Eppler Information Overload[1]

The Information Society, 20: 325–344, 2004Copyright c© Taylor & Francis Inc.ISSN: 0197-2243 print / 1087-6537 onlineDOI: 10.1080/01972240490507974

The Concept of Information Overload: A Reviewof Literature from Organization Science, Accounting,Marketing, MIS, and Related Disciplines

Martin J. Eppler and Jeanne MengisInstitute of Corporate Communication, University of Lugano, Lugano, Switzerland

Based on literature from the domains of organization science,marketing, accounting, and management information systems, thisreview article examines the theoretical basis of the informationoverload discourse and presents an overview of the main defini-tions, situations, causes, effects, and countermeasures. It analyzesthe contributions from the last 30 years to consolidate the existingresearch in a conceptual framework and to identify future researchdirections.

Keywords information explosion, information management strate-gies, information overload, information processing,information skills, information technology

In this article, we present a review of the literature on in-formation overload in management-related academic pub-lications. The main elements of our approach are literaturesynopsis, analysis, and discussion (Webster & Watson,2002). These three elements serve, in our view, the threemain purposes of a literature review, namely, to provide anoverview of a discourse domain (e.g., compiling the mainterms, elements, constructs, approaches and authors), toanalyze and compare the various contributions (as well astheir impact), and to highlight current research deficits andfuture research directions. These three objectives shouldbe met, with regard to the topic of information overload,as a clear overview, an analysis of the major contribu-tions, and an identification of future research needs stillmissing for this topic. The literature review should also

Received 27 October 2002; accepted 20 May 2004.We thank the four anonymous reviewers and the two editors for their

insightful suggestions.Address correspondence to Martin J. Eppler, Institute of Corporate

Communication, University of Lugano, Via G. Buffi, 13, 6900 Lugano,Switzerland. E-mail: [email protected]

help readers (researchers and managers alike) to recognizeinformation overload symptoms, causes, and possiblecountermeasures in their own work environment, as theflood of potentially relevant information has become aubiquitous research and business problem, from readingrelevant articles or reports, to screening e-mails or brows-ing the Internet.

While this is not the first review article on the topicof information overload (see Edmunds & Morris, 2000),it is the first one to analyze the problem of informationoverload across various management disciplines, such asorganization science, accounting, marketing, and manage-ment information systems (MIS). Other review articles onthe subject follow a discipline-based approach. Malhotraet al. (1982) and more recently Owen (1992) focus on con-sumer research (see also Meyer, 1998); Schick et al. (1990)examine relevant accounting literature; and Edmunds andMorris (2000), Grise and Gallupe (1999/2000), and Nelson(2001) concentrate on MIS research. Our review of con-tributions in the area of information overload is interdis-ciplinary because it aims to identify similarities and dif-ferences among the various management perspectives andshow to what extent they have discussed information over-load. We hope that by doing so, we can identify syner-gies between the different streams of information over-load research and highlight future research areas. Anotherbenefit of an interdisciplinary literature review is that itcan provide a more (cross-)-validated and general col-lection of possible symptoms, causes, and countermea-sures and thus lead to a more complete understanding ofthe phenomenon. This literature-based understanding canthen be used to construct testable models on informationoverload.

A second difference of our review in relation to priorcontributions is the way that the literature is summarizedand analyzed, as we present the results of our reviewin a highly compressed and often visual format. By ourproviding various diagrammatic overviews of the reviewed

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326 M. J. EPPLER ET AL.

literature, patterns in the development of the field can be-come visible. The major benefit of this visual approachis a more concise representation of the discourse domain,which allows for easier comparisons and hopefully alsoleads to a reduction of information overload for ourreaders.

THE CONCEPT OF INFORMATION OVERLOAD

In ordinary language, the term “information overload”is often used to convey the simple notion of receivingtoo much information. Within the research community,this everyday use of the term has led to various con-structs, synonyms, and related terms, such as cognitiveoverload (Vollmann, 1991), sensory overload (Libowski,1975), communication overload (Meier, 1963), knowledgeoverload (Hunt & Newman, 1997), and information fa-tigue syndrome (Wurman, 2001). These constructs havebeen applied to a variety of situations, ranging from au-diting (Simnet, 1996), to strategizing (Sparrow, 1999),business consulting (Hansen & Haas, 2001), managementmeetings (Grise & Gallupe, 1999/2000), and supermarketshopping (Jacoby et al., 1974; Friedmann, 1977), to namebut a few overload contexts (for an extended list ofthe contexts in which information overload has been dis-cussed in management-related academic literature seeTable 1).

Research on information overload relevant for the realmof management has mainly been undertaken in the areasof accounting (e.g., Schick et al., 1990), management in-formation systems (MIS) (initially highlighted by Ackoff,1967), organization science (e.g., Galbraith, 1974;Tushman & Nadler, 1978), and marketing and more spe-cially consumer research (e.g., Jacoby, 1984; Keller &Staelin; 1987; Malhotra, 1984). The main focus of thesedisciplines is how the performance (in terms of adequatedecision making) of an individual varies with the amountof information he or she is exposed to. Researchers acrossvarious disciplines have found that the performance (i.e.,the quality of decisions or reasoning in general) of an indi-vidual correlates positively with the amount of informationhe or she receives—up to a certain point. If further infor-mation is provided beyond this point, the performance ofthe individual will rapidly decline (Chewning & Harrell,1990). The information provided beyond this point will nolonger be integrated into the decision-making process andinformation overload will be the result (O’Reilly, 1980).The burden of a heavy information load will confuse the in-dividual, affect his or her ability to set priorities, and makeprior information harder to recall (Schick et al., 1990).Figure 1 provides a schematic version of this discovery. Itis generally referred to as the inverted U-curve, followingthe initial work of Schroder Driver, and Streufert (Schroderet al., 1967).

FIG. 1. Information overload as the inverted U-curve.

This inverted U-curve represents the first important def-inition of information overload, which was strongly de-bated in the following (see Malhotra et al., 1982; Russo,1974; or McKinnon & Bruns, 1992). For an overview of thevarious ways researchers have marked the point at whichinformation overload occurs, see Table 2.

Authors in the field of marketing define informationoverload by comparing the volume of information supply(e.g., the number of available brands) with the information-processing capacity of an individual. Information over-load occurs when the supply exceeds the capacity. Dys-functional consequences (such as stress or anxiety) anda diminished decision quality are the result. A similarway of conceiving the information overload phenomenoncompares the individual’s information-processing capac-ity (i.e., the quantity of information one can integrate intothe decision-making process within a specific time pe-riod) with the information-processing requirements (i.e.,the amount of information one has to integrate in orderto complete a task). This is the “classic” definition of in-formation overload, based on the information-processingview of the organization suggested by Galbraith (1974) andexpanded by Tushman and Nadler (1978). Following theirreasoning, information overload can be explained via thefollowing formula: information processing requirements> information processing capacities. The terms “require-ments” and “capacities” in this definition can be measuredin terms of available time. The requirements refer to a givenamount of information that has to be processed within acertain time period. If the capacity of an individual onlyallows a smaller amount of information to be processedin the available time slot, then information overload isthe consequence. Tushman and Nadler define informa-tion processing in this context as the “gathering, inter-preting, and synthesis of information in the context of or-ganizational decision making” (Tushman & Nadler, 1978,p. 614). Many variations of this definition exist. Schicket al. (1990) also stress the time factor as the mostimportant issue regarding the information overload prob-lem. Interestingly, this discussion includes the Schroder

THE CONCEPT OF INFORMATION OVERLOAD: REVIEW 327

TABLE 1Information overload situations

Context/overload situation References

Information retrieval, organization, • Searching on the Internet Berghel, 1997and analysis processes • Screening medical information Bawden, 2001

• Financial distress analysis Chewning and Harrell, 1990• Evaluating the variety of product

functionsHerbig and Kramer, 1994

• Analysis activities (strategicportfolio, environmental, newproduct analysis, service decisions)

Meyer, 1998

• Investment analysis Tuttle and Burton, 1999• Library management Meier, 1963

Decision processes • Managerial decisions in general Ackoff, 1967; Iselin, 1993• Management (project, strategic,

production management)Chervany and Dickson, 1974; Haksever and

Fisher, 1996; Meyer, 1998; Sparrow, 1999• Supermarkets (choice of product) Friedmann, 1977; Jacoby et al., 1974• Bankruptcy prediction process Casey, 1980; Iselin, 1993• Capital budgeting process Swain and Haka, 2000• Welfare assistance (decisions about

type and amount)O’Reilly, 1980

• Innovation choice Herbig and Kramer, 1994• Price setting Meyer, 1998• Advertising media selection Meyer, 1998• Strategy development Sparrow, 1999• Physician’s decision making Hunt and Newman, 1997• Financial decision making Iselin, 1988; Revsine, 1970• Brand choice (consumer decision

making)Jacoby et al., 1974, 1987; Malhotra, 1982;

Owen, 1992; Scammon, 1977; Wilkie, 1974• Aviation O’Reilly, 1980

Communication processes • Meetings Schick et al., 1990• Telephone conversations Schick et al., 1990• The use of groupware applications Schultze and Vandenbosch 1998• Bulletin board systems (BBS) Hiltz and Turoff, 1985• Face-to-face discussions Sparrow, 1999• Telephone-company services Griffeth et al., 1988• Electronic meetings Grise and Gallupe, 1999, 2000• Idea organization Grise and Gallupe, 1999, 2000• E-mail Bawden, 2001; Speier et al., 1999; Denning,

1982• Management consulting Hansen and Haas, 2001• City interactions Milgram, 1970• Disclosure law, contract complexity,

legal disclaimersGrether et al., 1986

et al. (1967) view that information load and processingcapacity are not independent, since the former can influ-ence the latter—that is, high information load can increaseone’s processing capacity up to a certain point (see alsoSchultze & Vandenbosch, 1998). In other studies (Iselin,1993; Keller & Staelin, 1987; Owen, 1992; Schneider,

1987), not only the amount of information and the avail-able processing time (i.e., the quantitative dimension),but also the characteristics of information (i.e., the qual-itative dimension) are seen as major overload elements.Keller and Staelin refer to the overall quality or “useful-ness of the available . . . information” (1987, p. 202), while


TABLE 2Definitions of information overload

Definitions Components/dimensions References

The decision maker is considered to haveexperienced information overload at the pointwhere the amount of information actuallyintegrated into the decision begins to decline.Beyond this point, the individual’s decisionsreflect a lesser utilization of the availableinformation.

• Inverted U-curve: relationship betweenamount of information provided andamount of information integrated bydecision maker

• Information utilization

Chewning and Harrell (1990),Cook (1993), Griffeth et al.(1988), Schroder et al.(1967),Swain and Haka (2000)

Information overload occurs when the volume ofthe information supply exceeds the limitedhuman information processing capacity.Dysfunctional effects such as stress andconfusion are the result.

• Volume of information supply(information items versus - chunks)

• Information processing capacity• Dysfunctional consequences

Jacoby et al. (1974),Malhotra (1982),Meyer (1998)

Information overload occurs when theinformation-processing requirements(information needed to complete a task) exceedthe information-processing capacity (thequantity of information one can integrate into thedecision-making process).

• Information-processing capacity• Information-processing requirements

Galbraith (1974),Tushman and Nadler (1978)

Information overload occurs when theinformation-processing demands on time toperform interactions and internal calculationsexceed the supply or capacity of time availablefor such processing.

• Time demands of information processing;available time versus invested time

• Number of interactions (withsubordinates, colleagues, superiors)

• Internal calculations (i.e., thinking time)

Schick, et al. (1990),Tuttle and Burton (1999)

Information overload occurs when theinformation-processing requirements exceed theinformation-processing capacity. Not only is theamount of information (quantitative aspect) thathas to be integrated crucial but also thecharacteristics (qualitative aspect) ofinformation.

• Information-processing requirements• Information-processing capacity• Quantitative and qualitative dimensions of

information (multidimensional approach)

Keller and Staelin (1987),Schneider (1987),Owen (1992), Iselin (1993)

Information overload occurs when the decisionmaker estimates he or she has to handle moreinformation than he or she can efficiently use.

• Subjective component: opinion, job andcommunication satisfaction

• Situational factors and personal factors

Abdel-Khalik (1973), Iselin(1993), O’Reilly (1980),Haksever and Fisher (1996)

Amount of reading matter ingested exceedsamount of energy available for digestion; thesurplus accumulates and is converted by stressand overstimulation into the unhealthy stateknown as information overload anxiety.

• Subjective cause component: energy• Symptom: stress, overstimulation• Subjective effect: information overload

anxiety

Wurman (1990), Wurman(2001), Shenk (1997)

Schneider (1987) distinguishes various information attri-butes, such as the level of novelty, ambiguity, uncertainty,intensity, or complexity. These information characteristicsor quality attributes can either contribute to overload orreduce it.

Beyond these approaches that try to conceptualize andmeasure the phenomenon of overload objectively, thereare others that conceive overload on the basis of sub-jective experience. Authors who have followed this ap-

proach are O’Reilly (1980), Haksever and Fisher (1996),and Lesca and Lesca (1995). In this “subjective” viewof overload, the feelings of stress, confusion, pressure,anxiety, and low motivation are the crucial factors thatsignal the occurrence of information overload. Empiri-cal research that follows this subjective view of the over-load phenomenon typically employs interviews or surveymethods (such as Haksever & Fisher, 1996) as opposed toexperiments.


This brief overview of the most frequently used defini-tions and the contexts within which they were developeddelineates the intellectual territory which is examined inthis literature review. Having described the background ofthe overload concept, we next briefly outline the method-ology used to analyze the relevant literature.

METHODOLOGY

To screen the relevant articles within the literature on infor-mation overload, we used the electronic database providedby EBSCOhost and limited our research to the articlesincluded by the Business Premier Source. This databaseprovides full-text access to 3000 journals, of which morethan 1000 are peer reviewed. EBSCOhost enabled us tosearch in the title or abstract of an article with the fol-lowing keywords: information overload, information load,cognitive overload, and cognitive load, which resulted ina total number of 548 retrieved articles. In order to reducethis large number to a more relevant subset, we introducedfurther criteria (see Figure 2), which were: first, a publi-cation date after 1970 (when computers started to be usedmore extensively in the workplace); second, that the ar-ticle is peer reviewed (which resulted in 205 remainingarticles); third, that information overload is a dominantand systematically addressed subject in the article and notjust mentioned once or twice (resulting in a total of 168articles); and finally, that the article approaches the sub-ject within the context of one of the four areas of interest,namely, accounting, marketing, MIS, and organization sci-ence (with regard to the article’s topics and its publicationjournal). This selection procedure has led to a total num-

FIG. 2. Selection criteria and article base.

ber of 97 considered articles. The 71 articles that wereeliminated discussed information overload in very specificcontexts that are quite different from those of today’s orga-nizations. They discussed information overload in contextssuch as library and bibliographic research, documentationof large-scale engineering designs, and students conduct-ing research for their essays). Figure 2 reveals that thegreatest number of retained articles comes from the mar-keting domain, followed by articles within organizationscience, then accounting, and finally MIS had the smallestnumber of articles on the subject.

One limitation of this methodology is that some arti-cles that have dealt with the issue, but have used labelsother than the four terms we used as keywords, are nottaken into consideration (i.e., labels such as data smog,information fatigue/overkill/overabundance/breakdown/explosion/deluge/flood/stress/plethora, document tsun-ami, sensory overload, etc.; see Eppler, 1998, for theseand other labels). These different terms, however, havenot achieved wide acceptance within the scientific com-munity and hence do not represent core contributions tothis scientific debate. Another limitation of our approachis that contributions on information overload that discussthe phenomenon from other perspectives (such as psy-chology, health care, and mass communication) are notextensively addressed. Examples of such important contri-butions include Miller’s “The magical number seven plusor minus two” (Miller, 1956) and Simon’s seminal “Infor-mation processing models of cognition” (Simon, 1979), toname but two crucial contributions. In order to moderatethis limitation, we used an additional inclusion criterion:If a publication was cited in more than two other overloadarticles, we screened it to see whether information over-load was indeed a major topic of the publication, and ifthat was the case, we included it in our analysis. We pro-ceeded likewise for books that have been cited frequentlyin relevant journal articles (such as Wurman, 1990, 2001;Shenk, 1997).

A CONCEPTUAL FRAMEWORK FORINFORMATION OVERLOAD RESEARCH

In order to provide a more complete (and less fragmented)picture of the research conducted on information over-load, the following framework lays out the most impor-tant topic clusters in the literature and underscores theirrelationships. These topic clusters are the main causesof information overload, the symptoms, and appropriatecountermeasures for mitigating the problem.

It is important to note that the framework depictedin Figure 3 is not based on a linear logic of causes and ef-fects, but instead emphasizes a system of circular,interdependent relationships. Correspondingly, it stressesthe fact that any countermeasure that is aimed at a specific


FIG. 3. A conceptual framework to structure research on information overload.

overload cause can have significant side effects on othercauses. Although this fact is frequently acknowledged inliterature (e.g., Bawden, 2001), it has scarcely been ex-plored empirically (for an exception, see Evaristo, 1993).In particular, the effect of certain (new) information tech-nology applications on the quality of information (seeWang et al., 1998), on the motivation of the individual,and on task parameters has been neglected. Also, a con-certed effort needs to be made to employ research meth-ods that can capture contextual factors (such as industrycharacteristics, the firm’s development stage, and the staffstructure) that are of critical importance for the occurrenceof overload. In general, research that provides “deep con-text” is missing, as most information overload research isexperimental, survey based, or purely conceptual.

This framework also highlights the fact that there can-not be a definitive solution for information overload. Therewill always be a need for a continuous cycle of improve-ment and refinement. We discuss the main elements (thecauses, symptoms and countermeasures) of the frameworkand the relevant literature in the subsequent sections. At theend of this section, we also demonstrate how this concep-tual framework can be converted into empirically testablemodels.

Causes of Information Overload

The main reasons for information overload at organiza-tional and interpersonal levels can be related to five con-structs, as shown in Figure 3. These inductively generatedconstructs are the information itself (its quantity, frequ-ency, intensity, and quality), the person receiving, pro-

cessing, or communicating information, the tasks or pro-cesses that need to be completed by a person, team, ororganization, the organizational design (i.e., the formaland informal work structures), and the information tech-nology that is used (and how it is used) in a company.Usually information overload emerges not because of oneof these factors but because of a mix of all five causes.All five causes influence the two fundamental variables ofinformation overload: the information processing capac-ity (IPC)—which is for example influenced by personalcharacteristics—and the information processing require-ments (IPR)—which are often determined by the nature ofthe task or process. We discuss these five causes and theirinfluence on IPC and IPR briefly in the next paragraphs.

An important factor influencing the occurrence ofinformation overload is the organizational design ofa company (Galbraith, 1974; Tushman & Nadler, 1978).Changes in the organizational design, for instance, due todisintermediation or centralization (Schneider, 1987) orbecause of a move to interdisciplinary teams (Bawden,2001), can lead to greater IPRs because they create theneed for more intensive communication and coordination.On the other hand, better coordination through standards,common procedures, rules, or dedicated coordination cen-ters (Galbraith, 1974) can reduce the IPR and positivelyinfluence the IPC (Galbraith, 1974; Schick et al., 1990;Tushman & Nadler, 1978; for other organizationaldesign elements that influence information overload seeSchneider, 1987).

After organizational design, the next important factor isthe nature of information itself. Schneider (1987) stressesthe fact that it is not only the amount of information that


determines information overload, but also the specificcharacteristics of information (also see Sparrow, 1998).Such characteristics are the level of uncertainty associ-ated with information and the level of ambiguity, novelty,complexity, and intensity (Schneider, 1987). Simpson andPrusak (1995) argue that modifying the quality of infor-mation can have great effects on the likelihood of infor-mation overload. Improving the quality (e.g., conciseness,consistency, comprehensibility, etc.) of information canimprove the information-processing capacity of the indi-vidual, as he or she is able to use high-quality informa-tion more quickly and better than ill-structured, unclearinformation.

The person and his or her attitude, qualification, and ex-perience are another important factor. While earlier studiessimply state that a person’s capacity to process informationis limited (Jacoby et al., 1974; Galbraith, 1974; Malhotra,1982; Simon, 1979; Tushman & Nadler, 1978), more re-cent studies include specific limiting factors such as per-sonal skills (Owen, 1992), the level of experience (Swain& Haka, 2000), and the motivation of a person (Muller,1984). Personal traits thus directly affect IPC.

Another important factor is the tasks and processes thatneed to be completed with the help of information. Theless a process is based on reoccurring routines (Tushman& Nadler, 1975) and the more complex it is in terms of theconfiguration of its steps (Bawden, 2001; Grise & Gallupe,1999, 2000), the higher is the information load and thegreater is the time pressure on the individual (Schick et al.,1990). The combination of these two factors that increasethe IPR can lead to information overload. Informationoverload is especially likely if the process is frequently in-terrupted and the concentration of the individual suffers asa consequence (Speier et al., 1999). Information overloadis also more likely if managers face an ever greater num-ber of parallel projects or tasks that they have to manage(see Wurman, 2001). In this way, complex tasks or pro-cesses directly increase the IPR. This fact is aggravated bya reduced IPC as a result of frequent context switching ordistraction.

Finally, information technology and its use and misuseare a major reason why information overload has becomea critical issue in many organizations in the 1980s and1990s. The development and deployment of new infor-mation and communication technologies, such as the In-ternet, intranets, and extranets, but especially e-mail, areuniversally seen as one major cause of information over-load (Bawden, 2001). There are, however, also argumentsin favor of e-mail. Edmunds and Morris (2000), for ex-ample, stress advantages like the fact that e-mail is anasynchronous form of communication and is less likely tointerrupt the normal work flow. Closely related to the prob-lem of e-mail overload is the discussion of pull versus pushtechnologies and whether they have a positive or negative

impact on an individual’s IPC and IPR. Pushing selectedpieces of information to specific groups reduces on theone hand their information retrieval time, but increaseson the other the amount of potentially useless informationthat a person has to deal with (Edmunds & Morris, 2000).In addition, it causes more frequent interruptions (Speieret al., 1999). Information technology can thus potentiallyincrease the individual’s IPC while at the same time in-creasing the IPR.

A complete list of the specific overload causes that arementioned in the relevant literature can be found in Table 3.It is structured according to the five categories discussedearlier.

Having reviewed the major causes of information over-load and their impact on IPC and IPR, we can now look attheir effects or observable symptoms.

Symptoms of Information Overload

One of the first researchers to examine the effects of over-load was the American psychologist Stanley Milgram(1970), who analyzed signal overload for people livingin large cities. In his study, he identified six common reac-tions to the constant exposure to heavy information load,which are allocation of less time to each input, disregardof low-priority inputs, redrawing of boundaries in somesocial transactions to shift the burden of overload to theother party of the exchange, reduction of inputs by filteringdevices, refusal of communication reception (via unlistedtelephone numbers, unfriendly facial expressions, etc.),and finally creation of specialized institutions to absorbinputs that would otherwise swamp the individual (seealso Weick, 1970, for this point).

In the organizational context, frequently describedsymptoms of information overload on the individual levelare a general lack of perspective (Schick et al., 1990),cognitive strain and stress (Malhotra, 1982; Schick et al.,1990), a greater tolerance of error (Sparrow, 1999), lowerjob satisfaction (Jacoby, 1984), and the inability to useinformation to make a decision (Bawden, 2001)—the so-called paralysis by analysis. Many other symptoms notedby different researchers are listed in Table 4.

The big question with regard to effects of informationoverload is whether and how it impacts decision accuracy,decision time, and general performance. While research re-sults have often been contradictory, especially among thegroundbreaking studies in marketing (the inconsistencieswere in part due to methodological problems; see Jacobyet al., 1974; Malhotra et al., 1982; Muller, 1984), there iswide consensus today that heavy information load can af-fect the performance of an individual negatively (whethermeasured in terms of accuracy or speed). When infor-mation supply exceeds the information-processing capac-ity, a person has difficulties in identifying the relevant


TABLE 3Causes of information overload

Causes of information overload References

Personal factors • Limitations in the individual humaninformation-processing capacity

Herbig and Kramer, 1994

• Decision scope and resulting documentation needs Kock, 2001• Motivation, attitude, satisfaction Muller, 1984• Personal traits (experience, skills, ideology, age) Owen, 1992; Hiltz and Turoff, 1985;

Muller, 1984; Schneider, 1987; Swainand Haka, 2000

• Personal situation (time of the day, noise, temperature,amount of sleep)

Owen, 1992; O’Reilly, 1980

• Senders screen outgoing information insufficiently Van Zandt, 2001• Users of computers adapt their way of interacting with

computers too slowly with respect to the technologicaldevelopment

Maes, 1994

• Social communication barriers break down Schultze and Vandenbosch, 1998Information characteristics • Number of items of information rises Bawden, 2001; Herbig and Kramer, 1994;

Jacoby et al., 1974; Jacoby 1977, 1984;Malhotra, 1982

• Uncertainty of information (info needed vs. infoavailable)

Schneider, 1987; Tushman and Nadler,1978

• Diversity of information and number of alternativesincrease

Bawden, 2001; Iselin, 1988; Schroderet al., 1967

• Ambiguity of information Schneider, 1987; Sparrow, 1999• Novelty of information Schneider, 1987• Complexity of information Schneider, 1987• Intensity of information Schneider, 1987• Dimensions of information increase Schroder et al., 1967• Information quality, value, half-life Sparrow, 1998, 1999• Overabundance of irrelevant information Ackoff, 1967

Task and process parameters • Tasks are less routine Tushman and Nadler, 1975• Complexity of tasks and task interdependencies Tushman and Nadler, 1975• Time pressure Schick et al., 1990• Task interruptions for complex tasks Speier et al., 1999• Too many, too detailed standards (in accounting) Schick et al., 1990• Simultaneous input of information into the process Grise and Gallupe, 1999, 2000• Innovations evolve rapidly—shortened life cycle Herbig and Kramer, 1994• Interdisciplinary work Bawden, 2001

Organizational design • Collaborative work Wilson, 1996• Centralization (bottlenecks) or disintermediation

(information searching is done by end users rather thanby information professionals)

Schneider, 1987

• Accumulation of information to demonstrate power Edmunds and Morris, 2000• Group heterogeneity Grise and Gallupe, 1999• New information and communication technologies (e.g.,

groupware)Bawden, 2001; Schultze and Vandenbosch,

1998; Speier et al., 1999Information technology • Push systems Bawden, 2001

• E-mails Bawden, 2001• Intranet, extranet, Internet Bawden, 2001• Rise in number of television channels Edmunds and Morris, 2000• Various distribution channels for the same content Edmunds and Morris, 2000• Vast storage capacity of the systems Schultze and Vandenbosch, 1998• Low duplication costs Schultze and Vandenbosch, 1998• Speed of access Schultze and Vandenbosch, 1998


TABLE 4Symptoms or effects of information overload

Symptoms References

Limited information search andretrieval strategies

• Search strategies through information setsbecome less systematic (this is less true for moreexperienced searchers)

Swain and Haka, 2000

• Limited search directions Cook, 1993• Move from compensatory search patterns to

noncompensatory search patternsCook, 1993

• Identification and selection of relevantinformation becomes increasingly difficult

Jacoby, 1977; Schneider, 1987

• Difficulties to reach target groups (senderperspective)

Herbig and Kramer, 1994

Arbitrary information analysisand organization

• Overlapping and inconsistent informationcategories

Eppler, 1998

• Ignore information and be highly selective(omission)

Bawden, 2001; Edmunds and Morris,2000; Herbig and Kramer, 1994; Hiltzand Turoff, 1985; Sparrow, 1999

• Loss of control over information Bawden, 2001; Wurman, 1990• Lack of critical evaluation (become too

credulous) and superficial analysisShenk, 1997; Schick et al., 1990;

Schultze and Vandenbosch, 1998• Loss of differentiation Schneider, 1987• Relationship between details and overall

perspective is weakened and peripherical cuesget overestimated

Owen, 1992; Schneider, 1987

• Higher time requirements for informationhandling and time delays

Jacoby, 1984; Hiltz and Turoff, 1985

• Abstraction and necessity to give meaning leadto misinterpretation

Sparrow, 1999; Walsh, 1995

Suboptimal decisions • Decision accuracy/quality lowered Malhotra, 1982; Jacoby, 1984; Hwangand Lin, 1999

• Decision effectiveness lowered Schroder et al., 1967• Inefficient work Bawden, 2001• Potential paralysis and delay of decisions Bawden, 2001; Schick et al., 1990

Strenuous personal situation • Demotivation Baldacchino et al., 2002• Satisfaction negatively affected Jacoby, 1984; Jones, 1997• Stress, confusion, and cognitive strain Jones, 1997; Malhotra, 1982; Schick

et al., 1990• Lacks to learn since too little time is at

dispositionSparrow, 1999

• Greater tolerance of error Sparrow, 1999• Lack of perspective Schick et al., 1990• Sense of loss of control leads to a breakdown in

communicationSchneider, 1987

• False sense of security due to uncertaintyreduction (overconfidence)

Meyer, 1998; Jacoby, 1984; O’Reilly,1980

information (Jacoby, 1977), becomes highly selective andignores a large amount of information (Bawden, 2001;Herbig & Kramer, 1994; Sparrow, 1999), has difficulties inidentifying the relationship between details and the overall

perspective (Schneider, 1987), needs more time to reacha decision (Jacoby, 1984), and finally does not reach adecision of adequate accuracy (Malhotra, 1982). Becauseof these many potential negative effects, it is important to


devise effective countermeasures. They should address notonly the symptoms of information overload but also itscauses. In the next subsection we provide an overview ofsuch mechanisms.

Countermeasures Against Information Overload

Literature on information overload not only discusses ma-jor causes and effects, but also proposes possible effec-tive countermeasures to address the issues related to in-formation overload. These countermeasures range fromgeneral suggestions concerning attitude to very specificsoftware tools (such as filtering agents, automatic sum-marizers, or visualization algorithms) that help to processlarge amounts of information. A list of countermeasuresmentioned in the literature can be found in Table 5, whichuses the same organizational schema that was used to clas-sify the causes, so that the two (causes and countermea-sures) can be directly related to one another (keeping inmind possible side effects).

With regard to information itself, information overloadcan be reduced if efforts are made to assure that it is ofhigh value, that it is delivered in the most convenient wayand format (Simpson & Prusak, 1995), that it is visual-ized, compressed, and aggregated (Ackoff, 1967; Meyer,1998), and that signals and testimonials are used to min-imize the risks associated with information (Herbig &Kramer, 1994). On the individual level, it is important toprovide training programs to augment the information lit-eracy of information consumers (Bawden, 2001; Koniger& Janowitz, 1995; Schick et al., 1990) and to give em-ployees the right tools so that they can improve their time(Bawden, 2001) and information management (Edmunds& Morris, 2000) skills. As far as improvements for theorganizational design are concerned, various authors takeon conflicting positions. While earlier contributions stressthe importance of self-contained tasks and lateral relation-ships (Galbraith, 1974), more recent studies see this fo-cus on collaborative and interdisciplinary work as a causerather than as a countermeasure of information overload(Bawden, 2001; Wilson, 1996). If the cause of informa-tion overload relates to process problems, several authorssuggest standardization of operating procedures (Bawden,2001; Schick et al., 1990; Schneider, 1987), collabora-tion with information specialists within the process teams(Edmunds & Morris, 2000), and use of facilitators or col-laborative tools (such as virtual team rooms) as “pro-cess enablers” for cognitive support (Grise & Gallupe,1999/2000). Finally, at the level of information technol-ogy, several authors advocate the use of intelligent infor-mation management systems for fostering an easier pri-oritization of information (Bawden, 2001; Meyer, 1998;Schick et al., 1990) and providing quality filters (Ack-off, 1967; Edmunds & Morris, 2000; Grise & Gallupe,

1999/2000). Examples of such intelligent systems are de-cision support systems (DSS) that reduce a large set ofoptions to a manageable size (Cook, 1993).

In the survey just described we can see that many au-thors list a multitude of possible countermeasures, but thatthey do not provide specific suggestions on how to com-bine organizational, technological, personal, and infor-mation- and task-based improvement actions. Clearly, asystematic methodology (comparable to other standard-ized problem solving approaches) to prevent or reduceinformation overload is still missing. Such a methodol-ogy should combine insights from various disciplines toprovide effective countermeasures that can be adapted tovarious contexts. For example, insights from consumerresearch on the importance of branding for reducing in-formation overload can be used for new MIS instruments(Berghel, 1997; Jacoby et al., 1974). Such prescriptivesuggestions must be based on rigorous empirical research.The next subsection outlines how our framework can beemployed to conduct such empirical research.

Testable Models Derived from the Framework

The framework just presented serves primarily as an ori-enting map for understanding and examining the overallscope of information load research across different dis-ciplines. However, it can also serve as a basis for futureempirical research. For instance, three testable models canbe derived from the framework.

The first testable model operationalizes the five causecategories as independent variables that lead to (or pre-dict) information overload (the dependent variable). Eachcause group consists of the individual items described inthe causes summary table (see Table 3). The data are col-lected via questions asked in a Likert-scale manner. In thisway, the correlation between causes and the occurrence ofinformation overload (measured as the subjective feelingof not being able to process all relevant information in theavailable time) can be measured. In addition, a question-naire based on this model can be used to test whether wehave allocated the individual causes to the right cause cat-egory (based on goodness of fit or Cronbach alpha values).

The second testable model relates to the possible symp-toms of information overload. The symptoms that are listedin Table 4 can be converted into questions. Based on ques-tionnaire results, one can then build groups of symptomsthrough factor analysis and correlate these groups (and theindividual symptoms) with the question regarding over-load (e.g. “Do you feel that you suffer from informationoverload?”). This can help us understand which symp-toms may be most representative of the overload phe-nomenon and thereby validate our symptom categories. Inthis model, the independent variables would be the identi-fied symptoms, whereas the dependent variable would be


TABLE 5Countermeasures against information overload

Countermeasures References

Personal factors • Improve personal time management skills and techniques Bawden, 2001• Training programs to augment information literacy:

information-processing skills such as file handling, usinge-mail, classification of documents, etc.

Bawden, 2001; Jones, 1997;Schick et al., 1990; Konigerand Janowitz, 1995

• Improve personal information management Edmunds and Morris, 2000• Systematic priority setting Schick et al., 1990• Improve the screening skills for information Van Zandt, 2001

Information characteristics • Raise general quality of information (i.e., its usefulness,conciseness) by defining quality standards

Allert, 2001; Keller and Staelin,1987; Meglio and Kleiner,1990; Simpson and Prusak,1995

• Focus on creating value-added information Simpson and Prusak, 1995• Promulgation of rules for information and communication

design (e.g., e-mail etiquette)Bawden, 2001

• Compress, aggregate, categorize, and structure information Ackoff, 1967; Grise and Gallupe,1999/2000; Hiltz and Turoff,1985; Iselin, 1988; Koniger andJanowitz, 1995; Scammon,1977

• Visualization, the use of graphs Chan, 2001; Meyer, 1998• Formalization of language Galbraith, 1974• Brand names for information Berghel, 1997• Form must follow function must follow usability Herbig and Kramer, 1994• Simplify functionalities and design of products Herbig and Kramer, 1994• Customization of information Ansari and Mela, 2003; Berghel,

1997; Meglio and Kleiner, 1990• Intelligent interfaces Bawden, 2001• Determine various versions of an information with various

levels of detail and elaborate additional information thatserves as summaries

Denning, 1982

• Organize text with hypertext structures or gophers Nelson, 2001• Interlink various information types (as internal with

external information)Denton, 2001; Meglio and

Kleiner, 1990Task and process parameters • Standardize operating procedures Bawden, 2001; Schneider, 1987;

Schick et al., 1990• Define decision models developed for specific decision

processes (e.g., decision rules)Ackoff, 1967; Chewning and

Harrell, 1990• Install an exception-reporting system Ackoff, 1967• Allow more time for task performance Schick et al., 1990• Schedule uninterrupted blocks of time for completing

critical workSorohan, 1994

• Adequate selection of media for the task Schick et al., 1990• Handle incoming information at once Sorohan, 1994• Collaboration with information specialists within the teams Edmunds and Morris, 2000• Bring decisions to where information exists when this

information is qualitative and ambiguousGalbraith, 1974

• Install process enablers for cognitive support Grise and Gallupe, 1999/2000• Use simpler information-processing strategies Schick et al., 1990

(Continued on next page)


TABLE 5Countermeasures against information overload (Continued)

Countermeasures Reference

• Regulate the rate of information flow Grise and Gallupe, 1999, 2000• Search procedures and strategy Ackoff, 1967; Bawden, 2001; Meyer,

1998; Olsen et al., 1998; Revsine,1970

• Define specific, clear goals for the information in order tocontextualize it and turn it meaningful

Baldacchino et al., 2002; Denton,2001; Meglio and Kleiner 1990

• Communicate information needs to providers Meglio and Kleiner, 1990• Provide incentives that are directly related with decisions

in order to make decision relevant information beprocessed more effectively

Tuttle and Burton, 1999

• Install a measurement system for information quality Denton, 2001Organizational design • Coordination through interlinked units Tushman and Nadler, 1978

• Augment info processing capacity through chances in org.design

Galbraith, 1974; Schick et al., 1990;Tushman and Nadler, 1978

• Creation of lateral relationships (integrate roles, createliaisons between roles, teamwork etc.)

Galbraith, 1974

• Coordination by goal setting, hierarchy, and rulesdepending on frequency of exceptions (uncertainty)

Galbraith, 1974

• Creation of self-contained tasks (reduced division oflabor, authority structures based on output categories) →autonomous groups

Galbraith, 1974

• Reduce divergence among people (e.g., with regard toexpectations) trough socialization (e.g., frequentface-to-face interactions)

Schneider, 1987

• Install appropriate measures of performance Ackoff, 1967• Hire additional employees Schick et al., 1990• Create slack resources Galbraith, 1974

Information technologyapplication

• Intelligent information management (prioritization) Bawden, 2001; Meyer, 1998; Schicket al., 1990

• Install voting structures to make users evaluate theinformation

Denning, 1982; Hiltz and Turoff, 1985

• Prefer push to pull technologies Edmunds and Morris, 2000; Denning,1982; Friedmann, 1977; Herbig andKramer, 1994

• Facilitator support through (e-)tools Grise and Gallupe, 1999, 2000• Decision support systems should reduce a large set of

alternatives to a manageable sizeCook, 1993

• Use natural language processing systems (search withartificial intelligence)

Nelson, 2001

• Information quality filters Ackoff, 1967; Bawden, 2001;Denning, 1982; Edmunds andMorris, 2000; Grise and Gallupe,1999, 2000; Hiltz and Turoff, 1985;Jones, 1997

• Intelligent data selectors (intelligent agents) Berghel, 1997; Edmunds and Morris,2000; Maes, 1994

• Use systems that offer various information organizationoptions (e.g. filing systems)

Hiltz and Turoff, 1985; Sorohan, 1994


the occurrence of information overload (in the opinion ofthe respondents).

The third testable model addresses possible counter-measures against information overload. It uses the five(cause) categories to ask respondents about countermea-sures that may or may not be in place in their organization(and that may or may not help fight overload). Based on thesurvey results, the effectiveness of these countermeasures(as well as their grouping) can be evaluated. The inde-pendent variables are the already implemented counter-measures in a company, whereas the dependent variable isthe occurrence of information overload for the questionedindividuals.

The main challenge in developing these three modelsis adequately converting the factors we have found in theliterature to scaleable questions that can be answered ac-curately (and honestly) by the respondents.

The framework presented so far gives a systematic over-view on the major findings of scientific research on infor-mation overload. The discussion on how our frameworkcan be tested with the help of three individual models indi-cates how future studies in the field can proceed. In order togenerate further suggestions on the future of informationoverload research, we next go beyond the mere descrip-tion of the field and analyze its inherent discourse patterns.This will enable us to see other development needs and ne-glected areas.

Biases and Tendencies in the Literature

In order to characterize the four literature domains, weemploy two visualization formats: the publication or ci-tation timeline for the analysis of the impact of relevantauthors in various management domains and the natureof their contribution, and the discipline Venn diagram forthe analysis of interdisciplinary research on the topic to

FIG. 4. Timeline of publications and citations of information overload studies in the area of marketing.

gain a deeper understanding of the information overloadphenomenon.

The Publication Timeline: Overload ResearchPatterns by Discipline

The timeline diagram does not focus on particular con-structs, but on the authors and their impact. It is a goodvisualization tool if the historic or process perspective of adiscourse is analyzed. We have drawn a timeline diagramfor each one of the four areas in which information over-load research has primarily been conducted over the last 30years, namely, accounting, marketing, organizational be-havior, and management information systems (MIS). Theobjective is not to map out all the articles per field andshow all the references to other overload articles, since theresulting diagrams would get too crowded and loose clarityand insight. The goal instead is to foreground major con-tributions. To determine the “relevant” contributions, wehave limited ourselves to articles that were cited repeatedlyby other articles. In the following subsections, we look ateach domain timeline in detail and provide suggestions forfuture research.

Marketing

Information overload within marketing, or more specifi-cally within consumer research, has become a critical issuesince the explosion in the number of brands in the earlyseventies. Figure 4 reveals that only a few studies havebeen done on a conceptual level and almost all the over-load research in marketing is of empirical nature. This maylead to slower, but more rigorous, theoretical progress. Fortheir theoretical base, the marketing researchers rely onthe findings of psychologists and cognitive scientists, inparticular on Miller’s study on our limited capacity for


information processing (Miller, 1956). Moreover, the em-pirical research is, with the only exception of Muller(1984), exclusively based on experiments and neglects sur-veys or case studies.

The tangled structure of the references reflects the in-tense debate that occurred around the Jacoby et al. firststudy. The methodology employed by them was contestedby Wilkie (1974), Scammon (1977), Malhotra (1984), andothers. Jacoby and Malhotra emerge to be the gurus withinthe field. The most intense period of research on informa-tion overload was from the mid-1970s to the mid-1980s.The main question that preoccupied the researchers waswhether the number of brands and their attributes (infor-mation load) influence product choice of consumers. Gen-erally, the research in the field of marketing focuses onthe impact of information overload on decision quality, ondecision time, and on the actual number of informationitems that can be processed in a typical purchase situation.As a result of this focus, the information overload litera-ture in the marketing domain neglects vital issues such asskills, timing, and technological and organizational issues.Nevertheless, the marketing discipline has legitimized theresearch on information overload by experimentally doc-umenting the inverted U-curve effect.

Accounting

The timeline of the contributions from the field of ac-counting (Figure 5) presents a similar picture as the oneof marketing, insofar as the conducted research is almostexclusively empirical. Again, the theoretical basis is bor-rowed from psychologists and cognitive scientists suchas Schroder et al. (1967), Miller (1956), and Simon andNewell (1971). Apart from these fundamental insightsfrom psychology, the research is not particularly inter-disciplinary. Schick et al. (1990) and to a smaller extentalso Tuttle and Burton (1999) are exceptions to this gen-

FIG. 5. Timeline of publications and citations of information overload studies in the area of accounting.

eral trend. Both articles include extensive literature re-views and contain important insights from organizationresearchers as well as from MIS scholars. Similar to thecase of marketing, the empirical research in accounting isbased on experimental situations and not on field researchin organizations. Additionally, Figure 5 shows that Casey,Iselin, and Abdel-Khalik are authors with a high impacton the studies of information overload in accounting. As atendency, the researchers who conduct empirical researchoften refer to conceptual studies, but the latter rarely referto the former. This, however, would be crucial for generat-ing consistent theoretical progress in the study of overloadin accounting. The main theme of accounting studies is theimpact of information load on decision quality or accuracyfor matters regarding budgeting decisions (as in Swain &Haka, 2000) or predictions of bankruptcy (as in Casey,1980).

Organization Science

What is striking in the area of organization science isthat almost all the contributions on information overloadare conceptual articles. The few empirical papers includeO’Reilly (1980) and Griffeth, Carson, and Marin (1988).These two studies work with a subjective definition of in-formation overload and focus on the satisfaction of the per-son experiencing information overload. The measurementtools they employ are questionnaires and not experiments.

Figure 6 depicts a rather loosely connected structureof citations, in which Galbraith and Tushman and Nadlerhave prominent positions. The main reason for this loose-ness of structure is that the authors refer to organizationscientists who made important contributions for generalorganizational issues, but not specifically in informationoverload-related topics. These contributions are thereforenot visible in the diagram. The most intense research ac-tivity took place in the 1990s. Possible reasons for this


FIG. 6. Timeline of publications and citations of information overload studies in the area of organization science.

heightened interest in the 1990s include the rapid propa-gation of the Internet and other information technologies,as well as the trend toward collaborative work and flathierarchies (Meyer, 1998). The organization science re-searchers are mainly interested in showing whether andhow the information-processing capacity of an individualcan be expanded through changes in the organizational de-sign and how this design influences the information pro-cessing requirements. Again, what has been said for theother research areas is also true for organization science,namely, that the research in this domain is not highly in-terdisciplinary. This is surprising for a field that typicallyincorporates many concepts from related social sciences.

Management Information Systems

Surprisingly, MIS has not been the discipline that has dealtwith information overload in the most extensive manner.Authors in the field of MIS mostly use the concept of

FIG. 7. Timeline of publications and citations of information overload studies in the area of MIS.

information overload as a starting point for their technol-ogy application discussions. Information overload per seis mostly not systematically defined, discussed, or ana-lyzed, but seen as a given problem that has to be resolved.Consequently, the net number of articles dealing primarilywith information overload in the MIS field is remarkablylow when compared to the total number of MIS papers thataddress the phenomenon in their title or abstract.

The major publication activity occurred in the 1990s(except for Ackoff, 1967; Denning, 1982; and Hiltz &Turoff, 1985) (Figure 7). In spite of this fact—and with theexception of Schultze and Vandenbosch’s article (1998)that combines insights from accounting, marketing, andorganization science—the MIS researchers do not seem toprofit enough from already existing information overloadstudies outside of their field. As mentioned earlier, thefocus of MIS researchers has been to propose effectivecountermeasures, and not to study the root causes of theproblem or its contextual factors.


Consequently, the MIS research is concentrated on con-ceptual studies and there is an obvious missing linkbetween conceptual and empirical studies; the two app-roaches do not often refer to one other. One very valu-able exception is again the contribution of Schultze andVandenbosch (1998), which combines both a literature re-view and a survey. The article refers to conceptual papersas well as to empirical findings from other areas outside theMIS domain. Aside from this exception, MIS researcherstend to be mainly interested in finding technical solutionsfor the information overload problem. Their contributionsare thus interesting with regard to technology-based coun-termeasures against information overload.

From the analysis of the different time lines several con-clusions can be drawn. One is that the transfer between em-pirical and conceptual studies can be improved and shouldbe intensified in future research.

Most of the empirical research that has been conductedwithin the aforementioned disciplines is done in experi-mental settings and hence does not rely on authentic man-agement contexts. Interestingly, some research areas focusmore on empirical studies and lack conceptual research,which is true for accounting and marketing, while the ar-eas of organization science and MIS are more interestedin conceptual approaches. But all the four areas, exceptto some extent the area of accounting, do not achieve a

FIG. 8. Cross-referencing among major information overload studies.

consistent transfer from empirical to conceptual researchand vice versa. This, however, is a crucial prerequisitefor cumulative research. Another prerequisite for cumula-tive research is the transfer of research findings betweenclosely related disciplines. This important issue is furtherexplored in the next section.

The Status of Interdisciplinary InformationOverload Research

The Venn diagram depicted in Figure 8 maps the cross-citations between major overload articles. The inclusioncriteria are the same ones as for the publication timelines.It facilitates an examination of the interdisciplinary statusof information overload research.

In general, only a few authors integrate various man-agement perspectives to study the problem of informa-tion overload. In fact, there are no intersections betweenthe area of accounting and either marketing or MIS toour knowledge. Similarly, there is no intersection betweenmarketing and MIS. Most of the intersections (in termsof citing and using relevant work from other domains)are visible within the area of organization science. Someauthors of the other three fields integrate findings fromorganization science. The diagram does not lay out theentire scope of interdisciplinary research, because it does


not show whether authors integrate perspectives of otherresearch disciplines, such as cognitive science or psychol-ogy. Clearly, future studies should draw more extensivelyon existing research in other contexts (Akin, 1997).

CONCLUSIONS

In conclusion, we discuss some of the limitations of ourapproach and we highlight implications of our analysis forfuture research on information overload.

The limitations of this review article relate to its method-ology and scope. In terms of methodology, the approachwe have chosen is a qualitative, inductive one with a fo-cus on surfacing the major categories in the overload dis-course. A bibliometric approach could have revealed moredetailed results regarding the impact of certain overloadcontributions, and a hypothesis-based method would haveled to a greater focus in the article. Our aim, by contrast,has been to provide a broad overview of the main dis-course elements in four business-related fields. In terms ofscope, it is clear that the four fields we reviewed are not theonly areas where information overload is a major concern.Library studies, pedagogy, military studies, and entertain-ment are other domains that could have been included inthe review. Our focus, however, has been on central disci-plines of business-related research. Based on the reviewedliterature in these four fields, several directions for futureresearch can be envisioned.

One, we need to employ alternative research methodsthat can be used to study the phenomenon. Our reviewshows that information overload has mainly been studiedvia experiments with the exception of a few studies thatused surveys, qualitative interviews, click-through anal-ysis, document analysis, and formal modeling methods.Because of this dominant focus on experiments, we sug-gest that other research methods should be employed inorder to triangulate prior findings. Such methods couldinclude ethnographies, action research, case studies, andlongitudinal studies, all of which capture more of the con-textual side of the overload problem than experiments.These inductive methods can then lead to more informedhypotheses and refined experiments. A longitudinal ap-proach could for example be used to examine the effectsof prolonged overload on an organization and on the pro-cessing capacity of its employees.

Two, we need to examine ways of increasing the amountof cross-fertilization in information overload research. Aswe have noted at various points in this article, truly inter-disciplinary approaches are not very common in informa-tion overload research. One of the reasons for this, and—we suspect—for the lack of transfer among empirical andconceptual studies, is that conducting interdisciplinary re-search increases the degree of information (over)load forthe researchers themselves (Bawden, 2001, p. 9). In or-

der to keep up with the developments in their field, re-searchers have to specialize and limit their research scope.Here editors and reviewers have a vital role to play. Theycan inform researchers about related and relevant findingsthat have been excluded from the researcher’s focus area.Editors may also occasionally ask reviewers from otherdisciplines to evaluate submitted articles so that they canprovide suggestions from other fields. Another approachcould be to create dedicated interdisciplinary journals thatencourage contributions that cross traditional disciplinaryboundaries (such as The Information Society).

This push toward interdisciplinarity does not have tolead to an identity crisis of a discipline such as MIS(Benbasat & Zmud, 2003) if the individual disciplines areaware of their relative strengths and weaknesses regard-ing a particular research topic. As shown in our reviewarticle, each one of the four domains has its advantagesand drawbacks for the study of information overload. Theadvantage of MIS studies on information overload, forexample, is their focus on solutions and on the effects ofnew information technology on the individual, the group,and the organization. However, it may have overlookedsome of the organizational parameters and their role in in-creasing or decreasing information overload. Organizationscience can provide insightful suggestions on this score(such as the effect of decentralization on the amount of in-formation that needs to be communicated and processed)leading to more realistic information technology (IT) solu-tions. On the other hand, the organizational point of viewmay lack insights on the individual’s reactions to suchchanges. This, in turn, is where the experimental studiesof accounting and marketing can provide helpful findingsand methodologies. These insights can be combined andoperationalized in empirical investigations, for example,regarding the effects of e-mail load and e-mail policies onworker productivity, decision quality, and communicationbehavior.

Such interdisciplinary approaches to the study of over-load may, however, require research projects based oninterdisciplinary teams that combine the talents of (for ex-ample) MIS, accounting, and organization science schol-ars. They may reduce the potential overload for the indi-vidual researcher, as he or she can focus on his or her areaof expertise while incorporating insights from other areasthrough other team members. The reasons why such in-terdisciplinary research teams are not more common aremanifold and include existing research habits, assumptionsand methods, and institutional barriers, as well as com-munication and terminology problems. Nevertheless, theoverload problem calls for interdisciplinary approaches asmany of the open research questions in this field crosstraditional disciplinary boundaries (from understandingindividual coping behavior to designing organizationalcountermeasures).


One such open research question relates to the interrela-tionships highlighted in our conceptual framework, specif-ically the reciprocal effects of technological, personal,information-based, task-oriented, and organizationalchanges. Our framework and its derived models for sur-vey research on causes, symptoms, and countermeasurescan be used for such purposes. This can lead to a rank-ing that distinguishes high-impact causes and countermea-sures from low-impact causes and inefficient countermea-sures. Although the tables in this article provide a goodoverview of possible causes and countermeasures, they donot yet qualify or prioritize these factors. Future researchshould thus examine the interrelationships between thelisted causes and countermeasures in more detail. Thiscan lead to MIS solutions that address the problem driversand root causes of the overload issue with effective coun-termeasures.

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Documents

Eppler Information Overload[1]