Keywords

1 Introduction

Organizations invest in new information technology (IT) on a continuous basis with the expectation that IT users will deploy these technologies to their fullest extent so return on investment will be maximized. Therefore, it’s important for IT users to learn and adopt newly implemented IT. However, if organizations do not provide the appropriate support structures to invigorate this behavior, proficient IT usage may be lacking. Supporting structures can also be inconsistent or insufficient in explicitly developing proficient IT usage.

The general expectation is that IT users are to become proficient with a newly implemented IT, even when the appropriate support or incentive structures that would encourage them to deploy a new IT is not devised. The belief may be that such usage is implied. However, providing incentives that focus on task completion without encouraging users to explore better ways of accomplishing it may discourage users from applying the most appropriate IT to complete a task. Hence, technology may not be used to the greatest extent that it could, and the resources that are being invested in new technologies may be wasted.

Although many are concerned with individual IT usage, they are equally concerned with or even more concerned with accomplishing overarching, short-term performance objectives such as meeting quarterly reporting deadlines or financial targets. Hence, many performance objectives are written as such to focus users’ efforts and attention on achievement of these overarching, short-term performance objectives. Although technology may be utilized by an individual to accomplish these performance objectives, its explicit use may not be required. Also, IT users may be encouraged to use a new technology or inspired to use the new technology through training. However, the actual objectives that are established for IT users do not incorporate these learning aspects and utilization of new technology (or the means) but focus on completing the final objective (the ends).

Therefore, the research question posed is: Do performance objectives and incentive structures that explicitly focus on learning and adopting new technologies as well as achieving accurate outcomes, respectively, result in greater utilization of the technology and higher quality performance outcomes?

To answer this question, an experiment will be conducted to assess the effectiveness of performance objectives and incentive structures which vary in terms of incorporating (1) learning and adopting technology objectives and (2) task completion only or accurate task completion incentives. The findings will provide insight into an individual’s propensity to learn and adopt technology (specifically, software applications) and apply it to a task when it is explicitly indicated rather than implicitly stated. Also, the results will provide insights into the quality of outcomes that are realized when incentive structures are focused on achieving accuracy versus task completion, and what influence this may have on the overall selection of technology to be applied.

2 Literature Review

IT users are faced with a challenging decision – should they: (1) invest the time and effort to learn and adopt novel technology that may accomplish a task better, or (2) utilize other familiar technology or means to complete the task so no added learning investment is needed and there is greater certainty regarding the outcome. This dilemma is consistent with the cognitive cost/benefit framework which proposes “individuals weigh benefits (impact on correctness, speed, and justifiability) and costs (mental effort of information acquisition and computation) before choosing a strategy for processing information in decision-making.” [1, p. 1830]. Previous research results suggest that organizations need to consider the relationship between compensation structures and IT adoption which can have an important influence on final task performance [2]. Previous studies have combined and expanded upon previous models and research of technology adoption by developing the coping model of user adaptation (CMUA) [3]. The model proposes that adapting or modifying technology can bring about a disruption to the work environment, and users can cope with the change with a variety of strategies, from benefit maximization to self-preservation. Therefore, upon the introduction of a new technology, individuals can respond in a variety of manners – with some leading to full utilization of the new technology and others avoiding it and finding alternative means to complete existing tasks.

Previous research suggests that performance measures that are nonfinancial are more likely to foster innovation and risk taking [4]. Therefore, if performance objectives are focused on activities such as learning a technology (versus simply completing a task or getting the job done), they may encourage users to innovate and explore the technology and find new, better, and more creative ways to accomplish tasks with it. Previous research has also had some focus on learning and applying technology. For example, research has focused on the impact of various incentive contracts on learning and performance outcomes [5]. The study found that individuals with incentive-based contracts (versus flat-wage contracts) were more likely to learn (revise their beliefs), increase effort (longer task duration), and improve performance. As another example, research has examined the influence that perceived usefulness, time pressures, and subjective norms have on an individual’s decision to learn novel technology functions [6]. The findings indicated that supportive structures can directly influence whether or not one elects to pursue learning and incorporating technology into work routines.

However, previous studies have not explicitly focused on the impact of incorporating performance objectives and incentive structures for IT users, and their subsequent impact on successful technology adoption and performance outcomes. In other words, what has not been evaluated is the impact of performance objectives supporting learning and applying a technology and the resulting choice of whether or not to learn a new technology that may assist one to accomplish tasks better. Also, when incentive structures are established that explicitly determine how an individual may be compensated, different choices in actions may occur. If the individual is to produce a high-quality outcome, they may be more inclined to select the technology that has a better fit with the task even if it requires an investment to learn. However, if the individual is not given incentives to produce a high-quality outcome, then they may be more inclined to select a more familiar technology that doesn’t require the learning investment even if the quality of the outcome suffers. Hence, the research objective for this study is to evaluate the impact of performance objectives that explicitly incorporate learning and adoption (i.e., specify learning and adopting the technology) and incentive structures that focus on producing quality outcomes (i.e., accurate task completion).

3 Theoretical Foundation and Hypotheses Development

In conducting this research, transaction cost economics, attribution theory, technology-to-performance chain, and task-technology fit model provide the theoretical basis for the hypotheses.

3.1 Transaction Cost Economics

Transaction cost economics has been applied in organizational contexts in which the task or transaction costs are examined under various governance structures [7–10]. These transactions can vary by degree of asset specificity (change in value that may occur if an investment made in a particular transaction is reallocated), unknowns/uncertainties, and frequency [8, 10]. Although transaction cost economics has been studied from an organizational perspective, individual-level application is relevant as well. Contract theory has been proposed to be applicable to a variety of transactions [10]. In the context of contracts, the substance of performance objectives can be viewed as a contract because employees are eligible for rewards if the performance objectives are accomplished according to pre-determined criteria. Transaction cost economics can be applied to employment contracts because of the unique nature of job tasks and processes.

Employment contracts have been proposed to be deficient and give employees volition regarding task completion [10]. Therefore, employment contracts can introduce variability in performance outcomes because of the discretion provided in methods or manners utilized to accomplish tasks. Transaction cost economics’ assumptions include bounded rationality and opportunism [8]. Bounded rationality implies individuals have cognitive limitations regardless of their intentions, and opportunism suggests that individuals may prioritize their own interests above other interests.

In applying transaction cost economics to the decision an employee makes in deciding whether or not to learn and apply a new technology to a particular job task, transaction costs exist for the employee in terms of the time and effort required to learn the technology and apply it to the task (versus performing the task using existing, familiar methods). Because of bounded rationality, the employee may intend to perform the task in the most effective and efficient manner possible, but cannot predict whether learning and utilizing the new technology will yield those results. Hence, a level of uncertainty arises. Also, considering the assumptions of opportunism, an employee would be more likely to pursue the option that provides him/her with greater rewards or least costs or effort, even if at the expense of the organization.

When learning and adopting technology performance objectives are not included and incentive structures are based on task completion only, the employee may focus on completing the task with known methods (i.e., not learn and adopt the new technology) because it reduces the level of uncertainty in achieving the incentives which provides opportunities for monetary rewards. Therefore, the following hypothesis is proposed.

H1: Individuals will have a greater propensity to use a familiar technology to complete a task if the incentive specifies task completion only and there is no learning and adopting technology performance objective than when a learning and adopting technology performance objective is provided.

3.2 Attribution Theory

Attribution Theory proposes that individuals make causal attributions to explain behavioral phenomena, or explain why people behave the way they do [11]. These causal attributions have been demonstrated to be the stimulus for subsequent actions or the response that one has [12]. For example, previous research found that when individuals were subject to a binding contract, their cooperative efforts were attributed to the constraints of the contract [13]. However, when a nonbinding contract was utilized, cooperative efforts were attributed to trustworthiness. Karsten [12] found similarities between the casual attributions of IS professionals and IS users when successful performance outcomes were achieved by IS users. However, when unsuccessful outcomes were realized, causal attributions deviated.

Therefore, research has shown, through the application of Attribution Theory, that when specific intentions are not made explicit, individuals may attribute the behaviors of another with their own explanations. Individuals may interpret the particular event with their own meanings and derive their own implications of another’s actions or inactions. Hence, when incentive structures do not specify that the quality of the outcome from IT use is more important than obtaining any outcome, IT users may attribute getting the task completed to be of greater importance than getting the task completed accurately. IT users may attribute incentive structures that provide no impetus for achieving a certain level of quality results to an organization’s lack of concern for quality and a greater concern for just “getting it done.” In conjunction with transaction cost economics, IT users may act opportunistically and complete the task with a more familiar technology even if it may result in less accurate outcomes. Therefore, incentive structures that specifically incorporate accomplishing a task with greatest accuracy may be more likely to result in individuals selecting the technology that will produce the most accurate results. Hence, individuals will be more likely to learn and adopt a new technology that is a better fit with the task and can provide more accurate outcomes than utilize a familiar technology even if no learning and adopting technology performance objectives are given.

Based on the above, the following hypotheses are proposed:

H2: Individuals will have a greater propensity to learn and adopt a new technology to complete a task if the incentive specifies task completion accuracy and there is no learning and adopting technology performance objective than when the incentive specifies task completion only.

H3: Individuals will have a greater propensity to learn and adopt a new technology to complete a task if the incentive specifies task completion accuracy and there is a learning and adopting technology performance objective than when the incentive specifies task completion only.

3.3 Technology-to-Performance Chain and Task-Technology Fit

According to the technology-to-performance chain, technology that is appropriate to complete the task and utilized by an individual can produce positive performance outcomes [14]. Task-technology fit (TTF) has been “defined as the extent that technology functionality matches task requirements and individual abilities. Task-technology fit is presumed to lead to higher performance” [1, p. 1829]. Specifically, “TTF is the correspondence between task requirements, individual abilities, and the functionality of the technology.” [14, p. 218]. To the extent that technologies have greater TTF, improved performance outcomes are more likely to occur.

Greater utilization of a technology alone will not necessarily produce positive outcomes. If the technology used to accomplish the task is not the most effective or appropriate (i.e., it is less likely to produce the most accurate results), then performance may suffer. Therefore, individuals who continue to utilize existing technologies that are less likely to produce accurate outcomes may not improve performance. If individuals adopt a new technology that is more likely to produce accurate outcomes (i.e., has a more appropriate fit to the task), then individual performance can be positively impacted. Hence, the incentive structures and performance objectives given to an employee can influence his/her choice of technology to accomplish the task and the quality of the outcome.

Based on the above, the following hypothesis is proposed:

H4: Individuals will have a greater propensity to learn and adopt a new technology to complete a task if a learning and adopting technology performance objective is provided and the incentive specifies task completion accuracy than when a learning and adopting technology performance objective is not provided.

4 Research Method

4.1 Overview

To test the research hypotheses for this study, a 2 × 2 design is proposed (see Fig. 1) in which performance objectives and incentive structures are manipulated. Subjects will carry out tasks to analyze accounting information using either Microsoft Excel or Access. The research subjects will receive the same training and complete the same exercises, but will receive variations in their performance objectives (learning and adopting technology performance objective versus no performance objective) and incentives (task completion only versus task completion accuracy). Subjects will be randomly assigned to one of the four experimental conditions and will be asked to not disclose the specifics of their performance objectives and incentives to other subjects. The subjects will receive training along with their performance objectives and incentives (performance objectives and incentives will be given before the training commences). In the following week, subjects will complete the associated exercises with either Microsoft Excel or Access. A pre-study questionnaire will be administered before the initial training, and a post-study questionnaire will be administered after the exercises are completed. The time allowed for training and exercise completion will be held constant for each condition.

Fig. 1.
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Research Design

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Course credit for the task will be based on the assigned incentive structure. Analysis of the effects of the different performance objectives and incentive structures will be assessed by comparing the method used to complete the task (Microsoft Excel or Access), and number of exercises completed correctly.

4.2 Subjects

Undergraduate students who are experienced Microsoft Excel users but are not experienced with Microsoft Access will be recruited. Students will receive the same training, and will receive their grade (final course credit earned) upon completion of the experiment.

Table 1. Research Conditions
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4.3 Procedure and Measures

In the initial session, subjects will be trained on Microsoft Excel (a refresher course) and Microsoft Access. During the initial training, they will also be asked to complete a demographic questionnaire in which they rate their need for cognition and need for achievement on a scale of 1 to 7 (with 1 being completely unmotivated and 7 being extremely motivated). Also, measures of computer self-efficacy, previous technology/software experience, level of familiarity with Excel, and level of familiarity with Access will be gathered. The training session will entail reviewing (for Excel) and learning (for Access) those skills needed to complete the required exercises for the following week. The exercises will be tailored so that they can be completed with either Microsoft Excel or Access, but utilizing Excel will be more complicated and cumbersome and, hence, more prone to errors. The performance objectives and incentives structures for each condition are summarized in Table 1.

After completion of the exercises, they will receive their course credit earned. They will also be asked which technology they chose to use (Excel or Access) to complete the exercises and asked “Why they chose to use that particular technology”. The latter part of this question will ask them to first rate on a scale of 1 to 7 (1 being completely unimportant to 7 being extremely important) if factors such as “Familiarity with the technology”, “Instructor concern with using the Technology”, “Ability to meet Learning Objectives”, “Ability to achieve Course Credit”, “Greater efficiency”, “Greater effectiveness”, etc. were weighed in their decision. They will also have the opportunity to provide any open-ended feedback after these ratings. Measures of number of exercises completed correctly will be taken and assessed by the researcher and one other individual who is familiar with Microsoft Excel and Access.

5 Results and Discussion

A 2 × 2 between-group factorial ANOVA will be used for data analysis. For hypothesis 1, analyses will be made of the use of technology (Excel versus Access) and accuracy of outcomes between the performance objective conditions (learning and adopting technology performance objective versus none). For hypothesis 2, analyses will be made of the technology selection and accuracy of the outcomes made for each group between the incentive structures conditions (task completion only incentive versus task completion accuracy incentive).

For hypothesis 3, analyses will be made of the use of technology (Excel versus Access) and the accuracy of the outcomes when the incentive structure indicates that accuracy of task completion is to be rewarded and learning and adopting technology performance objectives are provided. For hypothesis 4, analyses will be made of the use of technology (Excel versus Access) and the accuracy of the outcomes between the performance objective conditions (learning and adopting technology performance objective versus none) when incentives for task completion accuracy are given.

6 Implications and Conclusion

If the hypotheses are supported, this will indicate that given the choice of the means to complete a task, an IT user is more likely to select a familiar means and focus on completing a task regardless of being trained on a new technology that could provide a more efficient means and accurate task completion. Therefore, those who are concerned with the adoption of a new technology should align performance objectives with these concerns in order to derive the behavior that is desired from their IT users. Also, the results will imply that establishing incentive structures that are based on the quality of outcomes achieved can lead to individuals selecting a technology that is more appropriate for the task, which can result in more successful task outcomes. The results from this study would also imply that the application of a new technology will not just happen automatically, even after training has occurred, and assumptions should not be made that IT users know the importance of learning and adopting a new technology.

In conclusion, this research proposes to look at the influence that variations in performance objectives and incentive structures can have on the learning/adoption of a new technology and task performance outcomes. If IT users are being evaluated on other criteria that do not necessarily require new technology’s use, then IT users may be confused as to the appropriate actions to take – use a familiar means to complete the task so the objective has more certainty to be met or take a risk and invest the time to learn the technology and use it to complete the task. Overall, this research will contribute to the knowledge of performance objectives’ and incentive structures’ influences (as well as their variations) on IT users’ decisions to learn and adopt a technology to achieve higher quality outcomes versus just getting the job done.