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The Study Context Background

The Study Context Background
The Study Context Background

COVER SHEET

This is the author-version of article published as:

Gable, Guy G. (2000) The alignment of client and consultant views. In Proceedings Adminstrative Sciences Association of Canada Conference, pages pp. 67-76, Montreal, ONT.

Please enter the information about this item. Fields mark Accessed from https://www.wendangku.net/doc/df17987268.html,.au

ASAC-IFSAM 2000 Conference Montreal, Quebec

Canada

Guy G. Gable Information Systems Management Research Centre

Queensland University of Technology

THE ALIGNMENT OF CLIENT AND CONSULTANT VIEWS Gable [1996a] validated a multidimensional measurement model of client success when en-gaging external consultants to assist with selection of computer based information systems.

Following on from that study and employing the same data, this paper seeks to compare cli-ent and consultant views on the seven model dimensions and to interpret disparities.

Background

The Study Context

The study unit of analysis is the computer based information system (CBIS) selection project. Firms studied are registered clients of the Singapore Local Enterprise Computerisation Program (LECP), a Singapore Government Program to encourage and assist local businesses to become more competitive through the adoption of information technology [Gable and Raman, 1992a]. By using LECP firms, certain potentially confounding variables have implicitly been controlled for (e.g. con-sultant minimum experience, client expectations). In addition, other factors, important to the study, are also beneficially homogenous (e.g. all are computer system selection projects, all projects in-volve an external IT consultant). All LECP projects involve two main players; the client project manager and the client's chosen consultant. The consultant's role in the selection project is to conduct feasibility and system studies, to develop system specifications and to evaluate and recommend a software house. The definition of a consultant is implicit in the LECP minimum registration criteria. The consultant must have: a tertiary or professional qualification in an IT related field; a minimum of 8 years work experience in executing or managing feasibility study, planning, analysis, design, de-velopment or implementation of information systems; and detailed references for at least three recent consulting projects in these experience areas. The consulting team may include but may not be lim-ited to junior consultants with a minimum of 3 years relevant work experience, and who satisfy the other two criteria. Reasons for focusing the study on the CBIS selection project are several. The selection project is characteristic of many engagements where the consultant is engaged to assist only with the identification of a solution. Also, it is important to distinguish between selection failure and implementation failure. It may seem tautological that a more successful selection project will yield a higher level of implementation success, yet a well-selected and good solution may be imple-mented badly. Conversely, effective implementation may compensate for a poor CBIS solution. Thus, it is important to assess consultant engagement success prior to commencing implementation of the consultant's recommendations in order to decide: (1) whether to proceed, (2) how to proceed (e.g. planning implementation strategy), and (3) whether to retain the consultant further (e.g. to man-age implementation).

The Study Model

Based on the literature and case studies, Gable [1996a] identified the seven correlated dimen-sions of client success with consultant engagement reflected in Figure 1. The model makes a primary distinction between results success and performance. Results include the consultant's recommenda-

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ASAC-IFSAM-2000 Proceedings 2tions and improvement in client understanding, thus yielding three main areas of engagement success assessment. Each of these three areas of assessment can be measured more or less objectively versus subjectively. The relatively more objective measures for recommendations, understanding, and per-formance are usage/acceptance of the consultant's recommendations, change in client understanding,and actual versus estimated resource requirements, respectively. The more subjective measure for each of the 3 areas of assessment is the client's level of satisfaction. Following are briefly described each of the model dimensions (see [Gable, 1996a] for detailed discussion).

Recommendations Acceptance : In management science/information systems implementation research, usage refers to usage of the information system or model implemented. In the context of a CBIS selection consultancy usage refers to the extent to which the client 'uses', accepts, or intends on acting upon the consultant's recommendations. Recommendations Satisfaction : Given the client's investment in the consultant's recommendation (e.g. the consultant's fees and client resources), usage or acceptance may not be entirely voluntary. A client may act on the consultant's recommendations or feel compelled to act, yet not be satisfied with the 'fit' of the recommended solution. It is thus important to also measure the level of client satisfaction with the consultant's recommendations.Understanding Improvement : Improved client understanding can facilitate more effective imple-mentation and greater independence. Also, to the extent that the client is better equipped to conduct future, similar projects with reduced external assistance, they are better off. Understanding Satisfac-tion : Regardless of how much (or little) the client's understanding has improved, they may be more or less satisfied with the level and adequacy of their understanding. Performance Objective : Per-formance Objective is the degree to which actual project resource and time requirements equal those originally estimated. Performance Reasonability: While the intention at the outset, for both the client and the consultant, may be to bring the project in on time and on budget, as the project unfolds con-tingencies may dictate deviations from the original plan. Staff turnover in the client firm, improving client understanding of requirements and changing client needs due to a rapidly changing external environment, are all contingencies largely beyond the control of the consultant. Performance Rea-sonability is intended to measure consultant performance in light of these contingencies. Perform-ance Satisfaction : Regardless of consultant time and cost performance, the client may be satisfied or dissatisfied with consultant performance. The client may be dissatisfied because they feel that the consultant did not give value for money, did not demonstrate necessary expertise or experience, or Recom m endations

A cceptance Recom m endations Satisfaction Understanding Im provem ent Understanding Satisfaction Perform ance Reasonability Perform ance Satisfaction Perform ance Objective

did not keep the client adequately informed. Clients can also be satisfied despite poor sched-ule/budget performance. To the extent that the client is satisfied with the consultant's overall per-formance, the engagement can be considered more successful.

The Study Methodology

The study commenced with a single exploratory pilot case followed by a more explanatory, cross-case analysis of five firms [Gable, 1991]. The case studies aided in model building, operationaliza-tion and validation of model constructs, and interpretation of study findings. In order to test the ‘a prior’ model, all firms registered with the LECP were surveyed. Separate questionnaires were mailed to clients and consultants. All survey instrument items are seven-point Likert-like scales (all instru-ment items are consistently counter-intuitive with, for example, smaller scores (e.g. 1) indicating a higher level of success, and larger scores (e.g. 7) indicating a lower level of success). All dimensions of the a priori model are measured from the client's perspective. Recommendations Acceptance is measured for each of the three main components of the consultant's recommendation: hardware, software, and a vendor. Client Understanding Improvement is the difference between client under-standing at the start and the end of consultant involvement. Understanding Improvement and Under-standing Satisfaction are measured for both the selection process and for information requirements. Consultants were felt to be adequately informed and objective to assess three of the model dimen-sions: Performance Objective, Understanding Improvement and Recommendations Satisfaction. Client response to the mail-out was 80% (67 of 85 clients who had completed selection - responses were received from 69 clients, two of which, for the purposes of this study, were unusable due to missing data). Thirty-two of 35 consultants surveyed, representing 21 consulting companies, re-sponded regarding their involvement in 78 (92%) of the 85 completed selection projects. Approxi-mately half of the projects were handled by 'Big6' consulting companies (The world's six largest audit/consulting companies. Big6 firms represented in the survey sample include: Arthur Andersen & Co., Coopers & Lybrand Associates Pte Ltd, Deloitte Haskins + Sells, Ernst & Young Pte Ltd, and Price Waterhouse).

Criterion validity. Factor 'based' scores [Gorsuch, 1983] were derived for each dimension measured from client data (21 items) and for each dimension measured from consultant data (10 items), through summing the Z-scores of those items which loaded heavily on a given factor (Standardising the scores removes the effects of individuals who generally give either high or low responses. Note that all subsequent analyses reported herein, were also repeated using raw scores, yielding highly similar results). This yielded the model depicted in Figure 1. Besides items referenced thus far, crite-rion measures of overall success were also elicited from clients and consultants. Results of paired T-tests of these items indicate no significant differences between the two respondent groups. Signifi-cant correlations and similarity across means, standard deviations and standard errors are also ob-served. These data evidence the validity of the criterion data items. With the objective of further assessing the content, construct and criterion validity of the factor solution (factor-based constructs), four composite measures of overall success were computed as follows: (1) Client Dimensions Aver-age is the simple average of the seven client dimensions of success. (2) Consultant Dimensions Av-erage is the simple average of the three consultant dimensions of success. (3) Combined Dimensions Average is the simple average of the Client Dimensions Average and the Consultant Dimensions Average. Simply averaging the Client and Consultant Dimensions Averages to yield the Combined Dimensions Average has the effect of weighting equally the client and consultant views, even though more client items are associated with more client success dimensions than for the consultant (the correlation coefficient for the Client and Consultant Dimensions Averages is .453, significant at the .001 level). (4) Combined Criterion Average is the simple average of Z-scores for the client and consultant success criterion items. Individual client and consultant success dimensions and the com-ASAC-IFSAM-2000 Proceedings3

posite constructs (1, 2 and 3 above) were correlated with the individual client and consultant success criterion items and their average (4 above). This method of validation assumes the criterion scores are valid; thus the extent to which each dimension or dimension average correlates with the criterion scores, is evidence of their criterion validity [Kerlinger, 1988]. Results of this analysis are reported in Table 1.

From Table 1, generally strong correlations with the three criterion scores are observed. In addition, correlations between the Client, Consultant and Combined Dimensions Averages (11, 12, and 13 in Table 1) and their respective criteria are large and significant thus further evidencing the criterion validity of both the dimensions averages and their component dimensions. This is also evidence of the additivity of the dimensions. Additionally, it is observed that the Combined Dimensions Average and the Combined Criterion Average correlate more strongly (r=.75) than do the corresponding measures for the client (r=.62) or consultant (r=.62), thus suggesting that client and consultant views sometimes reflect divergent bias and when summed they may be compensating.

Construct validity. Following is first summarised, evidence of the validity of the individual success dimensions and second, evidence in support of combining the dimensions to yield an overall measure of Success (which also further evidences the validity of the individual dimensions). The validity of the individual success dimensions was demonstrated primarily through factor analysis. The factor analysis results were quasi-confirmatory in that: (1) the a priori relationship between instrument items and success dimensions was specified; (2) a subset of success dimensions included in the a priori model correspond approximately with measures employed in previous research (i.e.. usage, performance and satisfaction); (3) success dimensions identified from factor analysis corresponded broadly with the a priori Success measurement model; (4) the three dimensions identified from con-sultant data corresponded directly with a subset of the seven dimensions identified from client data thereby validating a subset of the client factor structure in two independent samples; and (5) as pre-dicted, significant positive correlation among the dimensions was observed (demonstrated follow-ing). Strong correlation was observed between client and consultant dimensions and their respective success criterion (Table 1).

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Table 1 - Correlations Between Client and Consultant Success Constructs

and Client and Consultant Success Criterion Items

Client Consultant Combined

Mean Std Dev Criterion Criterion Criterion Client Success Dimensions(1)(2)[(1)+(2)]/2

1RA Recommendations Acceptance 3.13 2.060.28**0.38***0.41***

2RF Recommendations Satisfaction 3.66 1.320.65****0.26**0.59****

3UI Understanding Improvement 2.260.300.31**0.040.19*

4US Understanding Satisfaction 3.21 1.430.63****0.29**0.59****

5PR Performance Reasonability 3.14 1.380.19*0.43****0.38***

6PS Performance Satisfaction 2.99 1.460.61****0.33***0.6****

7PO Performance Objective 4.560.970.180.070.16 Consultant Success Dimensions

8RF Recommendations Satisfaction 3.240.970.150.61****0.46****

9UI Understanding Improvement(client) 3.150.700.24**0.32**0.35***

10PO Performance Objective 4.910.860.42***0.32**0.47**** Overall Measures of Success

11(a)Client Dimensions Average 3.250.880.62****0.37***0.63****

12(b)Consultant Dimensions Average 3.770.560.41***0.62****0.63****

13(c)Combined Dimensions Average 3.530.630.62****0.59****0.75****

Notes: **** p < .001 *** p < .01 ** p < .05 * p < .10

a) 11 = average of 1 to 7. b) 12 = average of 8 to 10. c) 13 = average of 11 and 12.

(reproduced from [Gable, 1996a])

Evidence in support of combining the success dimensions to yield an overall measure of success, included: (1) all client dimensions in the final model loaded on a single factor in second-order factor analysis, (2) the same was true of consultant dimensions, and (3) of all the success constructs, the overall measure showed the strongest associations with its hypothesised explanators [Gable and Sharp, 1992b] and predictors [Gable, 1996b]. As regards these last three points, it is observed that the Success construct has been previously validated in three models (the Success Dimensions Model [Gable, 1996a]; the Success Process Model [Gable and Sharp, 1992b]; and the Success Prediction Model [Gable, 1996b]).

Comparative Analysis

Associations among the model dimensions were analysed suggesting important implications. Table 2 is a matrix of correlations among the seven client dimensions and the three consultant dimensions. ASAC-IFSAM-2000 Proceedings5

Table 2 - Success Dimensions Correlation Matrix

(1)(2)(3)(4)(5)(6)(7)(8)(9)(10) CLIENT DIMENSIONS:RA RS UI US PR PS PO

(1)Recommendations Acceptance RA1

(2)Recommendations Satisfaction RS.23*1

(3)Understanding Improvement UI.40***.28**1

(4)Understanding Satisfaction US.32**.52****.47****1

(5)Performance Reasonability PR.31**.31**.34***.33***1

(6)Performance Satisfaction PS.47****.55****.56****.63****.50****1

(7)Performance Objective PO.29**.04.23*.36***.15.37***1

CONSULTANT DIMENSIONS:RS UI PO

(8)Recommendations Satisfaction RS.16.01.01.16.45****.17.071

(9)Understanding Improvement UI.24**.09.03.01.28**.33***.11.37***1

(10)Performance Objective PO.20*.32**.13.30**.49****.25**.13.19*.051 Note: **** p < .001 *** p < .01 ** p < .05 * p < .10

Correlations among client dimensions. The upper left quadrant of Table 2 reflects correlations among client success dimensions. Of the 21 client dimension pairs, 7 are significant at the .001 level, 5 at the .01 level, 5 at the .05 level, 2 at the .10 level, and 2 are non-significant. The generally strong, positive correlations among the dimensions is evidence of convergent validity. The fact that the observed correlations are substantively less than 1.0 is evidence of discriminant validity. In other words, this evidence suggests the dimensions represent separate but correlated measures of success (Gable [1996a] further demonstrated, through second-order factor analysis, how the dimensions load on a single overall measure of success).

The observed lack of correlation between Performance Objective and Recommendations Satisfaction seems sensible. One might expect that adhering too rigidly to initial time and budget estimates could yield a poorer fit than otherwise. The client pair with the second lowest correlation is Performance Objective and Performance Reasonability. Clients appear to clearly discriminate between the 'rea-sonability' of the consultant's performance in light of project contingencies, and their more objective assessment of consultant performance relative to initial estimates of project duration and fees (Per-formance Objective).

Correlations among consultant dimensions. Next looking at correlations among the consultant dimensions (lower right quadrant of Table 2), it is observed that Performance Objective and Under-standing Improvement are not significantly correlated. It is also observed that the corresponding cli-ent pair is among the weaker of the client correlations (r=.23). This makes intuitive sense. Where a consultant must work to an unrealistic schedule or budget for whatever reason (e.g. they have under-quoted, the client has stretched the project scope, or the consultant is ineffective), client education on requirements and the process is likely to be neglected and project documentation may be minimised -all of which reduce client understanding. It is observed that, for both clients and consultants, Per-formance Objective generally has the weakest correlations with other dimensions.

Correlations between client and consultant dimensions. The consultant dimensions have rela-tively stronger correlations with two client dimensions (lower left quadrant of Table 2): Performance Reasonability (r=.45, .28, .49) and Performance Satisfaction (r=.17, .33, .25). The strong correlation observed between the consultant's Performance Objective dimension and the client's Performance Reasonability dimension (r=.49) suggests that consultants, rather than objectively scoring their actual performance against their original estimates, report how 'reasonably' they performed taking account of project contingencies (e.g. client changes to information requirements or project scope, or prob-lems with resource availability). Referring to Table 1, it is noteworthy that two client dimensions, ASAC-IFSAM-2000 Proceedings6

Recommendations Acceptance and Performance Reasonability, have stronger correlations with the consultant success criterion than with the client criterion. The discrepancy is especially large for the client's Performance Reasonability dimension (r=.19 versus r=.43) implying that the consultant, in assessing overall client success (as measured by the consultant's criterion item), places relatively greater emphasis on the Performance Reasonability dimension than does the client.

The client Performance Satisfaction dimension is observed to be correlated at the .001 level with all other client dimensions except Performance Objective (.01 level). It is also significantly correlated with two of the three consultant dimensions and was the first client factor extracted in factor analy-sis, explaining 37.3% of the variance in the factor model. These data suggest that Performance Satis-faction may be the 'least misleading' single-dimension, surrogate measure of overall success. Interestingly, though the client's measure of Recommendations Satisfaction shows significant corre-lation with one of the three consultant dimensions (at the .05 level), it shows no correlation with the consultant dimension of the same name (r=.01). Thus, while the client and consultant measures of Recommendations Satisfaction are derived from broadly the same items [Gable, 1996a], they are un-correlated. Referring to column three of Table 1, it is observed that the client dimension having one of the largest correlations with the Combined Criterion Average is Recommendations Satisfaction (r=.59). The consultant Recommendations Satisfaction dimension also scores high against the Com-bined Criterion Average in Table 1 (r=.46). These findings suggest that both the client and consult-ant place significant emphasis on this dimension in their overall assessment of success, although they appear to have quite different views on the goodness of fit of the recommendations (Table 2). Similar observations to those made in the preceding paragraphs for Recommendations Satisfaction can be made for the Understanding Improvement dimension. Thus, while both clients and consultants again attribute significant importance to client Understanding Improvement (Table 1), they score this dimension quite differently (Table 2).

Discussion

Reconciling client and consultant views. Client and consultant scores for Recommendations Satis-faction and Understanding Improvement were found to be uncorrelated (r=.01 and .03 respectively in Table 2) and several explanations suggested. The Combined Criterion Average and Combined Di-mensions Average (Table 1) were observed to correlate more strongly (r=.75) than either the corre-sponding measures for the client (r=.62) or the consultant (r=.62), thus suggesting that client and consultant views may reflect divergent bias and when summed they may be compensating. Research conducted by Hammond [1974] suggests a possible further explanation for observed disparity be-tween client and consultant scores of the dimensions. Hammond examined the interface between the manager [client] and the management scientist [consultant] on engagements where a relatively un-structured problem requires a one-shot decision by the manager (not unlike a CBIS selection proj-ect). He identified eight factors that differentiate prototypical managers from management scientists: (1) goal orientation, (2) time horizon, (3) comparative expertise, (4) interpersonal style, (5) cognitive style, (6) problem definition, (7) validation of analysis, and (8) degree of structuredness required. Hammond found that managers concentrated on the sub-goals in their area of responsibility while the management scientists tried to maintain a more holistic or organisational perspective on issues. Man-agers also tended to be more pragmatic and concerned with the end-product than the management scientists who approached problems from a normative perspective with concern for the means by which the ends can be realised. The managers' time horizon for the analysis was shorter than that of the management scientists and the management scientists had greater expertise in formal decision making techniques. They also were found to differ in the interpersonal style that each brought to a ASAC-IFSAM-2000 Proceedings7

problem. While both types demonstrated a task orientation, the managers also tended to give greater priority to maintaining good relationships within the peer group. Management scientists tended to be the more explicit and consequently more narrow in defining problems and more analytical in their approach to problem solving. The managers validated an analysis in very intuitive ways while the management scientist relied on an examination of the internal logic of a model for validation. Fi-nally, management scientists tended to require a higher level of structuredness in comparison to managers.

Hammond goes on to observe that these 'sharp' differences between the management scientist and the decision maker constitute a main obstacle to the success of the engagement. This suggests that the level of client success will be lower the more divergent are client and consultant views. In order to test this proposition, the absolute value of the difference between the client and consultant success criterion items was used as a proxy measure of divergence between client and consultant views. This difference was then correlated with the client criterion item, the consultant criterion item, and the Combined Criterion Average, yielding r's of 0.39, 0.41 and 0.49 respectively, all significant at the .01 level, thereby supporting the proposition. Thus, observed differences between client and consultant views are further explained and the criterion validity of the model is further supported (it is reiterated that all instrument items are consistently counter-intuitive with smaller scores i.e. 1 indicating a higher level of success and larger scores i.e. 7 indicating a lower level of success, thus accounting for the positive correlation observed). These results further highlight the importance of aligning client and consultant goals for the engagement and the yardsticks employed in assessing progress towards goals.

The three consultant dimensions correlate most strongly with the client dimensions Performance Reasonability and Performance Satisfaction, suggesting that consultants attribute significant impor-tance to these two areas of client concern. This may also suggest that consultants should increase their emphasis on the other four dimensions of client success.

Data from both Tables 1 and 2 suggest the importance the client implicitly attributes to Understand-ing Improvement. The level of client understanding of their information requirements and of the selection process are important elements of client independence. Yet, as was observed in the case studies [Gable, 1991a], few of the case firms or consultants paid more than cursory attention to Turn-er's [1982] 'higher' intangible goals of the project: build consensus and commitment, facilitate client learning, and improve organisational effectiveness (all of which correspond closely with the dimen-sions - Understanding Satisfaction and Understanding Improvement). From the case studies and discussion with LECP administrators, it would appear that many clients are unwilling to explicitly pay for these intangibles, and perhaps for this reason (and because they are often the most difficult to provide), consultants are often unwilling to explicitly deliver them. The consultant's ability to facili-tate improvement in the client's understanding (to transfer knowledge) should be an important con-sideration in consultant selection. Clients who recognise the value of this less tangible dimension and yet attempt to squeeze consultants for as much informal education as possible, do the consultant a disservice and risk compromising the project outcome. They also risk jeopardising a potentially valuable ongoing relationship with the consultant.

It was earlier maintained that it is important to assess selection success before commencing installa-tion in order to decide: whether to proceed; how to proceed; and whether to retain the consultant further.

Whether to proceed. Those dimensions expected to have the greatest influence on the client's deci-sion to proceed or not with implementation are Recommendations Satisfaction and Recommenda-tions Acceptance. It seems tautological that one should accept and proceed with a solution that offers a good fit, ceteris paribus, and not proceed with an ill-fitting system. Though some correspondence is ASAC-IFSAM-2000 Proceedings8

observed between the client's assessment of Recommendations Satisfaction and their level of Rec-ommendations Acceptance (r=.23 in Table 2), this is a weaker association among the client dimen-sions suggesting that clients may sometimes feel compelled to accept a less than satisfactory solu-tion. Reasons for such phenomenon identified from the case studies include: inappropriate local software offerings ("it is the best of what is available"), a reluctance to commit energy and resources to repeat or extend the selection project as necessary, or a sensed need to proceed with a satisficing solution as a learning experience or as an interim measure. Alternatively, clients may sometimes feel constrained not to proceed with a solution that appears to offer a good fit (e.g. due to changed or-ganisational circumstances). The client's level of acceptance of the final recommendations is often difficult to predict. From the cases, reasons given for not fully accepting the consultant's recommen-dations included: (1) We never expected to follow their recommendations, but were using the project as a learning experience; (2) The whole exercise was simply a show for staff, and (3) Organisational circumstances changed (e.g. management had become preoccupied with other concerns, business had worsened, key staff changes had occurred). These phenomena may also explain the relatively small correlation observed between Recommendations Acceptance and Recommendations Satisfaction. The consultant has little control or influence over these contingencies and often the client will guard closely their hidden agenda (e.g. they may be reluctant to reveal significant worsening of business). In fact, the consultant is sometimes employed as a scapegoat. In all cases, it is suggested that honesty and openness offer the greatest potential for a mutually beneficial outcome; the consultant is thus better able to assist the client with their hidden agenda and a face-saving solution may be arrived at in the case of worsened circumstances.

How to proceed. Where the client decides to proceed with implementation, dimensions that may suggest a need to review the implementation strategy proposed by the consultant and vendor, are Understanding Satisfaction and Recommendations Satisfaction. A poor level of Understanding Satis-faction suggests that the client should seek improved understanding of their requirements and the proposed solution before commencing implementation. This can be achieved through further discus-sion with the proposed vendor, further software trials, general computer literacy training, and thor-ough review of existing systems and information requirements. Where a decision is made to proceed with implementation even though the proposed solution is not perceived to offer a good fit, measures can be taken to supplement the software and ameliorate its limitations. The functionality of the soft-ware may be modified, extended, or contained through manual procedures, software package pa-rameter settings, supplementary software packages, or custom modifications by the vendor or another third party. The adoption of additional manual procedures will require related documentation and user training, thus increasing the responsibilities of users for the smooth running of the system. A decision to supplement the software with other packages or to modify the software, requires that the client have a deep understanding of the system's limitations and the proposed extensions. While some vendors and consultants will have adequately addressed these issues in the selection project, others will not. Where the issues have been adequately addressed, this should be reflected in the client's assessment of Recommendations Satisfaction.

Whether to retain the consultant. The decision to engage or not engage the consultant further is another important aspect of 'how to proceed'. Dimensions expected to have greatest influence on the client's decision to retain the consultant's services in implementation are Performance Reasonability and Performance Satisfaction. The more satisfied the client - the more reasonably they feel the con-sultant performed given project contingencies - the more likely they are to retain the consultant fur-ther. Also, to the extent that the client holds the consultant responsible for perceived poor Recom-mendations Satisfaction or poor Understanding Improvement, the consultant is less likely to be fur-ther retained. While the focus of the study is on client success, the client was also asked, 'Was the consultant invited (or will you invite them) to assist you with implementation of your chosen sys-tem?' On a one-to-seven scale, clients indicated whether this was likely (yes) or unlikely (no). Table 3 ASAC-IFSAM-2000 Proceedings9

indicates correlations between the 'likelihood' score and the client dimensions of success. As pre-dicted, Performance Reasonability, Performance Satisfaction, Recommendations Satisfaction and Understanding Improvement have the largest correlations with Likelihood. Performance Objective is again observed to have the smallest correlation. None of the consultant dimensions were significantly correlated with likelihood at the .05 level, perhaps suggesting that consultants have great difficulty in predicting follow-on work.

Table 3 - Correlations Between ‘Likelihood’ and Client Dimensions of Success

Recommendation Recommendation Understanding Understanding Performance Performance Performance Acceptance Satisfaction Improvement Satisfaction Reasonability Satisfaction Objective Likelihood of.27**.30**.39***.24**.44****.47****0.1 Follow-on Work

Notes: **** p < .001 *** p < .01 ** p < .05 * p < .10

Model validity. In support of findings in [Gable, 1996a], the validity of the success measurement model has been further evidenced by significant correlations observed in this analysis between the success dimensions and the criterion measures. Based on his [1996a] findings, Gable suggested excluding Performance Objective from the model. Evidence from the current study in further support of this includes: of the client dimensions, Performance Objective has the weakest inter-correlations with other client dimensions; it is not significantly correlated with any of the consultant dimensions; the consultant’s measure of Performance Objective correlates strongly with the client Performance Reasonability dimension suggesting that the consultant measures 'reasonability' rather than 'actual' performance versus 'estimated' performance; and the Combined Criterion Item is significantly corre-lated with all of the consultant dimensions and all client dimensions excepting Performance Objec-tive. While these results concur with Gable’s [1996a] analyses, in the case studies several consult-ants expressed the view that clients place substantial emphasis on initial project estimates (Perform-ance Objective) and use these to leverage additional services from the consultant. The evidence indi-cates otherwise. It is suggested that clients place relatively less emphasis on initial estimates and are much more 'reasonable' in their assessments than many consultants perceive. Also, the client Per-formance Objective dimension was found not to be significantly correlated with either the client's or the consultant's overall assessment of success. These findings suggest that consultants not focus too myopically on alternatives that conform to potentially unrealistic early estimates. Instances of prob-lems stemming from the alluded to myopia were observed in the case studies, further suggesting that consultants who attempt to adhere rigidly to initial estimates, perhaps to the detriment of the other dimensions of success, have misplaced priorities.

Study limitations. While strong evidence of the study model's validity has been presented, interpre-tation of associations among the dimensions has been a-theoretical in nature (though following di-rectly from the data and analyses). Further theory driven, preferably longitudinal research into this largely unexplored and important area is warranted. One objective of future research should be to further test interpretations of associations presented in this paper. Throughout the study, due to a dearth of prior empirical research on consultants, analogies have been implicitly and explicitly drawn between the client/consultant relationship and other relationships of academic and practical interest; the user/analyst, the manager/management scientist, and the manager/researcher. The user/analyst analogy has been relied upon implicitly through reference to the IS implementation literature. To what extent the findings of this study are applicable to these analogous relationships, is unclear and is an area for further study. The 'externalness' of the consultants studied may be a particularly rele-vant differentiating characteristic. Ultimately, problems of external validity are not solvable in any ASAC-IFSAM-2000 Proceedings10

conclusive way. Further research in other settings employing the constructs developed in this study may shed additional light on the generalizabilty of the findings.

CONCLUSIONS

Comparison of client and consultant views on client success indicates: (1) broad agreement on the goals for the consultancy (refer to prior discussion on the validity of the dimensions - addressed in detail in [Gable, 1996a]), (2) significant differences on perceived progress achieved toward the goals, (3) a significant inverse relationship between the magnitude of the differences observed and the level of client success, (4) relatively greater consultant emphasis on their own performance, and (5) relatively greater client emphasis on client learning. These findings highlight the importance of aligning client and consultant goals for the engagement and the yardsticks employed in assessing progress towards these goals. Cushing [1990] suggests that anticipated 'friction' can be controlled using strategies that induce goal congruence. Henderson [1990] suggests that shared goals can sus-tain the partnership when expected benefit flows are not realised. Shared goals also offer a common ground from which to negotiate solutions in areas where there is goal conflict. One approach to achieving a closer correspondence between client and consultant views on success, is for the client to refer to the dimensions identified in this study in their 'request for proposal' and for the consultant to do so in their proposal. Also, the validated study instruments (see [Gable, 1996a]) can be adapted to assess internal or external consultants, internal or external auditors, analysts, or management scien-tists, involved in IT selection projects, long-range planning projects, effectiveness reviews, or other helping projects where recommendations are a main end-result. Scoring of the individual dimensions following solution identification but preceding implementation, can be useful in deciding whether to proceed, how to proceed and whether to retain the consultant further. In addition to consultants con-cerning themselves with client success, clients must concern themselves with consultant success. When a consultant is required to work to an unreasonable schedule or budget, the quality of the project outcome is likely to be compromised along one of the less tangible dimensions that have not been explicitly contracted for, and possibly along the more tangible dimensions.

References

Gable. "Consultant Engagement For Computer System Selection: A Pro-Active Client Role in Small Enterprises," Information & Management, (20:2), 1991a, pp.83-93.

____________ Sharp, John A. "Outsourcing Assistance With Computer System Selection: A Suc-cess Factors Model," Proceedings of The 1992 Hawaii International Conference on Systems Sci-ence (HICSS-25), January 1992,

."Client Success When Engaging External Consultants,” Management Science, (42:8), 1996a, pp.1175-1198.

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."Outsourcing IT Advice: A Success Prediction Model,” Information Systems Conference of New Zealand, Palmerston North, New Zealand, October 1996b.

and Raman, K.S. "Government Initiatives for I.T. Adoption in Small Businesses: Experiences of the Singapore Small Enterprise Computerization Program," International Informa-tion Systems, Vol 1, No 1, 1992, pp.68-93.

Gorsuch, R.L. Factor Analysis, 2nd ed. New Jersey: Lawrence Erlbaum Associates Inc., 1983. Henderson, J.C. "Plugging Into Strategic Partnerships: The Critical IS Connection," Sloan Man-agement Review, (31:3), Spring 1990, 7-18.

Kerlinger, F.N. Foundations of Behavioral Research, 3rd edition, Holt, Rinehart, and Winston, 1988.

Turner, A.N. "Consulting is More Than Giving Advice," Harvard Business Review, (Sep-Oct 1982), 120-129.

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学习。起初,我很不情愿。渐渐地,我开始全身心地投入到学习中,并且觉得我不能让父亲失望。在那段时间里,我还遇到了一个小学同学,她学习成绩很好。我记得父亲说过的话,并问她如何学习。她坦率地告诉了我。这样,我在班上得了第十名,数学得了第一名。 在回家的路上,风吹在树叶上,好像在为我鼓掌。在我父亲知道这件事之后,他嘴角露出一丝微笑,让我继续努力。 人们说,“没有压力,就没有动力。”在生活中,如果没有逆耳忠言,一个人似乎就失去了通往成功之路的风向标。因此,让我们接受那些不在我们心中的建议,驶向成功!

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