Combining Qualitative and Quantitative Methods in ... - Blog Staff UI

Qualitative & Quantitative Methods 

Combining Qualitative 

and Quantitative 

Methods in 

Information Systems 

Research: 

A Case Study^ 

By: Bonnie Kaplan 

Department of Quantitative 

Anaiysis and information 

Systems 

University of Cincinnati 

Cincinnati, OH 45221-0130 

Dennis Duchon 

Division of iVIanagement 

and Mariteting 

Coiiege of Business 

University of Texas 

at San Antonio 

San Antonio, TX 78285-0634 

Abstract 

This article reports how quantitative and quaiitative 

methods were combined in a longitudinal 

multidisciplinary study of interreiationships between 

perceptions of work and a computer information 

system. The articie describes the 

problems and contributions stemming from 

different research perspectives and methodological 

approaches. It illustrates four methodological 

points: (1) the value of combining quaiitative 

and quantitative methods; (2) the need for 

context-specific measures of job characteristics 

rather than exciusive reliance on standard context-independent 

instruments; (3) the importance 

of process measures when evaluating information 

systems; and (4) the need to explore 

the necessary relationships between a computer 

system and the perceptions of its users, 

rather than unidirectional assessment of computer 

system impacts on users or of users characteristics 

on computer system impiementation. 

Despite the normative nature of these points, 

the most important conclusion is the desirability 

for a variety of approaches to studying information 

systems. No one approach to information 

systems research can provide the richness that 

information systems, as a discipline, needs for 

further advancement. 

Keywords: Methodology, research methods, research 

perspectives, qualitative methods, 

interpretivist perspective, computer 

system impacts, computer 

system evaluation, organizational impacts, 

work, medical and health 

care applications 

ACM Categories: K,4,3, H.O, J,3 

' This paper was presented at the Ninth Annual International 

Conference on Information Systems, Minneapolis, 

MN, November 30-December 3, 1988, 

Introduction 

Information systems had its origins in a variety 

of reference disciplines with distinct theoretical 

research perspectives on the important issues 

to study and the methods to study them (Bariff 

and Ginzberg, 1982; Dickson, et al., 1982; Mendelson, 

et al,, 1987), This article describes a 

study that combined some of these distinct perspectives 

and methods. The article discusses 

how limitations of one research perspective can 

be addressed by also using an alternative. This 

emphasis reflects the lesser familiarity information 

systems researchers might have with the 

perspective receiving more in-depth discussion 

and promotion, A key point to this article is the 

importance that both perspectives had for this 

study; no implication of preference in perspectives 

is intended. 

The discussion of perspectives provides background 

for understanding the process and methods 

of this research. Following the discussion. 

MIS Quarterly/December 1988 571

Quaiitative & Quantitative Methods 

the article describes how this study evolved. The 

emphasis is on methods rather than on research 

findings. 

The positivist perspective and 

quantitative methods 

Despite the differences in reference disciplines 

and the debate over a paradigm for information 

systems, American information systems research 

generally is characterized by a methodology of 

formulating hypotheses that are tested through 

controlled experiment or statistical analysis. The 

assumption underlying this methodological approach 

is that research designs should be based 

on the positivist model of controlling (or at least 

measuring) variables and testing pre-specified 

hypotheses (Kauber, 1986), although alternative 

methods might be acceptable until research has 

reached this more advanced and "scientific" 

stage. Despite some recent recognition given different 

research perspectives and methods (e.g,, 

Ives and Olson, 1984; Klein, 1986; Kling, 1980; 

Lyytinen, 1987; Markus and Robey, 1988; 

Weick, 1984), even those who argue for introducing 

doctoral students to such alternative approaches 

as field study and simulation methods 

nevertheless advocate research based primarily 

on the positivist tradition (e.g,, Bariff and 

Ginzberg, 1982; Dickson, et al,, 1982), 

Exclusive reliance on statistical or experimental 

testing of hypotheses has been soundly criticized 

in the social sciences, where some of its major 

proponents have called its effects "disastrous" 

(Cook and Campbell, 1979, p, 92), There are 

two grounds on which the approach can be 

faulted. First, the assumption that only through 

statistical or experimental hypothesis testing will 

science progress has come under attack, particularly 

by psychologists who perhaps have the 

dubious distinction of having practiced it the longest, 

Meehl (1978), for example, argues that science 

does not, and cannot, proceed by incremental 

gains achieved through statistical significance 

testing of hypotheses. Sociologists, too, 

have contributed to this debate, notably with 

Glaser and Strauss' (1967) influential argument 

for theory building through inductive qualitative 

research rather than through continual hypothesis 

testing. 

A second fault with the approach is the reliance 

on experimental or statistical control as the defining 

feature of scientific research. This reliance 

stems from the admirable goal of controlling experimenter 

bias by striving for objective measures 

of phenomena. Achieving this goal has 

been assumed to require the use of quantifiable 

data and statistical analysis (Downey and Ireland, 

1983; Kauber, 1986) and also removing 

the effects of context in order to produce generalizable, 

reproducible results. 

However, because the study of social systems 

involves so many uncontrolled — and unidentified 

— variables, methods for studying closed 

systems do not apply as well in natural settings 

as in controlled ones (Cook and Campbell, 1979; 

Manicas and Secord, 1983; Maxwell, et al,, 

1986), Moreover, the simplification and abstraction 

needed for good experimental design can 

remove enough features from the subject of 

study that only obvious results are possible. As 

illustrated in the next section, the stripping of 

context buys "objectivity" and testability at the 

cost of a deeper understanding of what actually 

is occurring,^ 

The interpretive perspective and 

quantitative methods 

The need for context-dependent research has 

been remarked upon by researchers in a variety 

of disciplines that, like information systems, necessarily 

incorporate field research methods. 

Even such strong advocates of quantitative and 

experimental approaches in behavioral research 

as Cook and Campbell (1979) state, "Field experimentation 

should always include qualitative 

research to describe and illuminate the context 

and conditions under which research is conducted" 

(p, 93), 

Immersion in context is a hallmark of qualitative 

research methods and the interpretive perspective 

on the conduct of research. Interpretive researchers 

attempt to understand the way others 

construe, conceptualize, and understand events, 

concepts, and categories, in part because these 

are assumed to influence individuals behavior. 

The researchers examine the social reality and 

intersubjective meanings held by subjects (Bredo 

and Feinberg, 1982a) by eliciting and observing 

what is significant and important to the subjects 

in situations where the behavior occurs ordinarily. 

Consequently, qualitative methods are characterized 

by (1) the detailed observation of, and 

^ We would like to credit an anonymous reviewer with 

this point. 

572 MIS Quarterly/December 1988


involvement of the researcher in, the natural setting 

in which the study occurs, and (2) the attempt 

to avoid prior commitment to theoretical 

constructs or to hypotheses formulated before 

gathering any data (Yin, 1984), 

Qualitative strategies emphasize an interpretive 

approach that uses data to both pose and resolve 

research questions. Researchers develop 

categories and meanings from the data through 

an iterative process that starts by developing an 

initial understanding of the perspectives of those 

being studied. That understanding is then tested 

and modified through cycles of additional data 

collection and analysis until coherent interpretation 

is reached (Bredo and Feinberg, 1982a; Van 

Maanen, 1983b), Thus, although qualitative methods 

provide less explanation of variance in statistical 

terms than quantitative methods, they can 

yield data from which process theories and richer 

explanations of how and why processes and outcomes 

occur can be developed (Marcus and 

Robey, 1988), 

Research traditions in information 

systems 

The growing recognition of the value of qualitative 

methods in social, behavioral, organizational, 

and evaluation research is manifest in studies 

and research methodology texts (Argyris, 1985; 

Bredo and Feinberg, 1982b; Lincoln and Guba, 

1985; Miles and Huberman, 1984; Mintzberg, 

1973; Patton, 1978; Van Maanen, 1983c), Van 

Maanen (1983a), for example, has long advanced 

and practiced these approaches in organizational 

research. However, despite the 

strong ties of information systems with organizational 

and behavioral research, the use of qualitative 

research, though practiced and advocated 

in information systems, has not been as visible 

in this field as in others. Instead, recently there 

has been greater reliance on laboratory studies 

and surveys (Goldstein, et al,, 1986), 

The dominant approach to information technology 

studies has been based on a positivistic experimental 

ideal of research. Using this approach, 

researchers examine the effects of one 

or more variables on another. These analyses 

tend to treat the research objects in one of two 

ways. Either they portray information technology 

as the determining factor and users as passive, 

or they view users or organizations as acting 

in rational consort to achieve particular outcomes 

through the use of information technology. In 

either case, the nature of the information technology 

and users is considered static in that they 

are assumed to have an essential character that 

is treated as unchanging over the course of the 

study (Bakos, 1987; Lyytinen, 1987), 

Markus and Robey (1988) characterize such approaches 

as "variance theory formulations of logical 

structure and an imperative conception of 

causal agency," In variance theories, some elements 

are identified as antecedents, and these 

are conceived as necessary and sufficient conditions 

for the elements identified as outcomes 

to occur. According to Markus and Robey, much 

of the thinking about the consequences of information 

technology in organizations assumes that 

either technology ("the technological imperative") 

or human beings ("the organizational imperative") 

are the antecedents, or agents, of change 

rather than that change emerges from complex 

indeterminant interactions between them ("the 

emergent perspective"). 

Most studies of computer systems are based on 

methods that measure quantitative outcomes. 

These outcomes can be grouped into technical, 

economic, and effectiveness and performance 

measures. Such studies treat organizational features, 

user features, technological features, and 

information needs as static, independent, and 

objective rather than as dynamic, interacting constructs, 

i,e,, as concepts with attributes and meanings 

that may change over time and that may 

be defined differently according to how individual 

participants view and experience the relationships 

between them. 

Because such studies are restricted to readily 

measured static constructs, they neglect aspects 

of cultural environment, and social interaction 

and negotiation that could affect not only the outcomes 

(Lyytinen, 1987), but also the constructs 

under study. Indeed, most evaluations of computer 

information systems exhibit these characteristics. 

Published accounts of computing traditionally 

focus on selected technical and economic 

characteristics of the computer system 

that are assessed according to what Kling and 

Scacchi (1982) call the "discrete-entity model," 

Economic, physical, or information processing features 

are explicitly chosen for study under the 

assumption that the computer system can be 

broken down into relatively independent elements 

of equipment, people, organizational processes, 

and the like. These elements can then 

be evaluated independently and additively. 

Social or political issues often are ignored. 

MIS Quarteriy/December 1988 573


That these assumptions underlie much of the 

research on information technology is evident 

in the implementation and impacts literature. The 

effects of some intervention are studied with respect 

to implementation success or impacts on, 

for example, organizational structure, user attitudes, 

or job satisfaction (Bakos, 1987; Danziger, 

1985; Ives and Olson, 1984; Kling, 1980; Markus 

and Robey, 1988), In these studies, information 

systems or computers are treated as having "impacts" 

(Danziger, 1985) rather than as socially 

constructed concepts with meanings that are affected 

by the "impacts" and that change from 

person to person or over time. Few such impact 

studies are longitudinal — a design promoted 

in information systems research to track changes 

over time by collecting data as events occur 

rather than retrospectively (Franz and Robey, 

1984; Vitalari, 1985), 

Often such studies do not proceed from an interactionist 

framework — one that focuses on 

the interaction between characteristics related to 

people or subunits affected by the computer 

system and characteristics related to the system 

itself (Markus and Robey, 1988). For example, 

they tend not to explore interrelationships between 

job-related issues and effectiveness of information 

technology. Jobs are often considered 

fixed, even though there are empirical and theoretical 

reasons to expect that computers change 

the amount and nature of work performed by 

system users (Brooks, et al,, 1977; Fok, et al,, 

1987; Kemp and Clegg, 1987; Kling, 1980; Millman 

and Hartwick, 1987; Zuboff, 1982; 1988), 

Moreover, job-related issues are interpreted differently 

by different individuals within an organization, 

and these differences can affect what constitutes 

a technology's "effectiveness" (Kling, 1980; 

Lyytinen, 1987), Goodhue (1986), for example, 

advises assessing the "fit" of an information system 

with a task. However, one person performing 

the task may have a rather different view of it than 

another person performing ostensibly the same 

task, and thus the "fit" will differ for different users. 

Consequently, different individuals may have 

different responses to the same system, Interactionist 

theories would account for this response. 

Theories that assume that the individual, the job or, 

task, the organization, or the technology is fixed 

and that one of these determines outcomes would 

not (Markus and Robey, 1988), 

Some argue that each type of research method 

has its appropriate uses (Markus and Robey, 

1988; Rockart, 1984; Weick, 1984); different research 

perspectives focus on different research 

questions and analytical assumptions (Kling, 

1980; Kling and Scacchi, 1982; Lyytinen, 1987; 

Markus, 1983; Markus and Robey, 1988), However, 

there is disagreement concerning the value 

and use of suggested alternative theoretical perspectives 

and practical approaches, such as critical 

social theory (Klein, 1986), structuration 

theory (Barley, 1986), case study (Benbasat, et 

al,, 1987), and socio-technical design (Fok, et 

al., 1987; Mumford and Henshall, 1979). On the 

one hand, Mumford (1985) advocates a qualitative 

approach. She calls for studies of a "total" 

situation through action-centered, interdisciplinary, 

participatory research in which research 

questions and hypotheses evolve as new developments 

are introduced, Lyytinen (1987) 

makes a similar appeal for case studies and action 

research on the grounds that "this research 

strategy seems to be the only means of obtaining 

sufficiently rich data" and because the validity of 

such methods "is better than that of empirical 

studies," On the other hand, even some proponents 

of case study base their position on a research 

design that fits the quantitative or quasiexperimental 

approach rather than the qualitative 

one (Benbasat, et al,, 1987; Campbell, 1984; Yin, 

1984), Moveover, there has been strong sentiment 

that information systems researchers need 

to move beyond case study to more experimental 

laboratory or field tests (Benbasat, 1984), 

Combining Methods 

Although not the dominant paradigm, qualitative 

methods and interpretive perspectives have 

been used in a variety of ways in information 

systems research (Barley, 1986; Fok, et al,, 

1987; Franz and Robey, 1984; Goldstein, et al,, 

1986; Hirschheim, et al,, 1987; Markus, 1983; 

Mumford and Henshall, 1979; Mumford, et al., 

1985), Interpreting information technology in 

terms of social action and meanings is becoming 

more popular as evidence grows that information 

systems development and use is a social 

as well as technical process that includes problems 

related to social, organization, and conceptual 

aspects of the system (Bland, 1978; Hirschheim, 

et al., 1987; Kling and Scacchi, 1982; 

Lyytinen, 1987; Markus, 1983), However, many 

information systems researchers who recognize 

the value of qualitative methods often portray 

these methods either as stand-alone or as a 

means of exploratory research preliminary to the 

"real" research of generating hypotheses to be 

tested using experimental or statistical tech- 



niques (Benbasat, 1984), Even papers in which 

qualitative and quantitative methods are combined 

rarely report the study's methodological 

rationale or details (Benbasat, et al,, 1987), One 

result is the failure to discuss how qualitative 

methods can be combined productively with quantitative 

ones. 

There has been a move in other fields toward 

combining qualitative and quantitative methods 

to provide a richer, contextual basis for interpretating 

and validating results (Cook and 

Reichardt, 1979; Light and Pillemer, 1982; 

Maxwell, 1986; Meyers, 1981; Van Maanen, et 

al,, 1982; 1983a), These methods need not be 

viewed as polar opposites (Van Maanen, 

1983b), It is possible to integrate quantitative and 

qualitative methods (Maxwell, et al,, 1986), Combining 

these methods introduces both testability 

and context into the research. Collecting different 

kinds of data by different methods from different 

sources provides a wider range of coverage 

that may result in a fuller picture of the unit 

under study than would have been achieved otherwise 

(Bonoma, 1985), Moreover, using multiple 

methods increases the robustness of results 

because findings can be strengthened through 

triangulation — the cross-validation achieved 

when different kinds and sources of data converge 

and are found congruent (Benbasat, et 

al,, 1987; Bonoma, 1985; Jick, 1983; Yin, 1984), 

or when an explanation is developed to account 

for all the data when they diverge (Trend, 1979), 

This article describes how qualitative and quantitative 

methods were combined during the first 

phase of an ongoing multi-method longitudinal 

study. Detailed research results are reported elsewhere 

(Kaplan, 1986; 1987; Kaplan and Duchon, 

1987a; 1987b; 1987c) and summarized here. This 

article has a methodological focus. It describes the 

development and process of the research and 

omits all but sketches of the data and analysis 

necessary to understand how the research 

evolved. Particular attention is given to how both 

quantitative and qualitative methods were used 

productively in a collaboration among investigators 

from different research perspectives. 

Research Setting 

Organizational and information 

context 

Computers have been used more in clinical laboratories 

than in many other areas of medical practice 

(Paplanus, 1985), The clinical laboratory represents 

a microcosm of automation and information 

needs from throughout a medical center 

(Lincoln and Korpman, 1980), The laboratory is 

responsible for performing tests ordered by physicians 

to diagnose illness or track the course 

of therapy and disease. Laboratories meet this 

responsibility by receiving and processing physicians' 

test orders, i,e,, collecting specimens 

(such as blood) from patients, performing the 

designated tests (such as blood sugar measurements 

or assessments of bacterial sensitivity 

to antibiotics), and reporting the test results for 

use by the physician and for inclusion in the patient's 

medical record. Laboratory technologists, 

who are specially trained, perform the laboratory 

tests. They may also report the results and 

discuss them with those treating the patient. 

The principal function of computers in clinical 

laboratories involves data management. Computers 

can relieve the clerical burden of data 

acquisition and transcription while adding new 

data entry and computer-related tasks. In addition, 

they improve legibility, organization, and accuracy 

of laboratory results reports; increase productivity 

and efficiency; reduce transcription 

error; and change laboratory organization and 

turnaround time for test results (Brooks, et al., 

1977; Flagle, 1974; Lewis, 1979; Nicol and 

Smith, 1986), Thus, such computer information 

systems affect both the process of work as well 

as the service product of a clinical laboratory. 

The research site 

Research was conducted within the Department 

of Pathology and Laboratory Medicine at a 650- 

bed midwestern urban metropolitan university 

medical center, A widely used and well-respected 

commercial laboratory computer information 

system was installed in April 1985 for 

use by all nine laboratories within the Division 

of Laboratory Medicine, These nine laboratories 

were responsible for laboratory work to support 

the care of patients admitted to the hospital and 

those treated in clinics and in the emergency 

unit. The laboratories also performed specialty 

tests for other institutions. 

This research site was selected because a new 

computer information system was replacing a 

manual operation in a context representative of 

the entire institution's information systems needs. 

Another advantage was that multiple organizational 

units would use the same computer 



system. Consequently, a comparison between 

units would be possible under conditions where 

system variables would be reasonably constant. 

Thus, the study could include both macro and 

micro units of analysis: individuals, workers and 

managers, individual laboratories as a whole, the 

department in which the laboratories were situated, 

and the organization as a whole. Mixing 

levels of analysis made it possible to explore 

the interplay among units at each level and 

across levels (Markus and Robey, 1988), 

The site became available because of the first 

author's prior contact with the director of laboratory 

medicine. The director arranged for entry 

into the laboratories and meetings and gave his 

support throughout the study's duration. He was 

available to the research team as needed. 

Research team 

The project was undertaken by four faculty members 

in the College of Business Administration 

at the University of Cincinnati, The researcher 

from the information systems (IS) area, Bonnie 

Kaplan, conceived the project, conducted the 

fieldwork, and provided knowledge of the research 

setting. She envisioned the purpose of 

the study as researching what happens when 

a computer information system is installed into 

a new setting. 

The other three researchers were from the organizational 

behavior area. Two of them left the 

study at an early stage; Dennis Duchon remained. 

Their primary interest was in testing preexisting 

theory in a new setting using questionnaires 

as the means of gathering quantitative 

data for statistical analysis. They viewed qualitative 

methods as a means for deriving quantitative 

measures, rather than as rich sources of 

research data useful for grounding theory and 

interpretation. Consequently, they approached 

the study differently from Kaplan in three ways: 

(1) they decided to research the impact of the 

computer information system on work in the laboratories; 

(2) they began the study intending to 

test theory through statistical analysis of quantitative 

survey data; and (3) they did not consider 

interviewing and observation as a means of data 

collection, 

iVIethods 

Research question 

Each member of the original research team formulated 

different research questions. The three 

organizational behavior members viewed the 

new computer system as an opportunity to test 

existing theory concerning job characteristics 

and job satisfaction in a new setting. Although 

each of their research questions differed, these 

three wished to investigate how job characteristics 

varied in the clinical laboratories. Consequently, 

the study focused on laboratory technologists. 

The research questions for these three 

researchers were to investigate (1) the effects 

of the computer information system on job characteristics, 

(2) how departmental technology affected 

computer acceptance, (3) how leadersubordinate 

relationships affected computer acceptance, 

and (4) how job characteristics varied 

among laboratories and changed over time. 

Working in the interpretive tradition, Kaplan expected 

to shuttle among questions, hypotheses, 

and data throughout the study. Because of the 

other team members' primary interest in the 

laboratory work, she also adopted this focus. Her 

research question was to identify and account for 

both similarities and differences among laboratory 

technologists and among laboratories in 

their responses to the computer information 

system. 

Research design 

Each research question reflected differences between 

a quantitative hypothesis-testing approach 

(wliere the effects of an intervention on dependent 

variables are statistically assessed) and a 

qualitative approach (where categories and theories 

are developed inductively from the data, generalizations 

are built from the ground up, and 

various interpretive schemes are tried and hypotheses 

are created and reformulated during 

the course of the study) (Glaser and Strauss, 

1967; Van Maanen, 1983b), These differences 

resulted in a longitudinal multi-method case 

study that incorporated each team member's interest 

and skills. Because this initial case design 

included both qualitative and quantitative methods, 

both positivist and interpretive perspectives 

were incorporated in order to best link method 

to research question,^ 

^ Case study, an investigation using multiple sources 

of evidence to study a contemporary phenomenon 

within its real-life context (Bonoma, 1985; Yin, 1984), 

has been advanced for information systems research 

in order to understand the nature and complexity of 

the processes taking place (Benbasat, et al,, 1987), 

Although they are often distinguished from quantita- 



The initial design was developed after considerable 

discussion and, as is common in qualitative 

research, was left open for modification and 

extension as necessary during the course of the 

study (Glaser and Strauss, 1967), Because research 

access to the site was not secured until 

shortly before the new system was installed, no 

pre-installation survey measures were taken. Consequently, 

as is often true in case studies, there 

could be no comparison of measures after installation 

(Cook and Campbell, 1979, p, 96), 

The first step in the design was to interview laboratory 

directors and selected hospital administrators 

prior to system installation. The second 

step was to observe in each laboratory after installation. 

The remaining steps were to administer 

questionnaires at several periods after the 

computer system was installed. 

The first wave of questionnaire data gathering 

occurred when a new routine had been 

established after the computer system was 

installed. The second wave was planned for 

approximately one year later, when the initial 

changes caused by the computer system 

became part of normal procedure. Future waves 

would be at intervals depending on initial results. 

Initially, regular participant observation at 

laboratory management meetings and staff 

meetings was not included in the design. This 

component was added when the meetings were 

instituted. 

Data collection 

Qualitative methods included open-ended interviewing, 

observation, participant observation, 

and analysis of responses to open-ended items 

on a survey questionnaire. Quantitative methods 

were employed to collect and analyze data 

from survey questionnaires. All participants were 

assured of confidentiality. 

Although both qualitative and quantitative approaches 

were used, it quickly became apparent 

that they were viewed differently by research 

team members. Each team member conducted 

interviews and observations, but only the qualitive 

or quasi-experimental research (Bonoma, 1985; 

George and McKeown, 1985), case studies may or 

may not exhibit the defining conditions of either quantitative 

or qualitative research (Campbell, 1984; Yin, 

1984), There are a variety of recognized approaches 

to case study (Benbasat, et al,, 1987; Bonoma, 

1985), 

tatively-oriented member kept systematic field 

notes to be used for data analysis, Qther team 

members viewed interviews and observations as 

providing "background" rather than "data," Consequently, 

Kaplan's field notes from each of 

these activities were used for analysis. 

Interviews and Observations 

The director of the laboratories, the chairman 

of the department, and the administrator of the 

hospital were interviewed early in the study. 

Teams of two or three researchers also interviewed 

the individual laboratory directors and 

some chief supervisory personnel during the 

week prior to computer system installation, 

Kaplan was present at all but two of these interviews. 

The purpose of the interviews was threefold: 

(1) to determine what interviewees expected 

the potential effects of the computer 

system to be on patient care, laboratory operations, 

and hospital operations; (2) to inquire 

about possible measures and focus of the study; 

and (3) to generate questionnaire items for a 

survey of laboratory technologists. 

During the month prior to administering the 

survey, individual researchers were present in 

the laboratories to observe and talk with laboratory 

staff while they worked. These observations 

were intended to influence questionnaire item 

development. 

Starting three months after the computer information 

system was installed, Kaplan was urged 

by one of the laboratory directors to attend 

weekly meetings where directors and head supervisors 

discussed laboratory management problems. 

These meetings were instituted as a result 

of system installation and they became a regular 

feature of laboratory management even after 

system problems ceased to be discussed, 

Kaplan attended these meetings regularly throughout 

the study as an observer and occasional participant, 

and was a participant observer at other 

departmental meetings. 

Survey Questionnaire 

A survey instrument was developed for laboratory 

technologists, the primary direct users of the 

computer information system. It was composed 

of three parts. The first part consisted of measures 

adapted from the standard instruments that 

addressed job characteristics (Hackman and 

Qldham, 1976), role conflict and ambiguity 

(House and Rizzo, 1972), departmental technol- 



ogy (Withey, et al,, 1983), and leader-member 

relationships (Dansereau, et al,, 1975), 

The second part of the questionnaire used Likertscale 

measures of expectations, concerns, and 

perceived changes that may be related to the 

use of the computer system. These measures 

were developed by analyzing the interviews and 

observations to derive categories for questions 

that focused on the primary expectations expressed 

by interviewees, attendees at meetings, 

and the laboratory technologists who were observed. 

Additional questions concerning expectations 

were adapted from Kjerulff, et al, (1982), 

The survey instrument concluded with four openended 

questions that assessed changes caused 

by the computer system and elicited suggestions 

for improved system use. These questions were 

also derived from the observations and interviews. 

They were intended to serve two purposes. 

The first was to ensure that important 

issues were addressed even if they had been 

included in scaled-response questions. The 

second was to elicit information about impacts 

for which measures were difficult to develop. 

All questionnaire items were pretested on a 

sample of laboratory personnel selected by head 

supervisors. After revision, the questionnaire was 

administered to all 248 members of the laboratory 

staff seven months after computer system 

installation. The staff knew about the study because 

of the prior laboratory observations and 

announcements at meetings. Each survey was 

accompanied by an explanatory letter, A research 

team member also explained the study 

during weekly staff meetings when the questionnaires 

were distributed. In some laboratories, this 

meeting was devoted to answering the questionnaire. 

In others, staff members were allowed to 

complete the survey during work hours provided 

that it not interfere with their job functions. 

A modified version of the questionnaire was 

distributed to all laboratory technologists for the 

second wave of data collection beginning nearly 

a year after the first. No further surveys were 

conducted. 

Sample 

Just before the system was installed, 11 interviews 

were conducted with 20 interviewees representing 

all the laboratories. These interviewees 

included laboratory directors at all levels. 

head supervisors, and an administrator of one 

unit of the hospital. 

All 248 members of the laboratory staff were surveyed 

starting seven months after system implementation. 

Data from all 119 completed 

(48%) questionnaires were analyzed, Qnly 

seven of the respondents had been interviewed. 

Most respondents were technologists who had 

college degrees, worked first shift, and had not 

worked previously in a laboratory with a computer 

information system. As is typical of laboratory 

technologists, almost all were women. 

Analysis and Results 

Data are presented from interviews immediately 

prior to installation and from the first wave of 

survey questionnaires seven months later. The 

quantitative data were analyzed using a standard 

statistical software package. Interview notes 

and responses to open-ended questions on the 

questionnaire were analyzed by the constant comparative 

method (Glazer and Strauss, 1967), 

Using this method, categories reflecting computer 

system issues important to laboratory directors 

and technologists were derived 

systematically. 

Open-ended questions 

Kaplan first analyzed open-ended questions on 

the questionnaire. Three themes predominated 

in the answers: (1) changes in technologists' 

work load, (2) improvements in results reporting, 

and (3) the need for physicians and nurses 

to use computer system terminals rather than 

telephones for results inquiry. Technologists expressed 

a general sense that their clerical duties 

and paperwork had increased and productivity 

had suffered. However, they credited the computer 

system with making laboratory test results 

available more quickly. They said that results reports 

were also more complete, more accurate, 

easier to read, and provided a picture of "the 

whole patient," Even though phone calls interrupted 

laboratory work, they felt that doctors and 

nurses expected to get test results by calling the 

laboratories rather than by using the computer 

system. In addition, respondents sensed they 

were being blamed by others in the medical 

center for problems caused by the computer 

system. 

When responses were grouped by laboratory, 

marked differences were evident among some 



Of the laboratories. Laboratories differed in their 

assessment of changes in their workload, 

number of telephone calls, improvement in reporting, 

and attitudes expressed toward the computer 

system. 

Scaled-response questions 

Responses to the questionnaire items pertaining 

to job characteristics and to the scaledresponse 

items assessing computer expectations 

and concerns were analyzed next by two 

researchers who intentionally remained unaware 

of the findings on open-ended questions. Having 

already analyzed the responses to open-ended 

questions, Kaplan assisted them in an initial Q- 

son of computer system questionnaire items and 

later interpretation of a factor analysis of these 

items. A fourth researcher had left the study 

team. Another of the original team members left 

the study during data analysis, leaving Duchon 

and Kaplan to interpret the results of Duchon's 

statistical analysis, 

A factor analysis of job items provided evidence 

of construct validity for the measures. Four factors 

were extracted: skill variety, task identity, 

autonomy, and feedback. These factors comprise 

a four-factor model of the core job dimensions 

(Birnbaum, et al,, 1986; Ferris and 

Gilmore, 1985), and are also used to assess job 

complexity (Stone, 1974), Qverall, no job characteristic 

dififerences due to environmental or individual 

factors were found. Respondents reported 

the same levels of job characteristics 

regardless of age, gender, job experience, etc. 

Five computer system variables were extracted: 

external communications, service outcomes, personal 

intentions, personal hassles, and increased 

blame. Reliability for these five factors 

ranged between ,53 and ,87, Data on these variables 

indicated that respondents generally were 

positive about the computer system. They reported 

that the system had improved relations 

and aided communication between staff in the 

laboratories and the rest of the medical center 

(external communications), and that results reporting 

and service was better (service 

outcomes). 

Respondents were very positive about their own 

continued use of the computer system (personal 

intentions). They did not think that irritants of 

their jobs, such as the number of phone calls 

and the amount of work (personal hassles), had 

increased, or that laboratory staff was blamed 

more and cooperated with less by physicians 

and nurses (increased blame). There were no 

statistically significant correlations between job 

characteristic measures and computer system 

measures. 

At this point, nothing reportable had been found 

in the quantitative data. This was surprising because 

computer system variables were similar 

to themes identified in responses to the openended 

questions, where some laboratories differed 

markedly in their responses. Consequently, 

Kaplan continued to seek explanations for the 

differences among laboratories that were evident 

in the qualitative data. 

Interviews 

Next, Kaplan again analyzed the interviews with 

laboratory directors, this time to determine expectations 

of the directors prior to system implementation. 

She thought that perhaps different 

expectations among directors could have contributed 

to different responses within the laboratories. 

Different expectations were not found. The 

analysis indicated that prior to implementation, 

directors generally agreed that there would be 

more work for laboratory technologists, but that 

technologists' jobs would not be changed by the 

computer system. 

This assessment was made with full knowledge 

that technologists would have to enter test results 

and some test orders, and that their papenwork 

and billing duties were expected to decrease. 

Interviewees also expected that test 

results would be available more quickly. They 

thought that reports would have fewer transcription 

errors, would be more legible, and would 

provide useful cumulative information and patient 

profiles. They expected these improvements 

to reduce the number of unnecessary laboratory 

tests and stat (i,e., emergency) and 

duplicate test orders, and anticipated that the 

number of telephone inquiries concerning results 

would decrease. 

Developing an Interpretative 

Theoretical Model 

The five computer system variables that were 

developed from the questionnaire items reflected 

themes very similar to those found in the qualitative 

data from interviews and responses to 

MiS Quarterly/December 1988 579


open-ended questionnaires: changes in workload 

and changes in results reporting. This congruence 

of themes from independent sources 

of data strengthened confidence in their validity. 

The analysis of qualitative data from the questionnaires 

suggested important differences 

among laboratories, even in the absence of statistical 

verification, because the differences were 

so striking. Knowledge obtained from observations 

in the laboratories and at meetings, as well 

as from comments made to her by individual laboratory 

technologists and directors, strengthened 

Kaplan's conviction that these differences were 

important. It remained to determine the nature 

of the differences and to explain the lack of reportable 

statistical results. 

An impression gained from the qualitative data 

suggested an interpretation, Kaplan had noticed 

that laboratory technologists seemed to emphasize 

either the increased work or the improved 

results reporting in their answers to the openended 

questions. The repetition of these themes 

in all the data sources reinforced their importance. 

This repetition suggested that there were 

two groups of respondents corresponding to 

these two themes. The finding that directors did 

not expect technologists' jobs to change raised 

the question of what constituted a technologist's 

job. It seemed that directors and technologists 

might have different conceptions of the technologist's 

job and that these differences were reflected 

in their assessment of the computer 

system. Individual laboratory technologists might 

also differ in their views of their jobs, just as 

they differed in what they emphasized about the 

computer system in their comments on the 

questionnaires. 

These insights led to a model depicting two 

groups of laboratory technologists. According to 

this model, one group saw their jobs in terms 

of producing results reports, the other in terms 

of the laboratory bench work necessary to produce 

those results reports. As shown in Figure 

1, the group who saw its jobs in terms of bench 

work was oriented toward the process of producing 

laboratory results, whereas the group 

who viewed its work in terms of reporting results 

was oriented toward the outcomes of laboratory 

work; the members of this group saw themselves 

as providing a service. 

The ways in which the computer affected the 

production work in the laboratories were assessed 

in those questionnaire items comprising 

personal hassles and increased blame, e,g,, effects 

on paper work and telephone calls. Thus, 

the group who saw its jobs in terms of the bench 

work (i.e,, the process of producing laboratory 

tests results) would have responded to the computer 

system according to how it had affected 

what was assessed in these items. The productoriented 

group of respondents who saw its jobs 

in terms of the service provided (i,e,, the product, 

rather than the process, of laboratory work) 

would have assessed the computer system in 

Orientation 

Process 

Product 

View of Job 

Responses to 

Computer System 

Computer System 

Variables 

Bench Work 

Increased 

Work Load 

Hassles 

Blame 

Results, Service 

Improved Results 

Reporting 

Communications 

Service 

Orientation = (Communications + Service) 

- (Hassles + Blame) 

Figure 1. A Modei Depicting Different Orientations to Work and the 

Computer in Clinicai Laboratories 



terms reflected in its responses to external communications 

and service outcomes items, e,g,, 

improved results reporting. 

This interpretation indicated a reason why job 

characteristics measures did not depict differences 

among laboratories and why there was 

no correlation between job characteristics and 

computer system variables. The kinds of differences 

in job orientation depicted in the model 

would not have been measured by job characteristic 

measures. 

After this model was proposed, two new variables 

were created to measure whether technologists' 

responses differed according to the 

computer system's impact on process versus 

product aspects of their jobs. As shown in Figure 

1, one variable combined scores on external communication 

and service outcomes, and the other 

combined scores on personal hassles and increased 

blame. The variable personal intentions 

was omitted because there was no theoretical 

basis for including it; personal intentions did not 

assess the interaction between specific aspects 

of the computer system and the job. Moreover, 

this variable was not a good discriminator between 

laboratories or individuals because individual 

respondents' scores were all high. 

There was a significant negative correlation between 

these two new variables, thus indicating 

that respondents tended to have high scores on 

one variable and low scores on the other, i.e. 

they were either product- or process-oriented. 

An orientation score for each respondent was 

then computed by subtracting the sum of that 

person's scores on personal hassles and increased 

blame from his or her scores on external 

communication and service outcomes. 

When the orientation score was regressed on 

laboratories, statistically significant differences in 

orientation were found across laboratories. Thus, 

some laboratories, like some technologists, were 

process-oriented while others were productoriented. 

Moreover, respondents from the laboratories 

rating the strongest process orientation 

expressed the most hostility on the open-ended 

questions from the survey, whereas respondents 

from laboratories with the strongest product orientation 

expressed strong satisfaction with the 

computer system. 

Thus, our results suggested that the interpretation 

was correct. Discussion with the laboratory 

director about the intermediate and final results 

and about the research papers produced from 

the study supported the interpretation. 

Discussion 

This study illustrates the difficulties as well as 

the strengths that occurred when research perspectives 

from different disciplines were combined. 

Although both authors agree that this collaboration 

enriched the study as well as their 

own understanding of research, it was rife with 

frustrations. Neither author initially realized the 

extent to which differing values, assumptions, 

and vocabularies would interfere with the project. 

Continued interaction was necessary to recognize 

that there were differences even in understanding 

the same words. Persistent effort 

was needed to identify and resolve these differences. 

A key incident in the study stemmed from 

these differences. Because the incident is illustrative 

of the difficulties as well as the enrichment 

caused by the different perspectives of research 

team members, we recount it here. 

After the initial statistical analysis of data from 

the scaled response questions, Duchon thought 

that there were no statistical results worth reporting, 

Kaplan thought he meant that there were 

no statistically significant differences among laboratories 

in their reaction to the computer system. 

However, she did not agree with these results 

because they did not fit with her observations 

and analysis, Duchon remained convinced that 

there were no results worth pursuing. Consequently, 

Kaplan began to analyze the remaining 

data from the interviews. This analysis strengthened 

her convictions that the qualitative data did 

indicate patterns worth investigating and that 

Duchon had to be convinced of this. That determination 

led to the development of the interpretive 

theoretical model. 

The turning point in the study, and in the authors' 

collaboration, happened when Duchon reanalyzed 

the quantitative data as suggested by 

Kaplan, This new analysis supported her interpretation. 

When the initial analysis was repeated, 

there were statistically significant differences 

among laboratories for the computer system variables. 

We were not able to determine the reason 

for the apparent lack of reportable differences 

during the first statistical analysis. Because of 

continued difficulties in communication, misunderstandings, 

in retrospect, were not surprising. 

Some of the difficulties we experienced have 

been described so that others who participate 



on similar research teams may shorten the learning 

period and reduce the communication problems. 

It should be clear from this account that, 

as is common in research that includes qualitative 

approaches, the process is messy. Impressions, 

interpretations, propositions, and hypotheses 

were developed over the course of the 

study, a process that hardly fits the positivist 

ideal of objective collection of neutral or purely 

descriptive "facts" (Van Maanen, 1983b), This 

messiness should not dismay those who experience 

it. 

Despite the methodological and communications 

problems, the collaboration was productive and 

friendly. Our tenacity in holding to our initial independent 

analyses of the different data — 

though a problem at the time — and the increased 

respect each of us developed for the 

other's approach, in fact, were positive aspects 

of our research. As has happened in other projects 

(Trend, 1979), a strong determination to 

accommodate each approach and to reconcile 

apparently conflicting data resulted in an interpretation 

that synthesized the evidence. We cannot 

state too strongly that the advantages of our 

collaboration outweighed the difficulties, 

Conciusions 

This article describes how qualitative and quantitative 

approaches were combined in a case 

study of a new information system. It illustrates 

four methodological points: (1) the value of combining 

qualitative and quantitative methods; (2) 

the need for context-specific measures of job 

characteristics; (3) the importance of process 

measures when evaluating information systems; 

and (4) the need to explore the necessary relationships 

between a computer system and the 

perceptions of its users. 

Combining qualitative and quantitative methods 

proved especially valuable. First, the apparent 

inconsistency of results between the initial quantitative 

analysis and the qualitative data required 

exploration. The initial statistical analysis revealed 

only that certain well-recognized job characteristic 

measures were not capturing the differences 

in the sample and that the correlations 

between job characteristics and computer 

system variables were not significant. However, 

systematically different responses to the computer 

system were present in the qualitative 

data. Further analysis to resolve the discrepancy 

led to the use of new measures developed from 

the quantitative questionnaire data that captured 

job-related responses to the computer system 

and that supported conclusions drawn from the 

qualitative data. 

Thus, triangulation of data from different sources 

can alert researchers to potential analytical 

errors and omissions. Mixing methods can also 

lead to new insights and modes of analysis that 

are unlikely to occur if one method is used alone, 

in the absence of qualitative data, the study 

would have concluded that there were no reportable 

statistically significant findings. However, 

on the strength of the qualitative data, a 

theoretical interpretative model was developed 

to drive further statistical analysis. The inclusion 

of qualitative data resulted in the generation of 

grounded theory. 

The results also suggest the value of investigating 

specifically how a computer system affects 

users' jobs. In this study, as in others, no correlation 

was found between standard job characteristic 

measures and variables measuring responses 

to the computer system. Nevertheless, 

these responses were related to differences in 

job orientation. Studies that independently 

assess characteristics and attitudes ignore the 

specific impacts of a computer system on work. 

Standard job characteristic measures do not necessarily 

assess job-specific factors or capture the 

different job orientations held by workers with 

"the same job," Instead of sole reliance on these 

measures, instruments need to include contextspecific 

measures of how a computer system 

supports work, as conceptualized by the worker. 

In this study, measures assessing these aspects 

were derived from knowledge of the setting 

gained by field experience, again suggesting the 

value of combining qualitative and quantitative 

methods. 

The study further suggests the need to move 

beyond outcome evaluations to investigating 

how a computer system affects processes. By 

extending research to examine specific efforts 

of individual computer information systems in particular 

contexts, our general understanding of 

what affects the acceptance and use of computer 

information systems can be improved. 

Lastly, there is a growing amount of literature 

that assesses the impacts of computer information 

systems. This study was intended, by part 

of the research team, as an assessment of the 

impacts of a computer information system on 

work. However, impact studies consider only one 



side of the important interaction between computer 

information system users and the computer 

information system: the effects of the system on 

users, the organization, or society. Research designs 

could just as well consider the impacts of 

users, the organization, or society on the computer 

information system. Sorting out the direction 

of causality is a difficult, if not meaningless, 

task that is made all the more difficult by the 

multitudes of confounding factors common to 

any field setting. It also may be impossible to 

do because the design does not account for 

changes due to the interrelationships between 

the "dependent" and "independent" variables. 

For example, in this study, the job orientation 

of the users could have mediated their response 

to the computer information system. Alternatively, 

the system itself could have caused the 

differences in job orientation. Rather than formulate 

the analysis in either of these ways, it 

seemed more fruitful to take an interactionist approach 

and consider the interrelationships between 

users' job orientations and their responses 

to the system, "Impacts" implies unidirectional 

causality, whereas, as has been found in other 

studies, investigations of interactions can be 

more productive and meaningful. 

Despite the normative nature of the 

methodological points, the most important conclusion 

is the need for a variety of approaches 

to the study of information systems. For the 

same reasons that combining methods can be 

valuable in a particular study, a variety of approaches 

and perspectives can be valuable in 

the discipline as a whole. No one method can 

provide the richness that information systems, 

as a discipline, needs for further advance. 

Acknowledgements 

Joseph A, iVIaxwell of the Harvard Graduate 

School of Education provided invaluable theoretical 

assistance and editorial advice. We are also 

grateful to the anonymous reviewers whose comments 

resulted in furtherclarifying and strengthening 

the paper. 

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About the Authors 

Bonnie Kaplan is an assistant professor of information 

systems at the University of Gincinnati's 

College of Business Administration. She 

is also an adjunct assistant professor of clinical 

pathology and laboratory medicine. Dr. Kaplan 

received her Ph.D. in history from the University 

of Chicago. She has had extensive practical experience 

in information systems development at 

major academic medical centers, Dr, Kaplan's 

research interests include behavioral and policy 

issues in information systems, implementation 

of technological innovations, information systems 

in medicine and health care, and history and sociology 

of computing. Her publications have appeared 

in Journai of Medical Systems, Internationai 

Journal of Technology Assessment in 

Health Care, Journal of Health and Human Resources 

Administration, Journal of Clinical Engineering, 

and volumes on human-computer interaction 

and on medical computing, 

Dennis Duchon is an assistant professor of organizational 

behavior in the Gollege of Business 

at the University of Texas at San Antonio, Before 

joining the faculty there, he was an assistant professor 

of organizational behavior at the University 

of Gincinnati. He received a Ph.D. in organizational 

behavior from the University of Houston. 

He has worked as a manager both in the United 

States and abroad. Dr. Duchon's research interests 

include behavioral decision making and the 

implementation of new technologies. He has published 

in the Journai of Appiied Psychoiogy, 

IEEE Transactions in Engineering Management, 

and Decision Sciences. 

586 MiS Quarterly/December 1988

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