Modeling service-seeking behavior in an academic library: a methodology and its application

Modeling Service-Seeking Behavior in an Academic Library: A Methodology and Its Application by Michael Heine, Ian Winkworth, and Kathryn Ray

The modern library is an ensemble of information services. The ways that its users choose and move between its services can be investigated and the picture so obtained complements that obtained from surveys on the effectiveness of individual services.

Michael Heine is a Principal Lecturer and Director of the Postgraduate Research Programme, School of Information Studies, University of Northumbria, Newcastle upon Tyne, NE1 8ST ⬍[email protected]⬎; Ian Winkworth is Director of Learning Resources, University of Northumbria, Newcastle upon Tyne, NE1 8ST ⬍[email protected]⬎; and Kathryn Ray is a Research Assistant, School of Information Studies, University of Northumbria, Newcastle upon Tyne NE1 8ST ⬍[email protected]⬎.

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nvestigations of the use of a library’s services can inform the library’s management about how the library and its users interact and can assist in the review of library policies. Traditionally, data on the use made of individual services [i.e., performance measurement (PM) data] are collected in furtherance of this aim. However, such data do not record the order in which users approach and progress their information use through the different services. For instance, do users tend to approach the catalog after visiting the shelves or vice versa? Do they search a bibliographic database in their subject discipline before browsing through the library’s periodical collection or after? Do they seek help from a subject specialist on the library’s staff before or after using a World Wide Web (Web) search engine? PM data, although highly useful in policy review, ignore such order relationships. It can readily be argued that knowledge about the order in which services are used is of fundamental value to the library management team. The ways in which users choose and move between information services to meet their information needs are expressions of their views about the best information strategy given those information needs. Each user’s strategy is formed on the basis of such factors as his or her perceptions of the value of the products delivered by each service, the likelihood of success in using each service, knowledge that the service exists, and the logical dependencies between one service and another (e.g., that one needs to know the shelf address of a book, and ideally its issue status, before searching for it on the shelves). Once an awareness of service choice and patterns in the

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movement between services is gained, a library’s management is in a stronger position to adapt policy to reality. For example, it may be considered, on the basis of survey data showing how users move from one category of service to another, that referral aids should be improved, or else provided de novo, or that the reasons for limited usage of a service regarded by library staff as valuable to users, but which users are avoiding, should be further investigated. An awareness of what users’ behavior patterns are, at a given time, also provides a benchmark against which the effects of a change in some policy can be compared. For example, a decision may be made to market a particular service more vigorously, to change its opening hours or its pricing policy, or to alter the number or deployment of its service points (e.g., computer terminals and help desks).1 The predicted effects of policy changes on users’ service-seeking behavior patterns can then be tested against the actual changes that occur, adding value to the PM data that shows the changes in each service taken individually. With the above in mind, we describes a methodology that aids the acquisition and analysis of data on the order of use of information services. The methodology is presented to aid academic librarians, but it is applicable to any library or, indeed, any system that supports information services whether or not recognized as “a library.” This study was motivated by the highly novel approach to the study of library/ user interaction described by J. MacGregor Smith and William Rouse, who modeled the physical movements of users within a public library.2 Another influence was the earlier study by Carol Sey-

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mour and J. Schofield, who investigated users’ behavior at card catalogs in academic libraries and who, as one aspect of this work, obtained data on users’ intended next action after experiencing failure at the catalog.3 The compartment model of data flow described by R. J. Lano was also an influence.4 The now considerable literature on library PM is also clearly relevant, but the present approach attempts to broaden the scope of such measurement rather than build on it. Our study differs from that of Smith and Rouse5 in that ours focuses on users’ behavior as characterized by service use rather than, as in their case, physical movement within a library building. This distinction is nontrivial for two reasons. First, a given information service may be offered at more than one physical point in a library, therefore, surveying usage of that service at a particular physical point will not necessarily show all of its usage. This will apply, for example, to a catalog service that can be accessed through terminals at many different points in a building or electronically from outside the library’s building, perhaps from the user’s home or from a campus computing laboratory. Second, a particular physical point or particular computer interface can be associated with usage of more than one type of service. For example, a single terminal might be used to search the Web, to find out if a particular document is in the local collection, to discover whether a document that is held by the library is on loan, to reserve a printed book that is currently on loan, to search a union catalog via a regional network, to search a bibliography stored in local mass memory [e.g., on a (DVD) accessible through a local area network], to read an article in an electronic journal, or send an e-mail to a friend. Again, a library help desk may support more than one service, offering advice on the selection of information resources or a catalog search technique, renewing a user’s registration, informing a user about how to make an interlibrary loan request, and so on. For these reasons, a study of users’ cognitive behavior patterns, as shown by service use, differs significantly from a study of users’ physical behavior patterns, notwithstanding the obvious relatedness of cognitive and physical behavior. The study reported here focuses on the cognitive movement of the user between information services of different types, irrespective of the physical locations of the relevant service points.

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Because the focus of our study was on cognitive behavior, the methodology reported here is hospitable to recent changes in the concept of a library, changes that have evolved in response to the opportunities created by electronic technology and increasing management concerns for system networking to achieve efficiencies and economies of scale. The newer outlook, in part associated with the digital library concept, is that a library of first resort to its users (i.e., one associated with a particular user community) should be viewed less as a store of information resources aspiring to self-sufficiency, and more as a system of procedures for accessing information resources that are, in part, dispersed across networks of cooperating peer-libraries. (To be pedantic, we should add that (1) the network is aided by document or document-surrogate servers that are not themselves libraries of first resort, in the sense described, and (2) users who belong to the community served by a library of first resort are increasingly able to bypass that library electronically, for example by personal subscriptions to the electronic journals of a learned society.) This change in viewpoint is easily overemphasized, but any study of library service use undertaken by an individual library must clearly recognize that the information services accessed by its users can be remote from it (i.e., can be owned and managed by other organizations). The library’s building is increasingly becoming a shell-structure of processes and human expertise that support services, rather than a shelter for resources and users, and management tools must accordingly be adapted to this change in concept. Lastly, the approach here differs from that of Seymour and Schofield’s6 in three respects. Our study addresses the sequence of service-uses within a user’s visit to an information system rather than the use of one particular service. It also addresses actual behavior in regard to users’ movements to successor services (and from predecessor services) rather than, as in their study, users’ intended or “proposed” behavior after service use. A final distinction is that the present study, unlike theirs, is not service pathology oriented. Rather, it seeks to explore serviceseeking behavior whether or not the user perceives failure or success in the delivery of service.

CONCEPTUAL FRAMEWORK Information-seeking behavior has long been a topic of interest within the information management profession because the latter exists to meet the needs of those who seek information and to facilitate the expression of those needs as demands on one or more information services. As such, information-seeking behavior may be regarded as an aspect of informationsearching behavior, which, in turn, is an aspect of information behavior. (The latter includes communication, for example.) This threefold hierarchy of concepts is the work of T. D. Wilson.7 Such a broad categorization for describing the behavior of individuals who use information systems (or, more accurately, who use the information services hosted by information systems) can be criticized on the basis of the everyday complexities of human actions. However, it provides a useful starting point for describing the types of human behavior associated with the individual person becoming informed.8 Accepting Wilson’s9 framework and our asides, the more specific concern of the present study was to develop a methodology that focused on just one aspect of information-seeking behavior, namely service-seeking behavior as expressed by users’ choices of library service and their movements between services in the attempt to fulfil an information need. Thus, our interest was in users’ cognitive behavior at this more macro level rather than the more micro level of information searching, to retain Wilson’s terminology. In effect, it was assumed that a meaningful distinction can be made between a user’s behavior to decide to use a service (e.g., a decision to use a catalog to discover the shelf locations of known documents) and a user’s behavior at the service (e.g., the searching of a catalog by author or title to verify a reference), even if the line between the two types of behavior sometimes needs to be arbitrarily drawn. Several examples illustrate this. A reader browsing open access shelves may be thinking in one or several modes, possibly at a broad level, “I think this is where the books on topic X will be,” but possibly also at a micro level, “this particular book is certainly on topic X, but reading a few pages of it shows it is too advanced for my purpose.” Again, when a reader scans a page of hypertext, such as a Web page, his or her pursuit of links to other pages could be interpreted as seek-

ing behavior, and yet such seeking might be prompted by detailed reading of current pages (i.e., searching behavior). In such cases, cognitive behavior might be conceptualized as alternating between macro and micro information styles. However, the inherently complex nature of information behavior, as indicated by such examples, does not invalidate studies that rest on definitions of particular behavior types. Instead, they may simply suggest that appropriate cautions should be recognized in interpreting their results because validity in the results of surveys that focus on behavior at the macro or service seeking level can be increased by several devices. Clear definitions of services, careful sample design and data collection, and declared arbitration rules where the behavior might be interpreted as being of either a searching or seeking type (or where it possesses a fine-grained character that alternates between these forms) can all help to distinguish information-searching behavior from serviceseeking behavior. If it is accepted as feasible that a study can largely separate information serviceseeking from information searching, several requirements follow. One requirement is an inventory of the information services that users can use because, otherwise, different behavioral forms cannot be categorized. A problem in this regard is devising an inventory that recognizes that several information services can support the same information-seeking functions. For example, a library may see the provision of information sources on open access shelves as one of its main services, yet this service has much in common with that of the provision of full-text, electronic sources via computer terminals. Both services support the same abstract function, namely document delivery: each differs from the other only in the medium of delivery. Another issue is how detailed the inventory of information services should be. For example, the service of providing users with open access to information resources might be seen as embracing the subservices of facilitating document retrieval based on subject criteria and on descriptions of known documents. The problem of how detailed an inventory should be becomes even more apparent when one attempts to identify the subservices included in what may too loosely be called a catalog service, as discussed earlier. A second, more minor, requirement of such a study is clearer terminology that distinguishes library ser-

vices from library functions and library processes. Perhaps at the risk of stating the obvious, a library service is a library function that is accessible to, and partly controllable by, a library’s clients. A library function is an assembly (or system) of processes.10

natural language, gender, and so forth. Such a study could be enriched by questions about the next service that the user intended to use, or the previous service used, but the essential focus, by definition, would be on the individual as a person rather than on his or her serviceseeking behavior.

MOTIVATION AND CONTEXT FOR THE STUDY: THE UNIT OF OBSERVATION The broad motivation for the study was the widely accepted need for the modern information manager to be user focused in reviewing policies and the obligation that this carries with it of acquiring and analyzing a range of pertinent data. The problem, however, is that the data commonly acquired tend to be polarized toward the characterization of either service use (i.e., PM data11) or the users that generate such use, with rather weaker concerns for the characterization of individuals’ needs as expressed in their information behavior. In the case of service use data, the unit of observation is the service transaction. For example, service use data might be found by examining the advice given at a help desk or the use of a catalog terminal where the service of interest was that of informing the user whether a wanted document, known to be held locally, is or is not on loan. In the latter case, the proportion of wanted documents that were not on loan could be evaluated, yielding one particular PM for the library concerned. Such studies might, of course, characterize the individuals bringing requests to the service and identify the service (if any) used before the present service and the service (if any) that they say they intend to use next. The focus of interest is, nonetheless, on the use of a given, single service. The library’s management could replicate the study across all services and obtain a portfolio of different performance measures, but the unit of observation in each case would essentially be service-use centered rather than user centered because information on individual users is lost with such replication. On the other hand, if the unit of observation was the user, rather than the service transaction, the management team might study each user’s information requirements independently of any approach made to any particular information service. One might discover what the user’s status was within the user community (e.g., teaching staff member, undergraduate student, technician, or research student) along with his or her age, habitual

“The information-seeking behavior concerned must have beginning and end points to be describable.” Both service use and system user can be combined in a survey. One such survey unit is formed by recording the sequence of decisions made by a system user to access the various information services when pursuing an information need. The information-seeking behavior concerned must have beginning and end points to be describable12 and, as such, will be referred to here as an episode of use (EOU). Examples of EOUs are readily imagined. The user might, say, be working on the preparation of: ●

A seminar paper;

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A bid for research funds by a member of faculty;

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An exhaustive literature search;

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A progress review presentation for a research project; or

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A survey of current practice in an area of teaching practice.

In each case, a sequence of decisions to use one or more information services will be defined by the user as he or she progresses the information task concerned.13 Once a management decision has been made to use a sequence of service-seeking decisions as the unit of observation, and relevant data have been obtained, several types of data description become possible. These include describing the following: ●

Lengths of these sequences. Summary statistics of this variable, such as mean, median, and quartile values or standard deviation, are then readily found.

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Frequencies of use of individual services. (These constitute one category of PM data formed here as a by-prod-

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●

●

●

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uct of data on EOUs. These frequencies do not assess service effectiveness, of course, only service popularity.) Extent to which subsequences of service use reoccur within EOUs (i.e., the extent of repetitive service-use patterns within instances of service seeking behavior.) Most common service-seeking sequences defined by the EOUs, along with evidence of their variety. (The description may also be of the most probable sequences for an arbitrary user.) Probabilities with which users at a given service will move to another given service. Probabilities with which users at a given service will have moved there from another given service.

EOU-derived data on sequences of service-seeking do not, of course, offer explanations for users’ behavior patterns (i.e., reasons why most users will access one service ahead of another) because such explanations are accessible (subjectively) only to the individual user. (Qualitative data based on verbal behavior are indirect, in this sense.) However, they identify areas of service where more detailed, qualitative data may be helpful (e.g., data that attempt to describe the affective antecedents of service use as investigated by Carol Kuhlthau.14 Richer portrayals of service-seeking by users than are offered by decision-sequence data will nevertheless be prompted by such data. On this basis, models of user behavior that enclose both highly encoded information behavior, in the form of service-seeking decisions, and more descriptive (i.e., less coded) data based on users’ verbal behavior as to their motivations and attitudes while attempting to fulfil their information needs may be able to be constructed.15 The particular strength of decision-sequence data lies in its offering a “bird’s eye view” of library/user interaction, where qualitative data are able to focus more on the complexities of specific transactions. However, although service-seeking data allow the frequencies of use of individual services to be identified, both relative to each other and absolutely, such data cannot describe service effectiveness as do the measures of availability or document delivery time.16 In that sense the EOU-centered approach and the PM approach are complementary.17 It also

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bears repeating that, although frequencies of service use can be derived from EOU data, the reverse is not true: the sequences of users’ movements between service categories cannot be reconstructed from statistics that pertain to individual services. Once service-seeking behavior data have been obtained, the management team will be in a stronger position to improve service provision policies. For example, users’ choices of service may raise questions about the following: ●

Relevance, extent, and quality of user education in regard to the existence, range, and nature of the library’s services;

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Adequacy of directional aids that refer users from one service to another within the library;

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Relative effectiveness, and nature and direction, of the dependencies between individual services;

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Adequacy and optimal deployment of service points within the library;

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Nature and effectiveness of the information technology support for individual services (e.g., down times and response times, user-friendliness, and effectiveness of interfaces);

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Visibility of services within the library and their opening hours; and

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Pricing regime for each service (e.g., direct charge or free at the point of use).

Findings that describe the balance between service attractiveness and service avoidance, for each service, might also suggest a need to examine the staffing of the service (e.g., whether the paraprofessional and professional staff levels are adequate and effectively balanced). In sharper terms, the acquisition of EOU-based data, and their analysis by objective means, will help to dispel or confirm some of management’s beliefs: is it really true that most students visit an academic library solely to use its desks for studying or solely to use a photocopier, for example? Do most users really go first to a (known or guessed) shelf location for hardcopy documents than go first to the online catalog to check for in-house locations of known documents? Is it true that less than, say, 1% of users’ visits involve the seeking of professional advice from library staff? EOU-based data also provide benchmarks on system use, which can enable the effects of

changes in policy to be assessed. The distinctiveness of such benchmarks over those provided by PM data are that they relate to users’ overall behavior within the information system (i.e., to the full range and character of users’ service uses). In other words, a less fragmented picture of system/user interaction than is obtained from PM data should be given because it is the user’s behavior within the entire EOU that is described.18 As noted below, EOU-based data also generate one category of PM data (service-use frequencies), although the reverse is not true. Lastly, because academic libraries are subsystems of a parent academic institution, the policies of the latter will influence the service-seeking behavior of the library’s users. For example, if a university’s policy is to reduce the number of teaching staff, this will almost certainly lead to reduced levels of contact between such staff and students, which, in turn, may lead to a greater dependence of students’ learning on library-managed information services and changes in (e.g., more systematic) patterns of informationseeking behavior.

GENERAL METHODOLOGY A set of information services offered by a library is first recognized, with similar services grouped into categories. (Examples are given later.) These categories may be labeled S1, S2, S3, . . . . A user’s EOU can then be characterized as a sequence of uses of these categories. Thus, for several user’s who are attempting fulfillments of particular needs, data on service-seeking might be recorded as follows: ●

User 1: S2, S3, S1, S4, S9;

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User 2: S7, S1, S2, S4;

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User 3: S4; and

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User 4: S5, S2, S5, S3.

Here, the second user has, for his or her recognized information need, first accessed one or more services in category S7, then accessed one or more services in category S1, then moved to service(s) in category S4, and then terminated use of the library’s services. The third user has accessed services in only one service category, namely S4, and has decided not to extend his or her information seeking beyond that category for the EOU concerned. Just how categories of service in libraries should be defined will be a matter of

professional judgment, as with the choice of PM variables. The judgment will be driven by management experience and intuition and will be responsive to the particularities of the library, its clientele, and host institution. However, as it would seem to be useful to have a general classification of library-centered information services, to provide a basis for comparison among libraries, the following is proposed. The scheme is prompted by the consideration that, in the most general terms, academic libraries provide core support for: ●

Document identification services;

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Document location services;

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Deferred document-use services; and

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Advice services.

These broad service categories may be broken down as follows: ●

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Document identification services: ●

The provision, within the managed system, of access to printed bibliographies to identify documents on the basis of general document attributes such as author, subject heading, and date;

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The provision of OPAC terminals that allow the same thing for system held documents; and

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The provision of access to local or remote database hosts for this purpose.

Document location services: ●

The provision of OPAC terminals that give the site/shelf addresses of known documents;

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The provision of a help desk that provides such information; and

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Signage within the (physical) collection facilitating access to locally stored documents.

Deferred document-use services: ●

The traditional services of lending printed documents for use outside the building and lending from closed access, short-loan collections for inhouse use;

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The reservation of documents on loan. (In digital libraries, this may be a user-authorized function supported by the OPAC.);

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The provision of copying equipment;

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The provision of Inter/Intranet client terminals allowing the user to download electronic sources; and

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The standard, loan-recall and interlibrary borrowing services for external, non-electronic sources.

Advice services: ●

The user supplies requests for advice to one or more service points via some medium (e.g., written memo, phone, fax, Web page pro forma, e-mail, or by physical movement to a staffed desk). The advice sought may require either professional knowledge (e.g., “Which information resource can best help me to. . . ?”) or paraprofessional skills (e.g., “How do I register as a system user?,” or “What is the intranet address for last year’s examination papers?”).

The above services’ classification attempts to avoid simplistic categorization where, for example, a category such as catalog services might be defined in such a way that it merges services that can be very distinct in functional terms.19 The classification also purposefully disregards the physical deployment of the points at which any service can be accessed. This based is on the assumption that the primary interest of a library’s management team will be in improved understanding of, and support for, users’ cognitive information behavior, with users’ physical movements through a library building regarded as derivative of that and, thus, of secondary interest. The level of detail to which a classification of a library’s information services should be taken presents another issue. If the classification is too broad, then meaningfulness and applicability in survey findings will be lost; too fine a classification, and variability in results arising from sampling error, will unduly limit the validity of conclusions drawn from the data. For example, the last category in the above scheme, advice services, might be argued to be insufficiently detailed because it does not distinguish between factual advice giving (e.g., “the reference books section is over there”) and professional advice giving (e.g., “to string search in DIALOG what you need to do is. . . ”). A pragmatic answer seems appropriate (as it is with problems of subject

classification), namely that detail in such a scheme should be provided to the extent determined by the needs of the policy review. However, a general classification, such as the one shown above (and developed later as Figure 1), will be appropriate if the aim is to understand users’ behavior as a whole or to compare service-seeking behavior between different libraries. For this reason, the authors suggest that a general services scheme should be included in the standards recognized by appropriate national bodies. Once a categorization of services such as that shown above has been agreed to, a list of sequences of service use associated with individual EOUs can be found by surveying users in an appropriate manner (e.g., by interviewing them at the end points of EOUs or by asking them to keep diaries of their service uses for a particular purpose). Data Reduction For a given classification of services and a given set of EOU-derived survey data in the form of a list of sequences of service use, various data reductions are possible. The previous main section identified six possible such reductions. In addition, in principle inferences can be drawn regarding the extent to which service-seeking behavior that follows use of one particular service is: (1) conditioned by the history of users’ seeking behavior prior to that usage or (2) memoryless20 (i.e., fully determined by the deliverables from that service alone.) Additional Variables Further variables extending the range of EOU data beyond that of simple sequences-of-service use may be suggested by the particular policy being reviewed. Such variables might be user related or might typify the EOU. User-related variables might be the user’s home department, whether the user has attended a library instruction course, or whether the user is a student or a member of faculty. EOUs might be typified by their duration, or by their nature as in the examples of information need given earlier. Such variables would allow sets of search sequences that reflected combinations of such factors to be lifted out for special attention, such as those characterized by users who are students (a user type), studying materials science (a department type), for a seminar paper (an EOU type). Cross-tabulations of “size of serviceseeking sequence” grouped into numeric

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Figure 1 Categories of Information Service

intervals, against one or more nominal level variables, would also provide deeper insight into the interactions between the library and its users. Although PM data and service-seeking data are obviously complementary, links between them can be created by statistical means. For example, one could survey users as they completed their behavior patterns and ask them to express, on a Likert scale (or alternatively a dollarworth scale), their overall satisfaction with the library services or by asking them to say how many useful information sources they had identified. These essentially PM-like variables could then be treated as dependent variables within a regression analysis, the independent variables being the size of the service-seeking sequence for the EOU, and/or a set of binary variables, each of which signified

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whether or not each service category had been accessed during the EOU. Approaches such as these lead to welldefined conclusions within clear logical structures because the data are expressed within broad categories and the relationships between data values sought are limited to quantitative ones. However, it is appreciated that the notions of service quality held by both library users and library managers are essentially intuitive and metamathematical. Accordingly, the types of data reduction indicated here necessarily remain open to judgments about their appropriateness or validity for a given problem, and the policy inferences that may be drawn from such data analyses will appeal to value frames and political realities that lie outside the mechanics of data acquisition and analysis, as well as to the data analyses. For a

broader view of the main issues involved in conceptualizing service quality, see the recent review article by Peter Hernon, Danuta A. Nitecki, and Ellen Altman.21 Transition Tables The matrix or array shown as Table 1 provides one way of analyzing the sequences of service use to provide an overall view of service-use behavior. Here, service categories are recorded in the diagonal cells of the table, but the first cell on the diagonal is labeled as a Start event, and the final cell on the diagonal as a Finish event. The number of transitions from use of service Si to use of service Sj is recorded in the cell that passes through the row in which Si occurs and the column in which Sj appears and is denoted by fij. For example, the cell immediately below that labeled S1 records the number of use

Table 1 The Basic Transition Matrix Start *

S1

*

*S2

*

S3

*

S4

*

S5 ...

* *

*

*

*

*

*

*

Finish

Notes: The array serves to record the frequenices of movement from one category of library service to another category. Service-categories are positioned in cells on the diagonal of the array, along with a Start event and a Finish event. The other cells serve to record the numbers of transitions from the service shown in the row to which the cell belongs, to the service shown in the column to which the cell belongs. Cells containing “*” represent impossible transitions. Row and column sums record the total number of usages of each service category.

transitions from S2 to S1. The cell immediately to the right of S1 records the number of transitions from S1 to S2. (For the mini list of sequences given earlier, these particular cells would hold frequencies of 0 and 1, respectively.) The matrix is nonsymmetrical about the diagonal. Cells marked with a “*” indicate impossible transitions. (A user cannot return to the Start event or to any service category having concluded the EOU.) The row total and the column total defined by any of the service-category cells (i.e., a cell on the diagonal) will be the same because any usage of a service must be followed by cessation of that usage. Such totals usefully show the relative popularity of the various service categories (and, in so doing, spin off one type of PM data). In particular, the sum of the frequencies in the row passing through the Start cell equals the total number of use episodes surveyed, as will also the sum of the frequencies in the column passing through the Finish cell. However, the distribution of transition frequencies within a row through a given service cell in general differs from that in the column through that cell because users’ approach patterns to a given information service, in general, differ from their exit patterns from that service. Frequency values of 0 attach to the diagonal cells themselves (i.e., fii ⫽ 0) because the service classification scheme, at a chosen level, ignores detail within each service category.22

Probabilities Probabilities can be constructed in a straightforward manner from the transition frequencies just described. For example, the probability that a randomly chosen library user will use a service in category Si during his or her EOU can be found by summing all the frequencies in the ith row of Table 1 and dividing this sum by the total of all uses of all services (i.e., the sum of such row sums for all service categories). Services can then be ranked in popularity so defined by this probability. However, these particular probabilities should be regarded more as PM data rather than service-seeking data because, in carrying out these summations, information on the order of use of the services is jettisoned. More sharply defined probabilities describe transitions between individual categories of service. For greater clarity in this regard, the following notation is introduced: ●

Si3Sj stands for a transition between service Si and service Sj.

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TotalSi stands for the total number of transitions to (or from) service Si.

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Pr(Si3Sj Si) stands for the probability that a user, at present using service Si, will next move to service Sj. (The conditional symbol signifies “situated at.”)

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Pr(Si3Sj Sj) stands for the probability that a user, at present using service Sj, has arrived there directly from service Si.

In more intuitive terms the various probabilities Pr(Si3Sj Si), for a randomly chosen user stationed at a given service Si describe the attractiveness of each of the other services Sj as next service of choice. The probabilities Pr(Si3Sj Sj), on the other hand, for a user stationed at service Sj, describe the historic influences on the user of the other services, Si (i.e., they describe the influences of the services that have brought the user to that particular service.23) Both probabilities are of interest to the information manager in that they show the relative attractiveness of different services to users, as expressed by their behavior, and the relative influences of each service on other services, on the basis of observational data. The discussion in the Appendix offers a novel measure of service performance based on these probabilities.

Most Probable Sequence and Most Common Sequence Although the library’s primary interest will presumably be in summarizing service-access behavior by individuals, the above probabilities allow a service-seeking sequence to be identified for an imaginary user who acts in the most probable way (i.e., a user who first chooses a service category according to what aggregated data show to be the most popular one when they commence their EOU, then chooses a second service category according to the most popular choice that follows the latter category, and so on for other categories). A service-seeking sequence defined in this way may be termed the most probable sequence (MPS). This sequence is not the same as the most commonly occurring sequence (MCS), which is identified simply by sorting and scanning the initial dataset. Given the large variety in search sequences that occur in practice, the MCS will vary strongly from survey sample to survey sample. Accordingly, MPSs appear to give a more definite picture of users’ seeking behavior, at the price of a rather more abstract view. Cut-down Transition Tables An information manager’s interest in user behavior, when addressing a particular policy issue, may be restricted to behavior involving only a few service categories. For example, a learning resources manager in a high school might be interested in the relative effects of a change in a library induction program on pupils’ dispositions: (1) to search a catalog before seeking professional advice and (2) vice versa. Again, a university librarian might be interested in the changes in the order of service seeking as between use of (1) the university library’s own catalog to identify local holdings, and (2) a union catalog to identify regional holdings, after a decision to recover the costs of regional borrowings from users. In such cases, a cutdown transition table that shows only the service categories of interest would be a convenient managerial tool. PM data in the form of service-use frequencies will, of course, identify changes in the relative volumes of use of both services, but will not show changes in the order in which these categories are used, nor the extent to which a user who has used one of the services abandons his or her search at that point.

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AN APPLICATION OF THE METHODOLOGY TO SERVICE-USE AN ACADEMIC LIBRARY

IN

Background, Sampling, and Data Capture To test and develop the methodology described above, a study of service use in two of the libraries managed by the Information Services Department of the University of Northumbria at Newcastle was undertaken. The chosen EOU was defined operationally as the single physical visit to a library building. (Electronic visits to the library, generated from outside the building, were disregarded.) The survey was restricted to students and was undertaken between January and March 1999, covering usage at both the end of the first semester (January) and the beginning of the second semester (February and March). A total of 309 students were interviewed. Interviewing occurred as subjects left the library concerned, which allowed data on the full sequence of service use during each visit to be captured, and students were interviewed on a next-inline basis. Interviewing was limited to two hours per day because of the heavy interpretative burden on the interviewer with this type of survey. Figure 1 shows the schedule of service categories used. This schedule was initially developed within the project team, then revised after formative criticism from professional staff in the Department of Learning Resources, and then further revised by using two successive pilot surveys. The pilot surveys used 20 students in each case. The broad classification of library services into the categories of resource discovery, resource delivery, and resource use, given by Peter Brophy and Peter Wynne,24 had an initial influence on the design of the schedule. As can be seen in Figure 1, the schedule recognized two levels of generality in describing library services: a broader categorization of services (columns 1 and 2) and an expanded set of categories (columns 3 and 4). Having these two levels allowed the alternatives of “seeing the wood for the trees” and “seeing the trees for the wood” when analyzing relevant data. The more detailed classification of services was the one used in the survey because the service-seeking choices it describes are easily re-coded to lie within the broader scheme, but not vice versa. Data captured via interview were quick, but demanding. The pilot studies had indicated that the interviewer would

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need both professional knowledge and general interviewing skills for data capture to be effective; this was borne out with the main survey. It had been decided at the survey design stage that additional data in the form of the user’s overall assessment of the value of the EOU would not be sought25 and that data capture would be spread over two library sites. The libraries concerned were those servicing the University’s Coach Lane Campus (157 interviews) and its City Campus (152 interviews). Both support broadly similar services, but these are offered to different teaching departments (e.g., humanities and engineering studies at City Campus and education and nursing studies at the Coach Lane Campus). The motivation for using two sites was to provide an initial test of the portability of the methodology, rather than to attempt comparative study as such. Even so, it was felt that preliminary comparative data could be of interest if the methodology were found to be workable at both sites. The following script was used when running the interviews: “Excuse me. Do you have a few minutes to answer some questions on behalf of the Library, please?” This was followed by an unscripted explanation of why the interviewer was there, and then the script: “Can you tell me the first thing you did when you entered the Library today?“ ”What did you do immediately after that?“ [repeated as necessary and concluding with] ”Can you also tell me which course you are studying?“ [and] ”Thank you for your time.“

Nevertheless, the script was not applied mechanically in view of inevitable variations in the way students presented themselves coupled with the need for the interviewer to respond creatively as the interview developed. Each student’s course of study was also noted at the time of the interview, and these were later mapped to U.K. HEFCE cost center codes,26 to help contribute to portability in the methodology, at least to other English university libraries. Classical library subject schedules such as Dewey or Library of Congress were considered to be less relevant to Northumbria University. However, they could also have been used to categorize students’ courses. Little difficulty was experienced in identifying students among other users, and refusal rates were reasonably low (41 by Coach Lane students and 53 by City Campus students). Refusals appeared to

be associated with either social factors (students leaving the library in the company of others tended to be reluctant to be delayed by an interview) or academic factors (e.g., a lecture or seminar class was imminent). These were seen as even handed random factors rather than sources of bias in the data. An uncertain cause of error in the data were associated with the methodology’s reliance on what students said rather than on direct observation. However, the reliability of selfreporting seems to have been rarely questioned in library user studies, and this source of potential error was not seen as of major significance.

“Interviewing itself was demanding, needing significant levels of both interrogative skills and professional knowledge to map students’ descriptions of their actions to the various service categories.” Interviewing itself was demanding, needing significant levels of both interrogative skills and professional knowledge to map students’ descriptions of their actions to the various service categories. Some of the problems were: ●

The interviewee would often appear to think visually (i.e., in terms of physical location within the building), rather than conceptually, in recounting the actions he or she had taken during their library visit. For example, “I went to Floor X to find some appropriate articles.” The interviewer would need to find out whether this meant that the subject (1) had browsed issues of printed journals on open access and/or used abstracting services to identify appropriate, but hitherto unknown, articles (thus using category S1.1 services); or (2) had prior knowledge of appropriate articles—from a reading list perhaps—and had attempted to obtain these from the local collection (category S2.1 services). The interviewer would also, more incidentally, need to discount the information about the named floor.

●

The interviewee was insufficiently specific. For example, “I searched for references checking the OPAC.” Did

this mean that the online catalog was searched for local holdings of previously known items (category S1.2), or was it used as a bibliography to identify relevant, but previously unknown, items (category S1.1)? The needs for verbal-behavioral interpretation, as in these examples, reinforced the view that interviewers for this methodology should have a detailed knowledge of information management practice, alongside general interviewing skills, if accurate data were to be obtained. Interviewing also needed to be fairly invasive to obtain the data wanted. Such difficulties can, nevertheless, be overemphasized because many of the subjects’ responses were categorized easily, as in “I went first to the basement for coffee;” “I used a computer to do some typing” (both services are in category S5); “I used a database to identify some current publications;” or “I went to the shelves and looked for some books for an essay [by] using the numbers on the shelves” (both of the services used here being in category S1.1). Systematic, as distinct from sampling, errors are inevitably present in a survey of library use. In the methodology concerned, these include those arising from (1) subjects’ miss or partial remembering of their actions during their library visit, and of the order of those actions, and (2) the need, in some cases, for the interviewer to decide whether to categorize the behavior as information seeking or information searching. For example, recalling the user who “went to Floor X to find some appropriate articles,” and assuming he or she used a catalog terminal on that floor for that purpose, how should such use be categorized when the user was working systematically through a class reading list? Should it be coded as S1.1 (documents sought that match some specification, such as a given author), S1.2 (holdings sought of specific documents), or even S2.2 (when the catalog is used to reserve a wanted item that is on loan)? In cases where the user’s behavior was innately complex, as here, the rule adopted by the interviewer was simply to try to identify the dominant mode of behavior. However, as with most, perhaps all, surveys of human behavior in complex settings, total consistency is unlikely to be attainable, any more than it is when classifying documents by subject. Data Analysis Table 2 shows the transition frequencies yielded by the survey when the

Table 2 Frequencies of Transition between Services (Broad Service Classification) Row sums

Column sums

Start

84

163

14

5

9

0

275

*

S1

98

9

0

4

3

114

*

17

S2

104

3

51

149

324

*

4

46

S3

3

22

70

145

*

5

6

0

S4

0

0

11

*

4

11

18

0

S5

53

86 *

*

*

*

*

*

*

Finish

*

114

324

145

11

86

275

Note: The cells show the numbers of transitions between the main service-categories, where the library visits involved the use of at least one information service (275 visits). The data are for both libraries.

broader service classification is used. This table excludes 34 EOUs where the user visited the library solely to use a utility service. (A separate table, not shown, in which these 34 EOUs were included was also constructed, on the grounds that utility usage of a university library reflects a university culture wider than that of a student’s course of study and also because such usage may bear on marketing policy.) The number of accesses to the five service categories, for any single EOU, had a maximum value of eight, a minimum of one, a median value of two, and a mean of 2.47. (If the 34 utility-serviceonly cases were included, the mean value decreased to 2.31.) Similar tables were constructed for the two libraries taken separately (Coach Lane Library, 136 visits; City Campus Library, 139 visits), again excluding student visits solely to use a utility service and also for several groups of students defined across both libraries and for particular subjects of study, namely, nursing and paramedical studies (30 students), biosciences (44 students), humanities (47 students), and education (41 students), with data from both libraries combined.27 Table 3 shows the transition frequencies under the more detailed scheme for classifying of library services.28 Under the broad service classification, service-use sequences for visits to the Coach Lane Campus Library were significantly shorter than those to the City Campus Library, using a one-tailed t-test to compare mean values (p ⫽.036). Using the same test, and after pooling data from both sites, library visits expressed as ser-

vice-use sequences were found to be significantly longer for biosciences students than for education students (p ⫽.017). Humanities students also had significantly longer service-seeking sequences than did education students (p ⫽.0012).29 However, although these results were significant, some caution is needed in drawing more qualitative conclusions, such as that biosciences and humanities students seek information more effectively than do education students. Although that conclusion could be true, other interpretations of the relative shortness of education students’ service-seeking sequences are possible. For example, education students may choose services in a more effective order than do biosciences and humanities students; their needs may be more easily met (e.g., they may be more dependent on monographs, rather than on journal articles that need to be identified by using online searches); that when they have chosen a service, their use of it is more effective than is that for other students; or that they are better supplied by their tutors with reading lists and shelf numbers, allowing them to bypass certain library services. To find valid explanations for effects such as this would require the methodology to be enhanced. The value of the present methodology to management, in this regard, is that it identifies differences in user behavior that suggest the need for further investigation and stimulates and focuses such questions. Table 4 re-expresses the frequency data in Table 2 as probabilities. The upper probability in each cell refers to the transition from the service in the same row as

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Table 3 Frequencies of Transition between Services (Finer Service Classification) Row sums

Column sums:

Start

68

11

5

56

52

55

14

5

0

9

0

275

*

S1.1

38

3

47

5

0

5

0

0

1

2

101

*

14

S1.2

0

33

2

0

3

0

0

3

1

56

*

0

2

S1.3

4

7

0

0

0

0

0

1

14

*

9

2

0

S2.1

78

1

40

1

1

4

13

149

*

2

0

1

2

S2.2

7

50

1

0

36

113

212

*

2

0

1

2

12

S2.3

0

0

0

16

31

64

*

4

0

0

2

43

0

S3

3

0

17

61

130

*

0

1

4

1

4

0

0 S4.1

0

0

0

10

*

0

0

0

0

1

0

0

0

S4.2

0

0

1

*

2

2

0

2

8

1

18

0

0

S5

53

86 *

*

*

*

*

*

*

*

*

*

*

*

Finish

*

101

56

14

149

212

64

130

10

1

86

275

Note: The cells show the numbers of transitions between the main service categories where, as in Table 2, the library visits involved the use of at least one information service, but where the finer categorization of services is recognized. As with Table 2, the data are for both libraries. Although the total number of visits is the same as for Table 2, the total number of service accesses is greater in view of the data recognizing more micro transitions, such as S2.1 to S2.2. Table 2 masks such transitions.

the cell concerned to the service in the same column as that cell (i.e., the Pr(Si3Sj Si) value, where Si here refers to the service in the row). The lower probability refers to the transition to the service in the same column as the cell concerned from the service in the same row (i.e., the Pr(Si3Sj Sj) value, where Sj refers to the service in the column).30 These might be thought of as recording posterior and prior influences, respectively. For example, given that the service currently used by a student is S1, the probability that he or she will next use a service in category S3 is 0.079 (i.e., 9/114), whereas if the service currently used is within category S3, the probability that the student will proceed to use a service in category S1 is smaller at 0.028 (i.e., 4/145). The relative attractiveness of these two service categories, so portrayed, suggests that services in S1 are accorded the higher status by the user. (The S1 services are relatively more attractive in the sense that traffic from S1 to S3 dominates traffic in the reverse direction: this category might accordingly be said to have the greater autonomy.) This view is confirmed if prior influences are examined. A student currently using a service in category S3 will have arrived there from the use of a

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service in category S1 with probability 0.062 (9/145), whereas a student currently using a service in category S1 will have arrived there from S3 with probability 0.035 (4/114). Again, S1 appears as the more important service category in users’ eyes, in the sense that use of it depends less on previous service-seeking behavior by the user. (Use of its services is less dependent on prior use of services in S3, than vice versa.) It is curious that the pattern of relative impact on and the pattern of relative influence by, when transitions between two service categories are examined, do not always promote the same service category. This can be illustrated by inspecting the relevant probabilities for S2 and S4. Users currently at S2 move ahead to S4 with a probability of 0.009 (the upper probability in the cell lying in the in the same row as S2 and in the same column as S4), whereas users at S4 move onward to S2 with a probability of 0.545 (the upper probability in the cell lying in the same row as S4 and in the same column as S2). This pattern points to S4 as the higher status category: it is more autonomous when this pair of categories is examined. However, the probability that users who are currently at S2 have arrived there from

S4 is 0.019 (the lower probability in the cell lying in the same column as S2 and in the same row as S4), which is smaller than the probability that users at S4 have arrived there from S2 (the lower probability in the cell lying in the same column as S4 and in the same row as S2), namely 0.273. This pattern now points to S2 as being the higher status category. (Use of services in S2 is less dependent on prior use of a service in S4.) The Appendix offers a more systematic approach to ranking service categories. The MPS for the dataset is the sequence 03S23100, readily apparent from Table 4.31 This service-seeking sequence also reoccurred under a variety of breakdowns of the data, providing a simple model of service-seeking behavior: a user behaving in the most probable manner, under this schema, will look for known items, then leave the library (without, for example, using a catalog or bibliography, seeking advice, or studying in the library). Under the finer service classification, the MPS has the more detailed form: 03S1.13S2.13S2.23100. Here, a user acting in the most probable way will first gather document metadata, look for specific documents, borrow or reserve

Table 4 The Sample Frequencies of Table 2 Re-expressed as Conditional Probabilities Start .305 .593 .051 .018 .033

.000

.737 .503 .097 .455 .105

.000

* *

S1 .052

.860 .079 .000 .035

.026

.302 .062 .000 .047

.014

S2

.149 *

.028 .317

.321 .009 .157

.460

.717 .273 .593

.542

S3

.035 .142 *

.455 .545 .000

.021 .152

.483

.273 .256

.255

S4

.044 .019 .000 *

.047 .128 .209 .000

.000

.000

.000

.000

S5

.616

.035 .034 .124 .000 *

*

*

*

*

.193 *

Finish

Note: See text for fuller details. For example the transition from S2 to S5 (denoted S23 S5 in the text), given that a user is currently using a service in category S2, has a probability of 0.157. In contrast, given that a user is using a service in category S5, the probability that he or she last used services in S2 is 0.593. These probabilities are interpreted in the text as connoting service impact and autonomy, respectively, and used to calculate a measure of authority for each service (RVS) given in Table 5.

Figure 2 Frequencies of Service-category Accesses (1)

items, and exit. As mentioned above, a rich variety of actual search patterns are disguised by these simple generalizations. Figures 2 and 3 illustrate frequencies of use of individual service categories (i.e., selective PM data). Figure 4 offers a visual expression of the main “attracted to” probabilities (the upper values in each cell of Table 4), and Figure 5 illustrates the main “influences on” probabilities (the lower probabilities shown in the table). In Figures 4 and 5, probabilities were required to be greater than 0.1 for services to be shown as connected. Lastly, Table 5 records the relative value of service (RVS) values for each of the five service categories, further to the definition of this performance measure given in the Appendix. For example, the value of the RVS of service category S3 was found to be 0.859 by evaluating: ●

The scalar product of the vectors (0.035, 0.142, 0.273, 0.256) and (1 ⫺ 0.062, 1 ⫺ 0.717, 1 ⫺ 0.000, 1 ⫺ 0.124), using the vectors of lower probabilities in row 4 and column 4 of Table 4, respectively, and excluding the values in row 1 and column 7 because these do not refer to services, to obtain the value 0.570;

●

●

The lengths of each of the above two vectors are determined by summing the squared values of their component probabilities, then taking the square root of those sums, giving lengths of 0.402 and 1.651, respectively; and Evaluating the expression 0.570⁄0.402⬘ 1.651.

Table 5 shows the rank values defined by these RVS values for each of the five service categories, along with the angle between the two vectors concerned (see Appendix). Services in category S2 (i.e., services providing direct access to documents) have a high rank value, which seems to be in accord with intuition and may suggest validity in the RVS measure. The next most valuable service category determined by the RVS measure was that of providing study facilities, S3, followed by advice-giving services, S4. In regard to the latter, it is intriguing that, although only about 4% of students’ visits involved seeking advice (and less than 1% of all visits involved the seeking of professional advice), this service category nevertheless ranks third in service authority. Some professional colleagues may regard the low status accorded to services supporting information retrieval, S1, on par with that of

Figure 3 Frequencies of Service-category Accesses (2)

the provision of utility services (S5), as counterintuitive. Possibly, this reflects (1) a student learning culture heavily dependent on tutors’ reading lists, browsing behavior, or standard textbooks for information delivery; (2) a lack of confidence in the use of relevant skills (and therefore inadequate user education); (3) inadequate provision of secondary literature, including online sources; and/or (4) congestion at service points and/or slow or unfriendly search software. However, RVS is a relative measure of the value of library services, not an absolute measure. Accordingly, this ranking of service categories may simply point to the higher value of the other services, as seen by these users, rather than either weakness as such in S1 services or any unwillingness or inability in students to carry out bibliographic searches. Tables of relative service value, such as Table 5, are essentially provocative of further thought and investigation.

CONCLUSION The characterization of library service use can be attempted from either side of the service desk. The PM approach to the description of library/user interaction, notwithstanding its value to information management, sees service usage from the service desk looking out and disregards the broader behavior patterns of users that generate service usage. Surveying those patterns, as in the methodology described here, offers a view of service use from the users’ side of the desk. As such, it provides a formal language for describing user behavior of the service-seeking kind and when applied will improve the librarian’s knowledge of what the system is that he or she is managing: a model of users and service usage. The effects of policy changes can then be seen in terms of both changes in user behavior and in PM data.

“Classification of information services is a necessary preliminary to capturing data on service-seeking behavior.” Classification of information services is a necessary preliminary to capturing data on service-seeking behavior. The difficulties in this regard stem from the need to define a classification that will distin-

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Figure 4 Probabilities of Main Movements to a Service-category

guish information seeking (defined here as a user’s decision to consult a particular service) from information searching (defined as information behavior at a given information service). Some arbitrariness in such a classification scheme seems inevitable, as it is when pursuing the information seeking versus information searching distinction. The capture and analysis of data so obtained to aid policy review seems preferable to basing reviews on unverified beliefs. Data on users and use can also identify myths, as well as convert beliefs into known facts, even if the main problems in this regard are in persuading one’s colleagues (and oneself) to write down relevant beliefs before a survey is undertaken and its results analyzed.32 Applying such a classification schedule in a practical survey presents its own problems. Defining EOUs is one, and it could be argued that the study took the easy way out in interpreting EOUs as visits by users to a library building, rather than in more information-need oriented terms. Capturing credible service-use data required professionally informed, skilled, and quite invasive interviewing as well as,

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at times, subjective arbitration by the interviewer to interpret a reported form of behavior as seeking or searching. The evolving, largely information technology focused concept of a library as a system that enables and facilitates access to information resources, rather than acts as a store of resources, strengthens the rationale of the methodology presented. However, at the same time, that newer concept introduces problems in data capture. Requests to a library, whether for advice, documents, or document descriptions, are increasingly sent electronically via terminals that lie outside the library’s physical province. The contraflow of advice, documents, and document descriptions are also increasingly being supplied electronically. Accordingly, data capture in the future needs to be situated closer to the natural habitat of the user (i.e., in the curtilage of the academic library, rather than more narrowly in the physical library building). There is virtue in this necessity, however, in that it puts increasing pressure on the librarian to see use in more user- and task-centered terms rather than from as service transactions. It might be said that

that is what librarianship should always have been about anyway. At the same time, a decision to focus studies primarily on the service user and his or her needs, as done in regard to service-seeking behavior, may mean abandoning the assumption that the user inevitably has a library of first resort. Although that seems a natural assumption to make in the context of universities, in other contexts (e.g., where the user is in a consultancy firm, or a smallscale manufacturing firm, or is simply a member of the public), the individual may well be using several libraries and several Internet providers for document identification and/or delivery. This may also be true in university settings, where users may be supplementing their use of the university’s own library with the use of electronic services provided by learned societies or of Web services accessed through portals provided by special interest groups, museums, research institutions, and so on. Future studies of serviceseeking behavior that are centered on the user will, therefore, have to be carefully designed to blend, on the one hand, the interests of stakeholder organizations that may see themselves as beneficiaries of the results of such studies, and on the other hand, the need to describe users’ usage of services of all types, whether or not delivered by any particular stakeholder organization. Our methodology did not attempt to sur-

Table 5 The Relative Value of Each Category of Information Service

Service category

RVS value for service category

Value of angle ␪ (°)

Rank value reflecting relative authority of servicecategory

S1

0.620

51.7

5

S2

0.900

25.8

1

S3

0.860

30.7

2

S4

0.689

46.5

3

S5

0.646

49.8

4

Note: The RVS construct (see Appendix) defines the authority of each category of information service relative to its companion services. It is defined as the normalized scalar product of two influence vectors associated with each service (vectors expressing the influence of the use of the servicecategory on use of other service-categories, and its immunity to influence from other servicecategories). The angle ␪ measures the similarity in direction (intuitively, the similarity in effect) of the two influence vectors concerned and is equivalent to the RVS value.

Figure 5 Probabilities of Main Movements from a Service-category

Acknowledgment: This study was funded in part by the University of Northumbria. We offer sincere thanks to students of the University who cooperated in our survey of their information-seeking behavior, and also to colleagues in the University’s Information Services Department who offered helpful criticisms of an early version of Figure 1. Helpful remarks were also passed by colleagues in U.K. academic libraries at a workshop, held in Newcastle in December 1999, on the methodology.

APPENDIX: RANKING SERVICE CATEGORIES BY RELATIVE AUTHORITY The transition probabilities can be combined, for a given service, to portray the overall authority of that service relative to that of other services, as implied by users’ service-seeking behavior. The scalar product of two vectors, both reflecting between-service influences provides such a measure. One vector is that which records the influences that a particular service Si has on the other services, namely, Pr共Si3S1兩S1), Pr(Si3S2兩S2), . . . , Pr(Si3SN兩SN) vey the experiences of users as subjectively accessible to them (e.g., their reason for choosing to use a particular service when they did, and why, following usage of that service, they chose the next service that they did). Useful enrichments of insights are likely to be given by less formal (i.e., qualitative) means. Other possibilities for extending the methodology may be offered by analyzing business/geography literatures for studies of human movement (e.g., studies of shopping behavior). The methodology might also be developed theoretically so as to assess memorylessness (in the sense of Markovian behavior) in information seeking within EOUs, and to clarify the criteria that lead to a user deciding to stop his or her service seeking.

“Transition tables and related diagrams can provide succinct summaries of users’ service seeking and add to the librarian’s armory of management tools.”

Lastly, transition tables and related diagrams can provide succinct summaries of users’ service seeking and add to the librarian’s armory of management tools.33 Statistics on the lengths of users’ serviceseeking sequences can be readily found, as can ad hoc measures of the relative values of different categories of service, such as the RVS measure and PM data in the form of relative frequencies of service use. Exploring the relationship between generic PM variables, such as overall satisfaction with the library at the end of an EOU or the use of individual services within that EOU using binary variables, would provide a further avenue of investigation. An intriguing area for research is that of the entropies associated with each row and column of the transition table and the effects on these of a change in service provision policy. At the present time, however, what would be most useful is having a much larger store of data from different libraries, whether university library based or based on other information systems, which could strengthen professional knowledge of what users actually do when they use a set of information services.

Here, Pr(Si3Si Si) is given the value 0 because the fine structure of user-behavior within a service category is disregarded by the methodology. This vector records the authority of the chosen service category, Si, as reflected by its being seen as a predecessor service relative to other services, that is, its “prerequisite” status. The other vector records the extent to which Si does not require the other services to be its own predecessors (i.e., the extent to which Si provides independent value to the library system of which it forms part). This is the vector: 1-Pr共S13Si兩Si), 1-Pr(S23Si兩Si), . . . , 1-Pr(SN3Si兩Si) These two vectors, associated with the ideas of “impact on other services” and “autonomy relative to other services,” respectively, embody different aspects of a single concept, which might be termed service authority. Two formal constructs, which combine the above two vectors into one singlevalued variable, are now described. One

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is their normalized scalar product, termed here RVS.34 Forming this product helps to define the joint effect of the two vectors for any given service category. Normalization makes it possible to compare different service categories (or compare the same service category for different libraries). This variable provides an overall figure of merit for a service category Si based on its relationships with the other services as defined by users’ serviceseeking behavior. In symbols, for each service Si, RVS is defined as follows:

System Design (Amsterdam: North Holland, 1979). 5. Smith & Rouse, “Application of Queuing Network Models to Optimization of Resource Allocation within Libraries.” 6. Ibid. 7. T. D. Wilson, “Models in Information Behavior Research,” Journal of Documentation 55 (June 1999): 249 –270. Wilson also acknowledges J. Rasmussen, A. M. Pejtersen & L. P. Goodstein, Cognitive Systems Engineering (New York: Wiley, 1994). See also T. D. Wilson “Information Behavior: An Interdisciplinary Perspec-

12.

A second formal construct, equivalent to RVS but expressing the essential notion geometrically, is the angle between the two vectors, that is, cos⫺1 (RVS). A highly authoritative information service, so defined, would then generate a small such angle, in view of the two vectors representing different aspects of the one concept. The RVS construct is, of course, only one of several measures that could be based on the vectors involved and that could provide simple, intuitive summaries of the interactions of users’ service-seeking patterns and the library’s services. (For example, the Euclidean distance between the two vectors might also be evaluated.) However, RVS appears to be the simplest such construct.

NOTES

AND

9. 10.

REFERENCES

1. Another example could be an organization’s adoption of a new policy of allowing staff free access to the Web from their desktops. This might be conjectured to change (plausibly increase) calls on its information system’s advice services in regard to Web search engine use and recommended portals generally, while possibly reducing the demand on in-house, network-based, mass memory resources. 2. J. MacGregor Smith & William B. Rouse, “Application of Queuing Network Models to Optimization of Resource Allocation within Libraries,” Journal of the American Society for Information Science 30 (September 1979): 250 –263. 3. Carol A. Seymour & J. L. Schofield, “Measuring Reader Failure at the Catalogue,” Library Resources & Technical Services 17 (Winter 1973): 6 –24. 4. R. J. Lano, A Technique for Software and

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tive,” Information Processing and Management 33 (1997): 551–572. For example, at a practical level, behavior forms such as the following can be observed: deciding to carry out a search for information resources, deciding to visit a particular system to search for resources, asking for professional advice in that connection, choosing a database host (then a database, then a search expression), scanning an initial retrieved set for informing items, refining a search expression, reading a book, and so on. Wilson’s ontology distinguishes and categorizes such actions. Wilson, “Models in Information Behavior Research.” Seen in this way, the acquisitions function is enabled by and embodied in the processes of selection, ordering, recording, and bookmarking and is just that, a function, not a service. The design of a user education course is also a process, not a service. However, the presentation of documents to users (e.g., books on shelves or past examination papers on an intranet server) is a service because users can access the items presented to them and also control what is presented to them by their physical behavior at the shelves or by menu choices at the intranet server. The delivery of a library induction course is also a service: users can opt for attending it or not, and if they do attend it, they can either listen to what is said or gaze out the window. Examples of sources focusing on performance measures are Joint Funding Councils’ Ad-hoc Group on Performance Indicators for Libraries, Effective Academic Library: A Framework for Evaluating the Performance of UK Academic Libraries (Bristol, U.K.: Higher Education Funding Council for England, and other bodies, 1995); Nancy A. Van House, Beth T.

13.

14.

15.

16.

17.

Weil, & Charles E. McClure, Measuring Academic Library Performance: A Practical Approach (Chicago, IL: American Library Association, 1990); Paul Kantor, Objective Performance Measures for Academic and Research Libraries (Washington D.C.: Association of Research Libraries, 1984); Ian Winkworth, “Performance Measurement and Performance Indicators,” in Collection Management in Academic Libraries, 2nd ed., edited by Clare Jenkins & Mary Morley (Aldershot, U.K.: Gower, 1999), pp. 71–105; International Federation of Library Associations, Section of University Libraries and Other General Research Libraries, Measuring Quality: International Guidelines for Performance Measurement in Academic Libraries (Mu¨nich, Germany: Saur, 1996). Given the natural continuity of cognition, the boundaries defining an information need may have to be set arbitrarily by the investigator. There is obviously much more to an information need—and to the person who perceives it to exist—than can be conveyed by a sequence of associated service use codes. Qualitative data, in the sense of weakly typed, could accordingly be sought on EOUs if richer studies are needed for management purposes. The balance to be sought seems to be that between the clarity of vision given by strong data typing and the richness of vision given by abandoning categorization. In the case of the survey reported later, qualitative data were not gathered because the primary aim was to test and demonstrate the methodology reported here. In that survey, EOUs were defined operationally by using visits to a library as expressions of information need. Carol C. Kuhlthau, “Students and the Search Process: Zones of Intervention for Librarians,” Advances in Academic Librarianship 18 (1994): 57–72. See, for example, the contributions of Ruth A. Palmquist & Kyung-Sun Kim, “Modelling the Users of Information Systems: Some Theories and Methods,” Reference Librarian 60 (1998): 3–25; and Maxine H. Reneker, “A Qualitative Study of Information Seeking among Members of an Academic Community: Methodological Issues and Problems,” Library Quarterly 63 (October 1993): 487–507. See, for example, Paul B. Kantor, “Availability Analysis,” Journal of the American Society for Information Science 27 (September/October 1976): 311–319; Kantor, Objective Performance Measures for Academic and Research Libraries; Richard H. Orr, “Measuring the Goodness of Library Services: A General Framework for Considering Quantitative Measures,” Journal of Documentation 29 (September 1973): 315–322. Baulking (i.e., not joining a queue for

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19.

20.

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22.

service) and reneging (i.e., leaving a service queue before reaching service) behaviors in regard to service queues are not apparent in PM data, although either form of behavior might sensibly form part of a study of use of an individual service. The sequence of use of services cannot be reconstructed from PM data. With PM data, weights could be associated with each PM variable, and a weighted sum of mean values calculated in an endeavor to provide some kind of system-wide measure of use. However, such weights would be management determined rather than user determined. In any case, their choice seems so arbitrary that little intuitive meaning would attach to such a sum. For example, the traditional catalog supports the bibliographical function (document-existence data) as well as documentcopy-held and document-copy-location functions. Modern catalogs may also support functions allowing the user to establish the status of document-copy loans, and the user’s own status as borrower, as well as a user-driven reservation function and even access to other libraries’ catalogs via the Z39.50 standard. That is, “Markovian.” See, for example, Fred S. Roberts, Chapter 5, in Discrete Mathematical Models (Englewood Cliffs: Prentice-Hall, 1976), pp. XX–XX. Peter Hernon, Danuta A. Nitecki, & Ellen Altman, “Service Quality and Customer Satisfaction: An Assessment and Future Directions,” Journal of Academic Librarianship 25 (January 1999): 9 –17. If finer detail in one or more service categories is recognized, the relevant transi-

tion matrix can be expanded to the required extent.

23. For a given cell (i,j), the two probabilities are related by: 24. Peter Brophy & Peter Wynne, Management Information Systems and Performance Measurement for the Electronic Library (Manchester, U.K.: Centre for Research in Library & Information Management, Manchester Metropolitan University, 1997), p. 1. 25. Although we had initially planned to link the two types of data together by using regression analysis, this aim was later rejected in view of both the possibility of confusing interviewees with questions of two distinct types and increasing the demands on their time to an unreasonable extent. 26. HEFCE (the Higher Education Funding Council for England) allocates government funding to English universities to support teaching and research. Its funding formula is sensitive to subject area. The funding codes for these areas are given in Annex A of the Council’s Assigning Departments to Academic Cost Centres (1997) [Online]. Available: (November XX, 1998). 27. Data are available from Michael Heine. 28. Table 3 was built from the primary data obtained in the survey. Table 2, the simpler table, was derived from the primary data after conflating service categories. For example, a subsequence, such as S1.2 3 S1.1, in the primary data would be reduced to the single item S1, with the number of service accesses accordingly reduced by 1.

29. Differences between sample means of sequence lengths for other groupings of students’ visits were not significant. 30. Estimates of standard errors for these probabilities, for the entire student population, are provided by the usual expression 公p(1 ⫺ p)/N, where N is the sample size, 275, and where the sample is assumed to be a simple random one. 31. Rather than choose the single most probable next service, a tree-structure of MPSs could be identified by thresholding the probabilities at some arbitrary value, for example, 0.33, and identifying variant “next services.” Doing this identifies 03S23S33100 as an alternative, probable, service trail. 32. In a seminar in Newcastle, colleagues were asked to state, before seeing the results of the study, whether the frequency distribution of library visits over lengths of service trails would be bell shaped, skewed positive, or some other shape. No one guessed correctly. (It was, in fact, skewed positive, that is, Zipfian in shape.) We had fared no better before seeing the results. Other questions appeared to confirm the fragility of some prior beliefs. 33. We would be pleased to generate and return transition tables and related output on behalf of persons interested in undertaking surveys of the type described, provided data files are sent to us in an appropriate format. Please contact Michael Heine for a description of the format. Confidentiality will, of course, be respected. 34. The vector cross product can be ignored as a possible construction because this was defined for only three vector coordinates (in this case, three service categories).

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