a Meta-framework for IT Business Value

301

Inter-Coder Reliability. Qualitative Report, 21(1):26–

42.

Parasuraman, A., Zeithaml, V. A., and Malhotra, A. (2005).

E-S-Qual: A Multiple-Item Scale for Assessing Elec-

tronic Service Quality. Journal of Service Research,

7(3):213–233.

Parker, M., Benson, R., and Trainor, E. (1988). Information

economics: linking performance to information tech-

nology. Prentice-Hall, Englewood Cliffs, New Jersey.

Parsons, G. L. (1983). Information technology: a new com-

petitive weapon. Sloan Management Review, 25(1):3–

14.

Pathak, S., Krishnaswamy, V., and Sharma, M. (2019). Im-

pact of IT practices and business value of IT measure-

ment. International Journal of Productivity and Per-

formance Management, pages 774–793.

Petrovic, O. (1994). Lean Management und information-

stechnologische Potentialfaktoren. Wirtschaftsinfor-

matik, 36(6):580–590.

Porter, M. E. (2001a). Strategy and the Internet. Harvard

Business Review, 79(3):63–78.

Porter, M. E. (2001b). The value chain and competitive ad-

vantage. In Barnes, D., editor, Understanding Busi-

ness: Processes, pages 50–66. Routledge, London,

New York.

Porter, M. E. and Millar, V. E. (1985). How information

gives you competitive advantage. Harvard Business

Review, 63(3):149–160.

Riggins, F. J. (1999). A Framework for Identifying Web-

Based Electronic Commerce Opportunities. Journal

of Organizational Computing and Electronic Com-

merce, 9(4):297–310.

Samulat, P. (2015). Methode f

ur Messungen

und Messgr

oßen zur Darstellung des

Ex-

post

Wertbeitrages von IT-Projekten. HMD Praxis der

Wirtschaftsinformatik, 52(4):624–635.

Schniederjans, M. J., Hamaker, J. L., and Schniederjans,

A. M. (2010). Information Technology Investment.

Decision-Making Methodology, volume 2. World Sci-

entiﬁc Publishing, Singapur.

Schryen, G. (2013). Revisiting IS business value research:

What we already know, what we still need to know,

and how we can get there. European Journal of Infor-

mation Systems, 22(2):139–169.

Schulze, U. (2009). Informationstechnologieeinsatz im

Supply Chain Management - Eine konzeptionelle und

empirische Untersuchung zu Nutzenwirkungen und

Nutzenmessung. Gabler, Wiesbaden.

Schulze, U. (2010). Informationstechnologieeinsatz im

Supply Chain Management: eine konzeptionelle und

empirische Untersuchung zu Nutzenwirkungen und

Nutzenmessung. Springer, Wiesbaden, 1 edition.

Schumann, M. (1992). Betriebliche Nutzeffekte und Strate-

giebeitr

age der großintegrierten Informationsverar-

beitung, volume 52. Springer, Berlin Heidelberg.

Sch

utte, R., Seufert, S., and Wulfert, T. (2019). Das

Wertbeitragscontrolling als Anreicherung bestehen-

der Vorgehensmodelle des Software Engineering. In

Oliver Linssen et al., editor, Projektmanagement und

Vorgehensmodelle, pages 111–125, L

orrach. Lecture

Notes in Informatics, Gesellschaft f

ur Informatik.

Schwarz, A., Mehta, M., Johnson, N., and Chin, W. W.

(2007). Understanding frameworks and reviews. Ad-

vances in Information Systems, 38(3):29–50.

Shang, S. and Seddon, P. B. (2002). Assessing and man-

aging the beneﬁts of enterprise systems: The business

manager’s perspective. Information Systems Journal,

12(4):271–299.

Soh, C. and Markus, M. L. (1995). How IT Creates Busi-

ness Value: A Process Theory Synthesis. In ICIS 1995

Proceedings.

Tallon, P. P. and Kraemer, K. L. (2003). Investigating

the Relationship between Strategic Alignment and IT

Business Value : The Discovery of a Paradox. In Shin,

N., editor, Creating Business Value with Information

Technology: Challenges and Solutions, pages 1–22.

IRM Press, Hershey.

Tallon, P. P. and Kraemer, K. L. (2006). The Development

and Application of a Process-oriented ”Thermometer”

of IT Business Value. Communications of the Associ-

ation for Information Systems, 17(June):995–1027.

auscher, K. and Laudien, S. (2018). Understanding

platform business models: A mixed methods study

of marketplaces. European Management Journal,

36(3):319–329.

Urbach, N., Smolnik, S., and Riempp, G. (2009). The

State of Research on Information Systems Suc-

cess. Business & Information Systems Engineering,

1(4):315–325.

Vanlommel, E. and De Brabander, B. (1975). The organiza-

tion of electronic data processing (EDP) activities and

computer use. The Journal of Business, 48(3):391–

410.

Wang, N., Liang, H., and Zhong, W. (2012). Resource

Structuring or Capability Building? An Empirical

Study of the Business Value of Information Technol-

ogy. Journal of Management Information Systems,

29(2):325–367.

Weber, R. P. (1990). Basic Content Analysis. Sage Publica-

tions, inc, Thousand Oaks, CA, 49 edition.

Webster, J. and Watson, R. T. (2002). Analyzing the Past to

Prepare for the Future: Writing a Literature Review.

MIS Quarterly, pages xiii–xxiii.

Weill, P. and Broadbent, M. (1998). Leveraging the new in-

frastructure: how market leaders capitalize on infor-

mation technology. Harvard Business Press, Boston,

Mass.

ICEIS 2021 - 23rd International Conference on Enterprise Information Systems

302