Helping relations between technology users and ... - IEEE Xplore

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IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT, VOL. 48, NO. 1, FEBRUARY 2001

Helping Relations Between Technology Users and Developers: A Vignette Study Shmuel Ellis and Marcie J. Tyre

Abstract—This paper describes a vignette study of help-seeking and help-giving by technology users and technical specialists in response to problems with technologies-in-use. It was found that decisions both to seek and to give help were affected by task variables, such as problem urgency and problem difficulty. We also found evidence that technology users’ decisions about help-seeking are not driven by opportunities to learn from specialists. In fact, users generally avoided the kind of direct, face-to-face interactions with specialists that could yield the richest learning opportunities, with more experienced users shunning such interactions most strongly. Technical specialists, on the other hand, showed a marked preference for forming intimate, face-to-face working relationships with technology users. Specialists saw these interactions as opportunities for gaining useful feedback on new technologies and how to improve them. The theoretical and practical implications of the findings are discussed. Index Terms—Help-giving, help-seeking, knowledge gap, learning, problem-solving, relative expertise, task difficulty, time urgency.

I. INTRODUCTION

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URING the last two decades, organizational learning has become an increasingly important topic among both scholars and practitioners [5], [17], [20], [33], [49]. As George Huber suggests, “organizations must [now] attend to learning with an intensity never before needed or imagined” due to increasingly complex and turbulent business environments [33, p. 822]. Accordingly, there has been an outpouring of research in recent years examining the meaning of organizational learning and exploring how it occurs in organizations. One important conclusion from this work is that organizational learning is an inherently social process. As Weick and Westley point out, “Learning is embedded in relationships or relating” [71, p. 446]. For learning to be “organizational,” individual discoveries must be exchanged and then built into shared assumptions and understandings [5], [64], [69]. This process has been variously described as mutual negotiation [70], story telling [18], [58], collaborative inquiry [5], and confrontation and contest [16]. In short, organizational learning involves sharing of knowledge, beliefs, or assumptions among individuals [64]. But how does such sharing occur? Clearly, it requires some means of bringing organizational actors together. Some of these means, most notably the creation of teams and workgroups, are relatively formal and have received considerable attention Manuscript received November 5, 1998; revised November 5, 1999. S. Ellis is with the Faculty of Management, Tel Aviv University, Tel Aviv, Ramat Aviv, 69978 Israel (e-mail: [email protected]). M. J. Tyre is with the Women’s Studies Department, Brandeis University, Waltham, MA 02454-9110 USA (e-mail: [email protected]). Publisher Item Identifier S 0018-9391(01)01643-9.

from researchers and managers [4], [38], [39]. But individuals also come together through more informal processes of helpgiving and help-seeking. Organizational actors often face problems that they cannot effectively resolve on their own. Hence, they need the help of knowledgeable others. For example, organizational actors frequently need to approach colleagues to seek ideas or feedback [1], get expert advice [24], solicit sponsorship and support [3], ask for explanations or guidance [52], or request feedback [6]. The need to call on organizational colleagues for assistance is likely to increase as organizations discard narrow job specifications and endow employees with greater responsibility for identifying and directing attention to problems. Indeed, various studies have been done about how people seek ideas [1], obtain expert advice [24], or ask for guidance in a new job [52]. Studies also investigate how people within organizations select and choose their communication media (face to face, telephone, letters and memos, bulletins and documents, or computer output). Communication media determine the richness of information processing and ultimately their relative advantage to organizational members [15], [44]. Bodensteiner [15] and Holland et al. [32] even suggest that the hierarchy of media is determined by feedback capacity, communication channels used, source, and language. Whereas face-to-face communication is the richest form of information processing, providing immediate feedback and allowing simultaneous observation of multiple cues (e.g., body language, facial expressions, and tone of voice), formal numeric documents are the poorest. Numeric information is most applicable for communicating simple, quantifiable aspects of organizations, as numbers lack the information-carrying capacity of natural language [21]. However, most, if not all, of these studies circumvent the social dimension of information flow within organizations or, in other words, ignore the problematics of help-seeking and help-giving inherent in those behaviors. For example, in a recent study, Nadler et al. [54] showed that the link between help-seeking and performance is curvilinear, namely, that when organizational members demonstrate a high level of helpseeking (from colleagues or superiors), they are considered to be less effective by their superiors. Help given by either superiors or colleagues can be regarded as a double-edgedsword, because it is associated with high costs (decrease in performance evaluations), on the one hand, and high instrumental benefits (problems resolutions), on the other hand. The present paper examines the issues of help-giving and help-seeking in problem-solving situations and the implications on learning from experience within organizations. One area in which help-giving and help-seeking can be especially critical for smooth organizational functioning is tech-

0018–9391/01$10.00 © 2001 IEEE

ELLIS AND TYRE: RELATIONS BETWEEN TECHNOLOGY USERS AND DEVELOPERS

nical problem-solving. For example, work by Orr [58], Hutchins [34], Weick [70], and Pentland [60], [61] illustrates that the technologies-in-use in organizations frequently prove intractable to users or to technicians, who must seek help through informal channels. Assistance in using or in repairing machines or tools can be critical both for organizational functioning and for individual learning. In the case of new technologies, users often have a large number of problems because of the immaturity or unfamiliarity of the technology, as well as shifts in the social and knowledge structures surrounding it [11], [45], [65]. Users may know a great deal about a particular application and their unique needs, but they must rely on technical specialists’ deep understanding of the technology when problems arise. Technical specialists, in turn, often cannot judge how well a technology fulfills field requirements and, therefore, must rely on users’ insights to test and refine new technologies as well as to generate ideas for future developments [66], [67]. This “fundamental asymmetry of knowledge” between technology users and specialists creates incentive for both parties to invest in helping interactions [59]. However, help-seeking and help-giving among such actors can carry complex costs as well as benefits. For specialists, the asymmetry of knowledge means that the very act of providing assistance can be instructive and even helpful, yet technical problem-solving is often time-consuming and a major potential distraction. Similarly, seeking help presents special problems for technology users. Specialist positions in research and development departments are sometimes considered as more prestigious or higher ranked than are positions of individuals who use the technology (e.g., manufacturing or service personnel). This can increase the social or psychological costs of seeking and receiving help [14]. Also, users and specialists may bring different, competing interests to a given problem, and they may have different objectives for solving it. For instance, specialists may see a complex problem as an opportunity to investigate and improve technology, whereas users may be more interested in finding a rapid solution to meet productivity goals. Thus, involving specialists in problem-solving may introduce intolerable personal costs and time delays for users whose productivity depends on availability of their technology. These factors suggest that users may refrain from seeking specialists’ help to resolve problems, and that technical specialists may avoid providing help to those who need it. We describe an experimental study in which we used theory on help-giving and help-seeking to examine interactions between users of new technology and technical specialists. In particular, we examined the factors affecting decisions by technology users to seek help and by technology specialists to give it. We also considered the nature of the desired helping relationships and the expectations for learning and problem-solving that both users and technical specialists bring to the interactions. II. RESEARCH ISSUES AND HYPOTHESES A. When Do People in Organizations Seek and Give Help? 1) Problem Urgency: Time pressure can have important effects on problem-solving in organizations [48], and it is likely to affect helping behaviors in organizations. The literature on

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organizational crises suggests that the presence of urgent problems can cause users to redefine their boundaries of acceptable behavior [43]. Whereas local norms may dictate self-sufficiency during normal times, urgent problems can change the rules so that actions to marshal outside support are considered necessary and even laudable. Also, research on information search suggests that when time pressure is high, people require less information [25] and demand clear-cut judgments [47]. Hence, when problems are urgent, help from knowledgeable others may be considered especially valuable and the instrumental benefits of help-seeking are likely to outweigh potential costs. Likewise, specialists are likely to view requests for help with urgent problems as more legitimate when users have time to find solutions themselves. The costs of not helping (in terms of lost organizational efficiency, user dissatisfaction, and possibly management sanctions) would therefore be considered high when problems are urgent. H1: The greater the urgency of a problem, the greater the likelihood of help-seeking and help-giving. 2) Problem Difficulty: In general, users are more likely to expect greater task-related benefits from asking for help and to perceive lower psychological costs when faced by difficult rather than simple problems. The reason is that the harder the problem, the lower the probability (or expectation) that users would (or should) be able to solve it themselves, and the more valuable expert help becomes. Further, admitting a need for help may be much more acceptable to oneself (and to others) when the problem is very difficult [22]. Specialists, in turn, are likely to view requests for assistance as more legitimate (and more interesting) when the problem is particularly difficult. Hence, we might expect a linear relationship between difficulty and helping. H2: The greater the problem difficulty, the greater the likelihood of help-seeking and help-giving. 3) Perceived User Expertise: Previous research suggests that an actor’s knowledge or competence level is likely to affect help-seeking decisions—the greater the gap in expertise between the help-giver and the help-seeker, the greater the help sought and the higher the help given [54]. This finding reflects the instrumental relationship between help-givers and help-seekers. The gap in expertise ensures higher relative benefits from this interaction than the cost of revealing their weakness in public. Some researchers (e.g., [51] and [55]) argue that asking for help presents a threat to self-esteem or perceived competence. Expert users may feel that they have less freedom to seek help than do novices, because seeking help would be inconsistent with their image as competent, independent actors [52], [72]. Researchers note that organizational newcomers have a license to ask “dumb questions” [63] or to request feedback [6], [7] in ways that would be considered inappropriate for established employees. Experts, by comparison, tend to refrain from seeking help even at the cost of effectiveness [17]. One may say that for experts, the more difficult the problem, the greater the legitimacy of seeking help. Besides, we may argue that competent and confident users may perceive higher gains from seeking help (in terms of

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learning new skills, developing new relationships, or related benefits) than may users with lower skill levels [26]. Whereas novices are sometimes able to learn what and how to do things, experts are capable of a more in-depth understanding of problems and solutions. Miyake and Norman [50], for example, find that experts tend to ask more questions than do novices, especially when the task is complex, because they are better equipped to formulate questions and to interpret responses. In other words, not only is it more legitimate for experts to ask for help when facing difficult problems, but also their capacity to absorb help when the issues at stake are complicated is greater than it is for novices. These findings suggest that the effect of expertise is probably moderated by the nature of the problem (i.e., its level of difficulty). More specifically, relatively novice users may be more likely than experts to seek help on easy problems because they feel free to ask “dumb questions.” When problems are very difficult, however, it is understood that even experts need assistance. Furthermore, the benefits of getting help with very difficult problems (not just solving the problem, but also gaining new knowledge or relationships through interaction with the developer) may be valued more highly by experts than by novices. Whereas tough problems provide intellectual challenge for experts, they are an excuse for novices to give up. H3(a): With easy problems, novice technology users request help more readily than do expert users. With difficult problems, experts seek help more readily than do novices. Although the concept of organizational citizenship behavior has been studied extensively over the last years [16], [41], and behaviors such as sharing and cooperating within and between organizations are considered highly important in the current business environment [56], [46], almost no research has been done on the phenomenon of help-giving (as opposed to helpseeking) in organizations, in general, and on the role of the relative expertise of the help-giver–help-seeker, in particular. It is obvious, though, that in organizational settings, where the two actors are often acquainted from past interactions, the seekers’ expertise could be an important variable [60]. One possibility is that specialists see requests from relative novices for help as more legitimate than experts’ requests because experts have more ability to help themselves, at least on relatively simple problems. This is in accordance with Nadler et al. [54] findings that the greater the gap of expertise, the greater the help sought and the help given. Still, some of the benefits of helping (such as gaining useful feedback or new ideas from users, or developing useful new relationships) are likely to be greater when users have a higher level of expertise, especially when problems are complex. This suggests that specialists’ propensity to provide help may depend on both the users’ expertise and the nature of the problem. When problems are simple, relative novices’ requests for help may be seen as more legitimate than relative experts’. By contrast, when problems are difficult, specialists may perceive greater benefits from responding to requests from experts than from novices. Very knowledgeable help-seekers are most likely to contribute new insights or ideas, and to help

minimize the time required to resolve a complex problem by providing useful observations or information. H3(b): With easy problems, specialists more readily help novices than do expert users, but with difficult problems, they more readily help experts than do novices. B. What is Exchanged in Helping Interactions? Clues, Collaboration, and Full-Service Solutions Researchers have explored the various methods actors use to elicit help or information (e.g., overt inquiry versus more subtle monitoring of others [6]–[8], [52]). However, in a situation in which one actor makes an explicit request for help from another, researchers have not addressed the nature of the helping relationship requested (or offered). Help-seekers may simply request a few clues that enable them to proceed independently toward resolving a problem, want to work jointly with a helper, or ask the helper to solve it. Similarly, helpers may want to keep their involvement to a minimum (provide a few clues or talk by telephone), may want to work jointly with the help-seeker, or may prefer to take over and solve the problem. The nature of the helping relationship can affect instrumental outcomes (whether the problem is solved, how long it takes) and may affect learning opportunities for both help-seekers [55] and help-givers [68]. It can also affect users’ and specialists’ attitudes toward each other (e.g., sense of other’s competence) and even toward themselves and their tasks (e.g., sense of own control over the situation) [9], [28]. Further, different types of helping relationships require different levels of time and effort investment from help-seekers and help-givers. Preference for a certain type of helping relationship is likely to be affected by task and interpersonal variables, among others. For example, because working alone from clues can be timeconsuming, we would expect that the greater the problem urgency, the more likely users would be to request specialists’ help in person, or even turn problem-solving over to the specialists altogether. High problem difficulty may also raise the perceived costs of seeking help in the form of ideas or hints alone, because such minimal inputs from specialists could prove insufficient. Similarly, specialists may be more willing to become directly involved in problem-solving when urgency is especially high (when there is no time to encourage users to find their own solutions) or when problems are particularly difficult (because users would be unlikely to solve such problems without the direct efforts of specialists). Hence, as either problem urgency or problem difficulty increases, help-givers would be more likely to extend help in the form of joint working relationships, or to accept full responsibility for resolving the problem, and be less likely to simply offer clues or hints to users. H4(a): As problem urgency increases, help-seekers are more likely to seek joint work or full solutions from specialists, and specialists are more likely to work jointly or to provide full solutions. H4(b): As problem difficulty increases, help-seekers are more likely to seek joint work or full solutions from helpers, and helpers are more likely to work jointly or to provide full solutions.


C. Learning from Helping Interactions Social interactions, in general, and helping exchanges, in particular, can be an important vehicle for learning within organizations [8], [14], [17], [59], [57], but not all exchanges provide the same learning potential. First, time pressure is likely to have significant effects on learning expectations, because it tends to constrain the information search through both experimentation and interaction. Therefore, working on urgent problems may yield less learning for both users and specialists, and that difference may be reflected in their expectations. Furthermore, because difficult problems are generally more complex and unusual than are easy ones, helping interactions involving difficult problems may be perceived as offering greater learning opportunities than do those involving simpler problems. It is also reasonable to predict that users will expect more learning when there is a large gap in expertise between themselves and their helpers. In contrast, situations in which the gap of expertise between specialists and users is small will elicit more learning opportunities for the specialists. They will be more willing to provide help in situations that offer an opportunity to interact with experts from whom they can learn. H5: The more difficult the problems, the greater users’ and developers’ expectations for learning. H6: The greater the gap of expertise between users and specialists, the higher the users’ expectations for learning. In contrast, for specialists, the smaller the gap, the higher their expectations. The nature of the helping relationship is also likely to affect learning. Specifically, joint work (as opposed to delegating the problem to a specialist or merely exchanging verbal clues) provides users with an opportunity to learn from specialists’ actions and explanations, while giving specialists the opportunity to understand users’ needs and to observe actual performance of the technology. Hence, both sides may expect greater learning benefits from joint work than from an exchange of hints, or solution of the problem by the specialist. In terms of information richness, understanding can be checked and interpretations corrected through this mode of interaction. Interpersonal messages include multiple clues starting from body language and facial expressions and ending with tone of voice and verbal expressions. Furthermore, joint work on problem-solving involves two learning methods, experiential learning and learning from others. On the one hand, it enhances involvement, and on the other hand, it facilitates data analysis [10], [26]. Finally, it should be noted that according to Nonaka and Takeuchi [57], direct interaction on the plant floor is the major means of transmitting implicit knowledge and an excellent medium for transforming implicit into explicit knowledge and vise versa. H7: Help-givers and help-seekers expect more learning when working jointly than when simply exchanging clues, or when helpers undertake unilateral problemsolving. Another way of examining actors’ learning expectations is by exploring the objectives for entering into a helping relationship. For help-seekers, the primary objective is straightforward: to resolve the problem being faced (even though they may have secondary goals, such as learning from or forming a relationship

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with the specialist). The question of why help-givers respond to requests is not so easy. They may be primarily interested in instrumental goals (solving the user’s problem) or motivated by learning opportunities. Learning could stem from gathering feedback about the problems encountered by users or from acquiring new ideas about potential products or services that users would value. Incentives for providing help may vary with differences in the type of problem and the situation. As time pressure increases, so does the need for resolution or “closure” [47]. More urgent problems may force the specialists to focus on simply solving the problem rather than on gathering feedback or picking up new ideas. Furthermore, because difficult problems tend to be relatively rare and complex, and possessing interesting new features, specialists are more likely to be interested in gaining feedback or new ideas when addressing difficult problems than with easy ones. Similarly, feedback or new ideas are more likely to be prominent objectives for specialists when working with expert technology users than when working with novices who are unlikely to provide many new insights. H8: Help-givers are more interested in gaining feedback or ideas (as opposed to simply solving the problem) when problems are less urgent or more difficult, or when the help-seekers are relatively knowledgeable. III. METHOD A. Research Design Although we wanted to understand helping patterns among actual technology users and specialists, we were also interested in establishing causality. We, therefore, decided against a traditional field study and instead undertook an experimental “vignette study” (for example, see [62] and [73]). This approach has the advantage of capturing the actual preferences of technology users and specialists in a real organizational setting, while enabling us to experimentally control other possibly confounding variables. The limitation of this approach is that it is not an observational study of help actually being sought and given. However, we assume that subjects’ attributions to the target persons were reliable because of their intimate acquaintance with the types of issues presented, vividness of the vignettes, and anonymity of questionnaires. The vignettes were two-page stories describing a factory in which a problem with a recently installed process machine, had just been discovered. The machine was complex and automated, designed by a developer working in an in-house technology laboratory. The story ended with the question of what to do next. Vignettes were developed after extensive field work at the manufacturing site selected for the study. To maximize verisimilitude, we wrote the vignettes in consultation with technical and manufacturing employees at the site. It should be noted that in the chosen site, machines developed in the research and development department were transferred to the manufacturing plant floor. Although the development engineers were generally accessible, users (process engineers) refrained from calling them for help. Either they did not ask for help at all or they consulted with local engineers who were less knowledgeable or experienced with the machines than were

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the developers at that time. Such behaviors caused directly or indirectly massive damages. Three variables were manipulated in different versions of the factorial design. Problem diffivignette to form a culty was adjusted by changing the description of the problem: “a circuit board needs to be replaced” (easy); “the machine is losing tolerance but some possible causes have been identified” (medium difficulty); and “the machine is losing alignment and, given the novelty and complexity of the equipment, diagnosing and fixing it are expected to be exceptionally challenging” (very difficult). Problem urgency was manipulated by changing the need for current production: either “any schedule slippage could cost the company market share” (high urgency) or “the product there is not much won’t be introduced for several months time pressure” (low urgency). User expertise was manipulated still by describing the user either as “very little experience a novice” (novice/low expertise) or “competent, knowledgeable deep understanding excellent reputation” (high expertise/expert). The developer was described as highly knowledgeable in all scenarios. In a help-giving or help-seeking situation, the two actors play an equally important role. Help given or sought is not the result of the perceived expertise of one of the two, but of the perceived gap between them [18]. Therefore, it was decided to measure their relative expertise, which was affected, of course, by the experimental manipulation. The relative expertise of the user–developer pair was computed by subtracting the subject’s rating of the developer’s technical expertise (on a 1 to 7 scale included in the questionnaire) from his or her rating of the user’s technical expertise (also on 1 to 7 scale). Using gap scores turned the expertise variable from a manipulated into a measured variable. This is a limitation for an experimental study. We believe that from a theoretical perspective, however, that the gap measure better reflects the helping relations between the help-giver and the help-seeker. The vignettes presented to technology users and specialists were essentially the same, the major difference being whether the subject was assigned the role of developer or user in the story situation. Manipulations were the same, and users and specialists were confronted with exactly the same machine problems. The machine breakdown story was followed by a set of questions about help-seeking (or giving) intentions. Three questions tested subjects’ propensity to seek or to give help. As these questions were designed to represent different aspects of the same construct, the responses were aggregated into a dependent variable representing overall willingness to give or to seek help. The three elements of willingness (to give or seek help) were lag time before giving or calling for help, cost the subject would bear giving or calling for help, and confidence that giving/seeking help was justified. The three questions for the developers asked how quickly (in days and hours) they would respond to the user’s request for help, how certain they were that the decision to provide help was justified (1 to 7 scale), and how much effort they would expend in providing the help requested (four choices, from “do not get involved at all” to “get directly involved in solving the problem”). The -coefficient for reliability for the help-giving scale was 0.75. Factor analysis (principal components method) performed on the help-giving three-item scale revealed, as expected, one factor with eigen-

value above one (2.06). This factor explained 68.56% of the scale variance. The three questions for the users asked whether they would call for help or would work on the problem themselves (1 to 7 scale, “definitely call” to “definitely not call”), whether they would invest funds if necessary to get the help (1 to 7 scale, “definitely yes” to “definitely no”), and how long (days/hours) would they be willing to work on the problem themselves before calling for help. The -coefficient for reliability for the help-seeking scale was 0.73. Factor analysis (principal components method) performed on the help-seeking three-item scale revealed, as expected, one factor with eigenvalue above one (2.26). The factor explained 75.26% of the scale variance. Users and specialists were also asked about their expectation of learning from working on the problem. Answers ranged from 1 (“No, this is very routine work”) to 7 (“Yes, I see real learning opportunities”). The last question probed the kind of help users preferred and the kind of help developers were willing to offer. Users were asked to describe the message that they would leave for the developer in case he or she was not available. There were three alternative answers, one requesting the specialist to come to the plant and resolve the problem by him/herself, a second requesting that the specialist discuss the situation with the user over the phone, and a third asking the specialist to come work on the problem together with the user. In the same way, specialists were asked the best way to respond to the user’s call for help: first, just to call and discuss the problem over the phone; second, to go to the plant and begin work by him/herself; and third, to go to the plant and work together with the user to diagnose and solve the problem. B. Research Sites and Subjects Vignettes were administered to actual technical specialists (engineers responsible for developing new production processes and equipment) and users (manufacturing personnel responsible for using such equipment) in a major U.S. manufacturing company, who had been working in either capacity for an average of eight years. In most cases, subjects came from the same divisions and factories in which we conducted related fieldwork; so we had an exceptionally thorough understanding of their normal working context. (Different subjects were used for fieldwork and experiments.) Moreover, in most cases, users and specialists who served as subjects had actually worked together (or were currently working together) on a new technology project and, thus, had experience in exactly the same situations posed by the vignettes. In short, every effort was made to make the experiment reflect real experience. In total, 162 technology users and 162 technical specialists participated in the vignettes, which were administered on company premises to groups of five to 15. Variations in the variables of interest were distributed randomly. IV. RESULTS A. Analytical Approach Analysis of variance was used to examine the determinants of help-seeking and help-giving, as well as to investigate the type of helping relationships preferred and actors’ expectations about


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TABLE I WILLINGNESS TO SEEK HELP AS A FUNCTION OF PROBLEM DIFFICULTY, PROBLEM URGENCY, AND USERS’ RELATIVE EXPERTISE

TABLE II SPECIALISTS’ WILLINGNESS TO PROVIDE HELP AS A FUNCTION OF PROBLEM DIFFICULTY, PROBLEM URGENCY, AND USERS’ RELATIVE EXPERTISE

learning. Trend analyzes and analytical contrasts were also performed to facilitate understanding of the effects of problem difficulty (three levels) and its interactions with other independent variables. Loglinear analyzes were applied for categorical dependent measures. For each dependent variable, we report parallel results for both users and technical specialists in parallel. B. Manipulation Checks Manipulations were checked by analysis of variance. Results showed that all manipulations, except expertise, were successful (main effects of difficulty on problem’s perceived difficulty, of urgency on problem’s perceived urgency, and of expertise on perceived user’s expertise were all highly significant ( ). As already noted, in the following discussion, the experimentally manipulated variable is not expertise, but specialist expertise minus user expertise, as perceived by the subjects. The difference between scores for low (below-median) relative expertise and high (above-median) relative expertise was also ). highly significant ( C. Propensity to Give or to Seek Help The means and standard deviations of propensity to seek and experimental situations are disgive help under the played in Tables I and II.

1) Problem Urgency: As expected, according to our first hypothesis, problem urgency had a significant and consistent effect on both users’ help-seeking and on specialists’ help-giving behavior. Increased problem urgency led to attribution of greater willingness to seek help (to users) and to give help (to specialists). Specifically, attributed users’ willingness to seek help was stronger under high urgency ( ) than under low ur; , , partial gency ( ). Attributed specialists’ willingness to provide help was ) than under low urstronger under high urgency ( ; , , partial gency ( ). 2) Problem Difficulty: The effects of difficulty on helping behaviors were slightly more complicated. First, problem difficulty had only a moderate main effect on users’ attributed , , partial willingness to seek help ( ). Specifically, according to our second hypothesis (H2), the research participants thought that users’ propensity to ) seek help would be stronger with difficult problems ( ) or easy problems than with moderately difficult ( ; ). Contrary to our expectations, partici( pants did not differentiate between moderately difficult and easy problems. In contrast to our second hypothesis, problem difficulty had no effect on the likelihood to provide help among spe-

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TABLE III USERS’ PREFERENCES FOR TYPE OF HELPING INTERACTION AS A FUNCTION OF PROBLEM URGENCY

TABLE IV USERS’ PREFERENCES FOR TYPE OF HELPING INTERACTION AS A FUNCTION OF USERS’ RELATIVE EXPERTISE

cialists, i.e., attributed willingness to give help did not increase with problem difficulty. 3) User–Developer Relative Expertise: As expected, the statistical analysis yielded a significant interaction effect of difficulty and relative expertise on propensity of specialists to , , partial ). give help ( According to the second part of our third hypothesis (H3b), under easy problems, subjects who were playing the role of specialists indicated they would provide help more readily to relatively novice users (high perceived gap of expertise, ) than to relatively expert ones (low perceived gap ). In contrast, when problems were of expertise, moderately difficult or (to a slight extent) very difficult, subjects empathetic with the specialists were more willing to provide help to relatively expert users (low perceived gap of expertise, ) than to relatively novice users (high perceive gap ). Only with highly difficult problems of expertise, were they willing to provide almost the same amount of help , and to novice and expert users (relatively experts, ). relatively novices, The disordinal interaction effect of problem difficulty and relative expertise on propensity to seek help was also significant , , partial ). The direction ( of this interaction, however, did not confirm our expectations, as expressed in the first part of the third hypothesis (H3a). For easy problems, relatively expert users (low perceived gap of ex) were actually perceived as slightly more pertise, willing to seek help than were novices (high perceived gap of ), but on very difficult issues, they were expertise,

perceived as considerably less willing to seek help ( versus ). D. Types of Helping Interactions Results regarding preferences for different types of help-seeking behaviors were surprising. First, users in general were perceived as showing a distinct preference for arm’s-length helping interactions that kept specialists away from their equipment rather than more direct interaction. More than half the sample (62.28%), across levels of problem urgency, relative expertise, or problem difficulty, reported that they would rather seek clues or ideas over the telephone and less than one-third (30.81%) wanted to work on the problem jointly with the specialist. Surprisingly, only 10.69% wanted to , get a ready-made solution from the specialist ( ). The loglinear analysis demonstrated that users’ attributed preferences for particular forms of help were strongly influ, ). enced by the level of urgency ( According to our fourth hypothesis (H4a), and as reflected in Table III, users under high problem urgency, as opposed to low urgency, were perceived to be more than twice as likely to want to work jointly with helpers or to look for full solutions (delegate problem-solving responsibility to the specialist). Furthermore, only about 39% of the users under high urgency preferred to receive clues or ideas over the phone. In contrast to H4a, and as reflected in Table IV, no significant relations between type of helping interaction and problem difficulty were found.


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TABLE V SPECIALISTS’ PREFERENCES FOR TYPE OF HELPING INTERACTION AS A FUNCTION OF PROBLEM DIFFICULTY AND PROBLEM URGENCY

TABLE VI USERS’ EXPECTED LEARNING AS A FUNCTION OF PROBLEM DIFFICULTY, PROBLEM URGENCY, AND USERS’ RELATIVE EXPERTISE

Although it had not been hypothesized, users’ attributed preferences for structuring the helping interaction were also found to be affected by their level of expertise (See Table IV). The preference for arm’s-length interactions (clues or ideas by telephone) was especially strong among relatively expert users (65.85% versus 51.42%). Expert users were also perceived as more willing to delegate problem-solving to specialists than were novices (13.41% versus 7.14%). Novices, on the other hand, were perceived as almost twice as willing to work jointly with the specialist as were experts (41.42% versus 20.73%). In contrast to users, subjects adopting the role of specialists (across experimental conditions) indicated a distinct preference for working jointly with the user to help resolve technical problems (72.2%) rather than stepping in to solve the problem alone (12.3%), or providing clues or ideas over the telephone , ; see Table V). The pref(15.4% erence for joint work was robust to all types of problems, but was especially pronounced, as expected, for very difficult problems when time pressure was also high (interaction effect of problem urgency and difficulty on type of help was significant; , ).

E. Helping Exchanges as Opportunities for Learning The means and standard deviations of subjects’ perceptions of users’ expectations about learning from problem-solving are presented in Table VI. First, according to our fifth hypothesis (H5), it was found that users’ expected learning depended heavily on problem difficulty ( , , partial ). Users’ mean rating of expected learning was 4.97 under low difficulty, 5.91 under moderate difficulty, and 6.38 under high difficulty. Second, an unexpected significant urgency by rela, , tive expertise interaction was found ( ). In particular, subjects identifying with partial relatively expert users (low perceived gap) believed the target persons would learn more when problems were more urgent versus ), but subjects empathizing with ( relatively novice users (high perceived gap) did not think so and , respectively). Finally, a signifi( ; ; cant triple interaction was found ( ), meaning that the urgency by relative expertise interaction changes under the three levels of problem difficulty. More specifically, as can be seen in Table VI, under low and

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TABLE VII DEVELOPERS’ EXPECTED LEARNING AS A FUNCTION OF PROBLEM DIFFICULTY, PROBLEM URGENCY, AND USERS’ RELATIVE EXPERTISE

TABLE VIII USERS’ EXPECTED LEARNING AS A FUNCTION OF TYPE OF HELPING INTERACTION

moderate levels of difficulty, subjects emphathetic with relatively expert users expected for more learning under low urgency than under high urgency whereas subjects empathetic with relative novice users, expected for more learning under high urgency than under low urgency. In contrast, when problems were difficult, subjects emphathetic with relatively expert users expected for more learning under high urgency than under low urgency, whereas subjects empathetic with relative novice users did not distinguish between the two situations. The means and standard deviation of subjects’ perceptions of specialists’ expectations about learning from problem-solving are presented in Table VII. According to our hypothesis (H5), developers, like users, expected a more learning as a function of problem difficulty , , partial ). The mean ( rating of expected learning for developers was 5.52 under low difficulty, 5.36 under moderate difficulty, and 5.41 under high difficulty. Also, subjects higher expectations of learning attributed to specialists when urgency was high than when versus ; ; urgency was low ( , partial ). Although the data analysis did not yield a significant effect of users’ relative expertise, an unexpected interaction of users’ relative expertise and problem , ; partial difficulty was found ( ). More specifically, whether problems were easy or difficult, subjects empathetic with developers expressed higher expectations for learning when the help-seeker was relatively versus and versus novice ( , respectively). By contrast, when problems were moderately difficult, more learning was expected when the versus 5.26). Fihelp-seeker was relatively expert (

nally, a significant triple interaction was found ( , , ). We can see the main source of this interaction in Table VII: whereas under easy and urgent problems, or difficult and less urgent problems, developers expect a more learning from interaction with relatively novice users than with relatively expert users, under moderately difficult and urgent problems, developers expect a more learning when they interact with relatively expert users. F. Types of Helping Interactions and Learning Expectations In order to test the seventh hypothesis (H7), we ran a one-way analysis of variance in which type of learning interactions was the independent variable and learning expectations was the dependent variable. The means and standard deviations are presented in Table VIII. It was found that subjects playing the role of users did not express different learning expectations under the different types of interactions with the helper (communicate by telephone, work jointly with the specialist, delegate problemsolving to the specialist). Also, specialists’ attributed preferences for the type of helping interaction (by telephone versus joint work with the user versus working on the machine alone) did not directly affect their learning expectation from problem-solving. The means and standard deviations of specialists’ attributed preferences for the type of helping interactions are displayed in Table IX. However, goals attributed to specialists concerning the helping interaction did relate to their learning expectation. Specifically, the more subjects perceived that specialists value feedback from the helping interaction, the more they attributed


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TABLE IX DEVELOPERS’ EXPECTED LEARNING AS A FUNCTION OF TYPE OF HELPING INTERACTION

TABLE X SPECIALISTS’ GOALS FOR HELPING INTERACTION AS A FUNCTION OF PROBLEM DIFFICULTY, PROBLEM URGENCY, AND USERS’ RELATIVE EXPERTISE

them with expectations for learning ( ; ). The association between gaining new design ideas and expected ), but still highly significant learning was weaker ( ). Specialists, as reported by the subjects, did not ( associate the importance of simply fixing the machine with ; ). learning opportunities ( G. Help-Givers Goals We also examined the goals specialists set for the helping interaction. Were they simply interested in getting the machine running (providing a solution), in gaining feedback from the user on the design of the machine, or in gaining new ideas from the user that might be helpful in a future design project? The relevant means and standard deviations are given in Table X.

We treated the questions relating to each of these three objectives as three levels of a within-subject independent variable, thus, creating a split-plot factorial design with three between-subject factors and one within-subject (repeated measures) factor. The subjects’ ratings on the three seven-point scales were used as the dependent variable. Analysis of variance yielded a significant main effect of specialists’ goals for helping , , partial ). exchanges ( Specifically, gaining user feedback was the most important ) attributed goal for specialists. It was more important ( ; ) or than gaining new design ideas ( ; ). A significant simply solving problems ( disordinal interaction was found between specialists’ helping , , partial goals and problem urgency ( ). As reflected in Table X, no differences were found

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between specialists’ attributed interest in collecting feedback and ) and ideas from users ( ( and ). However, urgency did increase considerably their attributed focus on getting the equipment fixed. Under high urgency, subjects adopting the role of specialists rated getting the equipment fixed as the specialists’ most important objective under high urgency versus under low ( ). Surprisingly, no clear differences in urgency; developers’ attributed goals resulted from differences in users’ relative expertise or in problem difficulty. V. DISCUSSION The objective of the study was to increase our understanding of helping interactions in organizations. We asked how certain task variables (level of difficulty, urgency, and expertise gap between helpers and helpees) influence the willingness of organizational actors to seek and to give help. We also examined how differences in the nature of the problem and in the nature of the helping behavior (or lack thereof) affect perceived opportunities for learning from problem-solving activities. The results provide insights into user and specialist perceptions of how and whether they want to learn. The bottom line of our results is surprising and hints at a potential systemic organizational problem deriving from the difference between user and specialist perceptions that may block knowledge acquisition and knowledge creation within organizations. Specifically, whereas specialists appreciate learning and are aware of where and when to acquire knowledge, users, as perceived by the subjects, are not aware of the learning opportunities embedded in the various modes of interaction with technical specialists. According to our first hypothesis, we found that subjects attributed greater willingness to seek and to provide help to the target persons when problems were urgent than when time pressure was low. Secondly, according to our second hypothesis, we found that problem difficulty is positively related to helpseeking and providing. Thirdly, according to our third hypothesis, we found that the effects of problem difficulty are dependent on the relative expertise of the potential help-seekers and help-givers involved. For specialists, the direction of results was generally as expected: on easy problems, technical specialists were perceived as considerably more willing to help novice users than relatively expert users, whereas on difficult problems, specialists were perceived as more willing to help expert users (this preference was much more pronounced on moderately difficult problems than on very hard ones). This result supports the idea that specialists may consider difficult problems as potential learning opportunities, and they may see expert users as better learning partners than novice users. Expert users (compared with novices) are probably perceived as a potential source of knowledge: on the one hand, we may learn from them, and on the other hand, working with them may decrease the time required to solve especially difficult problems. Results for technology users on this question were different. Contrary to our first two hypotheses, on easy problems, relatively expert users and novices displayed almost equal willingness to seek help, but on difficult problems, expert users were perceived as considerably less likely to seek help than were novices. This

finding seems odd, because we would expect that more expert users, compared with novice users, would be able to cope with straightforward problems on their own. Further, expert users should be aware of their limitations in addressing very hard problems and see such problems as opportunities for learning through specialist interventions. In light of these results, it seems that for expert users, solving tough problems by themselves is more of a professional challenge and opportunity to excel and to gain appreciation than an opportunity to learn. Asking for assistance may threaten this opportunity. If this is true, the findings reflect a major cultural problem within organizations, namely, that organizational members are driven primarily by personal ambition and professional competitiveness, resisting the sharing and cooperation that embody the infrastructure for learning and knowledge creation in organizations [46]. Furthermore, expert users are ready to risk loosing face [53] and to ask for assistance when problems are easy, but they cannot afford to seek help when problems open a window to excellence and recognition. The results relating to the types of helping interactions preferred by users and specialists, and the learning expectations associated with such interactions, are generally consistent with the results on willingness to provide or to seek help. Users and specialists neither share the idea that help-giving and help-seeking interactions are opportunities for learning nor do they believe that they accelerate competition. Unexpectedly, subjects indicated that, in general, users avoid working directly with specialists whose help they required, preferring to limit inputs to clues or ideas that could be communicated by telephone. This means that their help-seeking behavior is motivated by the desire to complete their assignments rather than by their motivation to learn. With urgent problems, instead of decreasing the problem-solving time by working directly with the specialist, they demonstrated increased preference for arm’s-length assistance. This finding was, as expected, in accordance with H4a. However, in light of the other results, we may view it now from a different angle: even under urgency, there are users who are willing to sacrifice the benefit of decreased problem-solving time rather than experience direct interaction with an expert specialist. Finally, had motivation to learn influenced the preference for particular type of help, problem difficulty would have an effect on their choice. The fact that contrary to H4a the statistical analysis did not yield such an effect, consolidates our interpretation of users’ behavior. Of particular notice is the finding that relatively novice users were perceived as considerably more willing than were experts to work jointly with a specialist helper (even though they still preferred arm’s-length interactions). Experts were perceived as showing a distinct preference for limiting their interactions with helpers to telephone conversations. Again, only users that do not see themselves at the same level of expertise as the specialists can afford to ask them for help. Experts were also perceived as more willing than were novices to simply delegate problem-solving responsibility to specialists. This suggests that expert users are foregoing important learning opportunities afforded by working directly with technical specialists—either to preserve their autonomy or because short-term considerations such as time costs of joint work seem more important than do long-term issues such as knowledge acquisition.


In contrast to the emerging picture of users’ help-seeking behavior, the pattern of results regarding specialists’ help-giving behavior was generally in accordance with our expectations. More specifically, specialists express a definite and consistent desire to avoid arm’s-length relationships, preferring instead to enter into joint working relationships with users who need their help. According to H4a and H4b, working directly with the user, as reported by the research participants, was preferred in all situations examined, particularly when problems were urgent and, above all, when both urgency and difficulty were high. It seems that unlike users, specialists probably do not perceive their relationships with users as competitive; thus, they are willing to work closely with them on the plant floor to solve current technological problems. For specialists probably feeling more secure about their organizational status or expertise, working closely with expert users does not pose a risk. On the contrary, it provides an opportunity for learning. The findings regarding user and specialist expectations about the learning potential inherent in their helping interactions generally support the logic of our interpretation of user and specialist preferences. As opposed to our H7, there was no association between users’ attributed expectations about learning whether or not they would seek help, or the type of helping relationship they would choose. Instead, their attributed expectations about learning opportunities varied only with problem difficulty (as predicted by H5, more learning was expected with more difficult problems) and, for some users, with problem urgency and difficulty. More specifically, when problems were difficult, subjects did not perceive novice users as distinguishing between urgent and nonurgent situations in terms of their learning potential. However, they perceived relatively expert users to feel that they would learn more when problems are urgent. This finding seems to contradict our intuitive notion that the time pressure associated with urgent problems would make learning difficult. It seems, though, that expert users expect that when faced by urgency, specialists will be more willing to share their knowledge or simply be inclined to deliver complete solutions for their problems. By contrast, specialists, as perceived by the research participants, were much more likely to associate helping interactions with opportunities for learning. Like those of users, attributed specialists’ learning expectations varied with problem difficulty (more learning with more difficult problems, as expected) and problem urgency (more learning with greater urgency, which was not expected). Also, specialists, according to the subjects’ reports, predicted that working with more expert users in helping interactions would yield more learning, especially on moderately difficult problems. More accurately, when problems are moderately difficult, specialists expect a more learning when approached by novices with urgent problems. When approached by relative experts, they expect far more learning when users are facing low urgency. These results further indicate their willingness to learn from interactions with users. Whereas easy problems have low potential for enriching specialist knowledge, difficult problems may trigger stress among users that can block mutual learning. Moderately difficult problems and expert users, or moderately difficult but not urgent problems with novice users, are probably the best conditions to

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facilitate learning. An even stronger indication of specialists’ receptivity to learning opportunities is that they were perceived to believe that user feedback from technologies-in-use is the most important objective in providing help. Indeed, they were perceived as considering gaining feedback as even (slightly) more important than solving the problem at hand. (Even when this was not true, under high problem urgency, the reason was not that the rated importance of gaining feedback decreased, but only that the importance of finding a solution increased with greater problem urgency.) Moreover, the more specialists were perceived to value helping as a mode of gaining feedback (or as a way of gaining new ideas), the more learning they were perceived to expect. Thus, the evidence indicates that specialists recognize and act on (or at least intend to act on) learning opportunities inherent in helping interactions. They value the feedback received from such interactions, and they understand that learning is likely to vary according to the energy invested into eliciting such feedback. They are willing to invest time and effort to work jointly with users who might provide such information, and they (often) turn to relatively expert users as richer sources of learning. A senior executive in a multinational high-tech organization once told the first author of this paper, “We are running a modern advanced organization that uses top of the line information systems. In our organization, members are updated on any issue in no time. But, all super advanced technologies prove to be useless when one needs a superior’s support or a colleague’s technical assistance. In the long run, the cost, in dollars, of the reluctance to seek help is much beyond what you can imagine.” In the short run, reluctance to seek help may cause delays in production or development and even immediate damage. In the long run, reduced helping (especially face-to-face) interactions hinder the processes of knowledge transformation and transmission [54], [26]. In the current hypercompetitive business environment where knowledge turned out to be an important organizational resource, it is very hard to convince people to give or to seek help. In other words, it is a tough mission for managers or organizational consultants to reduce organizational actors’ perceived cost of help-seeking [54]. There are three ways to increase help-seeking or help-giving. The first way is to strengthen the interpersonal relations between potential helpers and helpees. People tend to offer help to people they know, to people they like, to people with whom they share similar opinions or attitudes, or to people that helped them in the past [29], [40], [23], [2], [31]. People also tend to seek help if they feel that they can reciprocate their helpers in the future [30]. Interestingly, research on altruism has consistently shown that people who are induced to be in positive moods are more likely to help others [13], [35], [37]. In more general terms, positive affect consistently brings greater sociability and benevolence [36]. The second way is to increase organizational commitment and strengthen norms of generalized reciprocity [12], [19]. This can be done, for example, by implementing a mentoring system [42]. Because training and providing professional and social support are integral part of the mentors’ organizational role, people feel more comfortable to approach them with requests for help and guidance. The third way is to create other organizational mechanisms for knowledge dissemination.

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In cases in which it is difficult to motivate organizational actors to seek or to provide help, managers must create mechanisms to encourage knowledge dissemination within their organizations. Such mechanisms include cross-functional teams, informal meeting opportunities, regular discussions to solve current problems, job rotations, increased incentives for knowledge sharing, and so forth [57], [56]. Although these mechanisms prove to be useful for knowledge sharing, managers should remember that the importance of help interactions extends beyond this particular role. Help-giving and help-seeking reflect an important facet of the organizational learning culture—cooperation and benevolence. A. Limitations of the Study As previously noted, we have chosen to use the vignettes method to test our hypotheses. Having made this decision, we defined both the advantages and limitations of our study. In order to understand the causal relationships between task characteristics and patterns of helping among technology users and specialists, we sacrificed the richness of the data that could have been gathered via a field observational study of help actually sought and given. We included only three independent variables in the study and omitted important others (e.g., problem importance, various sources of help, availability and accessibility of these sources, kind of technology) from our experimental design. Furthermore, in order to increase the internal reliability of our experimental design, we had to jeopardize the external validity of the experiment by using, for example, one basic experimental setting taken from a single site. Indeed, individuals from different sites may behave differently in various helping interactions, and they may be influenced by various combinations of situational variables. However, the important question that should be raised here is whether this variance is systematic or just random noise. After numerous visits to many organizations and in-depth conversations with many users and developers, we tend to believe that the phenomenon of imbalance between willingness of developers to provide help and hesitation of users to ask for it is pervasive. We believe that the best way to detect important phenomena or influencing variables is through field observational studies, but the best way to understand them is in laboratory or field experiments. Effective use of various research methods will yield the best results. REFERENCES [1] J. T. Allen, Managing the Flow of Technology. Cambridge, MA: MIT Press, 1977. [2] P. R. Amato, “Personality and social network involvement as predictors of helping behaviors in everyday life,” Social Psychol. Quart., vol. 53, pp. 31–43, 1990. [3] D. Ancona and D. F. Caldwell, “Beyond task and maintenance,” Group Organization Studies, vol. 13, pp. 468–494, 1988. [4] L. Argote and J. E. McGrath, “Group processes in organizations: Continuity and change,” in International Review of Industrial and Organizational Psychology, C. L. Cooper and I. T. Robertson, Eds. New York: Wiley, 1993, vol. 8, pp. 333–375. [5] C. Argyris and D. Schon, Organizational Learning II: Theory, Method, and Practice. Reading, MA: Addison-Wesley, 1996. [6] S. J. Ashford, “Feedback-seeking in individual adaptation: A resource perspective,” Acad. Manage. J., vol. 29, pp. 465–487, 1986. [7] S. Ashford and L. L. Cummings, “Feedback as an individual resource: Personal strategies of creating information,” Organizat. Behavior Human Performance, vol. 32, pp. 370–398, 1983.

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Shmuel Ellis is a Senior Lecturer in the Leon Recanati Graduate School of Business Administration, Tel Aviv University. He received the Ph.D. degree in social psychology from Tel Aviv University in 1985. During the years 1985–1988 he was on the faculty of the Open University of Israel and during the years 1990–1992 he was a Visiting Assistant Professor at the Sloan School of Management, MIT. His academic interests center on the relations between uncertainty and mechanisms of organizational learning, processes of organizational knowledge creation and management, and drawing lessons from experience. S. Ellis published books in analysis of variance and experimental designs and papers in refereed journals like “Human Relations,” “Journal of Applied Social Psychology,” “Public Opinion Quarterly,” “Leadership Quarterly” and “Journal Ergonomics” and IEEE TRANSACTIONS ON ENGINEERING MANAGEMENT.

Marcie J. Tyre holds the M.B.A. and D.B.A. degrees from Harvard Business School, and the B.A. in economics from Wellesley College. From 1988 to 1999 she taught management of technology at the MIT Sloan School of Management, where her research focused on the issues that users face in dealing with new technology. Tyre is now a Resident Scholar at Brandeis University in the Women’s Studies Scholars’ Program. Her current research centers on issues of pregnancy and mothering in the United States.