Cognitive styles: web portal acceptance items measurement

Raja Suzana Raja Kasim Accounting Research Institute &

Center of Applied Management, Faculty of Business Management, UiTM

rsuzana@salam.uitm.edu.my

Annurizal Annuar Center of Applied Management,

Faculty of Business Management, UiTM Universiti Teknologi MARA

annurizal@salam.uitm.edu.my

Abstract—The central intention of the paper was to establish a conceptual valid assessment framework and sound instruments for the measurement of cognitive styles in predicting academician and non-academician staff use and acceptance of technology. The latest technology acceptance model, unified theory of acceptance and use of technology, along with psychology--cognitive styles was tested to examine the role of cognitive styles in influencing the usage of I-Staff portal. A stratified random sampling method was used in this paper and 72 respondents participated. The instrument development and validation were adopted using the Rasch measurement paradigm, framework and model. Rasch analysis confirmed the cognitive styles among academician and non-academician staff. Instruments were developed: a 15-item to measure system acceptance including performance expectancy, effort expectancy, social influence and facilitating conditions and items measuring the experienced using the portal and cognitive styles respectively. The findings were discussed in relation to the theoretical perspective underpinning this paper. Besides, findings from this paper helped to identify several salient categories that affect the unified theory of acceptance and use of technology variables. Results of this paper also confirm the robustness of the Venkatesh’s model on unified theory of acceptance and use of technology.

Keywords- cognitive styles, web portal, acceptance model, unified theory of acceptance and use of technology, higher education institutions, Malaysia

I. INTRODUCTION Portal technology has gripped the imagination of information technology (IT) users and vendors ever since the popularization of the concept at the end of 1998 [1]. It is predicted that the growth of the portal market will be phenomenal. This prediction is supported by the Meta Group [1, 2], claiming that by 2002: Knowledge management collaboration and innovation will permeate the Global 2000 corporation’s strategy.

Recent study from the lens of weblog systems found that all variables in the UTAUT model significantly affected the behavioral intention to use [6, 7]. This result is similar [3, 4]. On the other hand, none of the literature had discussed the issue of individual portal usage affected by individual

cognitive styles particularly in making decision in accepting a portal.

Venkatesh and his colleagues suggested that future research should attempt to “test additional boundary conditions of the model in an attempt to provide an even richer understanding of technology adoption and usage behavior [13, 14]. According to Kirton Adaption - Innovation theory introduced by [18], individuals can be located on a personality dimension, ranging from adaptor to innovator. The theory indicates that individuals have different styles of changing, creativity, problem solving and decision making. The individuals’ cognitive styles are important in determining the portal acceptance.

The current study examined the construct of cognitive style using Kirton adaptive-innovative (KAI) [18] an additional variable on technology acceptance that was not included in any of the models from which the UTAUT was composed. Due to this, the need to understand individuals’ differences in particular cognitive styles in accepting a portal is vital to UiTM management.

II. LITERATURE REVIEW The term cognitive style is developed by cognitive

psychologists who did their research on problem solving and perceptual and sensory functions [14]. It is believed that the construct of Cognitive Style was originally proposed by [1] referring to an individual's habitual way of perceiving, remembering, thinking and problem solving. He is the first researcher who deliberately used the ‘style’ construct in association with ‘cognition’ [13, 14]. Due to the massive studies and research, a lot of conceptualizations and terms are introduced.

To further illustrate the meaning of cognitive style, [6] defined cognitive styles as the individual way in which a person perceives, thinks, learns, solves problems, and relates to others. [13] defines a cognitive style as an individual’s characteristic and consistent approach to organizing and processing information. Another definition was born when [14] referred cognitive style as the way in which people process and organize information, and arrive at judgments or

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conclusions based on their observations. To bring clarity in the various field of cognitive style, [4] concludes that all these different conceptions one way or another imply that cognitive styles are consistent individual differences in ways of organizing and processing information and experience.

[5] also contributed to another definition of cognitive style as an individual’s method of processing information. Definitions generally [13,14] put emphasis on individual differences or preferred and individual habitual approaches in perceiving, organizing, retrieving, conceptualizing and representing information, and constructing relationships.

In the light of these definitions, recent studies by [16] termed cognitive style as the way in which people perceive environmental stimuli, and how they organize and use this information for guiding their actions. A cognitive style influences how people look at their environment for information, how they organize and interpret this information, and how they use these interpretations for guiding their actions [17].

A great number of style dimensions have been reported in the literature about cognitive style. Scientific research often passes through dialectic cycles, typically initiated by relatively simple hypotheses, which get more and more complex as an increasing number of boundary conditions are identified [16].

Cognitive styles research has been in such a state, and it appears as if research in the field was diminishing, plausibly as the result of the confusion related to the use of the term in the field, and then in recent years regained credibility as a research object. Researchers have overviewed and summarized existing research and integrated style dimensions. Such examples are the wholist-analytic and verbaliser-imager dimensions [15] and the analytic-intuitive dimension ([16]. More recently, [17] have proposed an overall framework that integrates more fully the various models of style.

A. Unified Technology Acceptance and Use of Technology (UTAUT)

The Unified Theory of Acceptance and Use of Technology (UTAUT) developed and tested by [1] in the MIS field, give an idea to explore about technology acceptance in the portal studies. For that reason, this paper will take UTAUT model as the framework. The UTAUT model states in short that expectations about performance, expectations about effort, and social influence heavily determine the intent to adopt a technology. The model also states that a person’s gender and age heavily influence all three factors, that experience influences effort expectations and social influence, and that voluntariness heavily modifies social influence. A. Performance Expectancy

Performance Expectancy is defined as “the degree to which an individual believes that using the system will help him or her to attain gains in job performance” or in this case I-Staff portal technology will help him or her to attain gains in job performance [1]. This scale is most closely related to Perceived Usefulness in Technology Acceptance Model [9] [9].

B. Effort Expectancy

Effort expectancy is defined as “the degree of ease associated with the use of the system” [1]. This scale is most closely related to Perceived Ease of Use in the Technology Acceptance Model [9]. This variable is expected to be more salient in the early stages of a new behavior, when process issues represent hurdles to be overcome, and later become overshadowed by instrumentality concerns [9, 14]. [1], drawing upon other research, suggest that effort expectancy is more significant for women than for men. C. Social Influence

Social Influence is defined, as it traditionally is in user acceptance literature, as “the degree to which an individual perceives that important others believe he or she should use the new system” [1]. [1] looks at TRA, TAM2, TPB/DTPB and C-TAM-TPB, social factors in MPCU, and imaging IDT to find a unified construct. As for Thompson et al. (1991), he uses the term social norm in defining construct and acknowledged the similarity to subjective norms within TRA. D. Facilitating Conditions

Facilitating conditions, defined as “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh 2003, p. 453), has a significant direct effect on actual use but only when moderated by age and experience (i.e. older users with more experience). He utilized three different constructors which were used to develop this unified construct: Perceived behavioural control (TPB/DTPB, C-TAM-TPB), Facilitating conditions (MPCU), and Capability (IDT). [1] also affirms that all this earlier constructs are parallel relations and therefore the intentions are the same. E. Cognitive Styles

According to [16], to improve user satisfaction, previous studies recommend system designs which suit the user’s approach to problem solving; that is, to match systems to the cognitive styles of users. The main goal of this paper is to identify whether cognitive style has any effect on the I-Staff portal acceptance and usage. If the result shows significant relationship, this will prove that individual cognitive styles have the effect on usage as it influences the process of information for an individual while using the I-Staff portal. Based on Kirton Theory, the styles of an individual in using a system is influenced by the ways of what they think to make their decision whether that individual is an innovator or adaptor person. This paper will examine the individual differences based on their ways of thinking in making decision. This paper will look into the role of cognitive styles in portal acceptance besides examining the relationship between UTAUT variables. As noted by [6], it is important to consider cognitive styles as individual differences in examining the relationship between the acceptance variables. It is important to indicate whether there is any influence of cognitive styles on usage of the system leading to the system acceptance. In

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meeting these objectives, a theoretical framework which also serves as a research framework of these relationships has been developed as below:

Figure 1: Research Framework

V. RESEARCH DESIGN The stratified sampling technique is used as a sampling tool. Stratified random sampling of a population is performed by taking simple random samples of strata, mutually exclusive segments of the population, and aggregating the resulting estimates to produce estimates applicable to the entire population. If stratifying the population results in strata with relatively low variance, a small sample can measure each stratum to a desired confidence. [17] for this paper, all departments are selected and the numbers of samples are randomly selected. Based on the following stratified random sampling formula the sample size was determined. n = (confidence)² variance (error)² Based on the pilot study, the mean and standard deviation of the acceptance obtained is 2.8 and standard deviation 0.68, respectively. Using a 95% confidence and a bound on the error of estimation of 0.133, a sample size of 100 was obtained. n = (1.96)² (0.68)² (0.133)² =100.421 =100 Based on the equation above a sample size of 100 was obtained and all respondents responded to the questionnaire.

The respondents are divided into two categories namely academician and non-academician staff. Eight faculties were involved during the survey which consists of Faculty of Accountancy, Faculty of Applied Science, Faculty of Business Management, Faculty of Civil Engineering, Centre of Islamic Thought and Understanding, Language Academy, Faculty of Computer Science and Quantitative Science and Faculty of Office Management and Technology. In contrast, 9 departments of non-academician staff were involved. The Director’s Office, Administration department, HEA department, HEP department, Finance department, Library, Facility department, Information Technology department and Plantation department were the respondents for the non-academician staff. However, the survey is limited only to UiTM Pahang Kampus Jengka staff only and does not involve Raub and Kuantan campus. 50 respondents from academician and 50 respondents from non-academician were involved in the survey.

VI. DATA ANALYSIS AND RESULTS

A. Reliability From Table 1, the reliability of item difficulty estimates is

high (.99). The item separation index of 9.16 indicates that the items can be separated into 1 difficulty strata. As item reliability indicates the ability of the test to reproduce the hierarchy of items along the measured variable [21, 22], a reliability coefficient of .73 suggests that this order of item hierarchy will be replicated with a high degree of probability if the items were given to other comparable cohorts.

With regard to person measures, the reliability coefficient is considerably higher at 0.91. Responses to the statements in the questionnaire showed greater consistency and this showed in a higher reliability coefficient for the data.

Table 1. Person and Item Reliability Coefficients

SUMMARY OF 72 MEASURED Persons +-----------------------------------------------------------------------------+ | RAW MODEL INFIT OUTFIT | | SCORE COUNT MEASURE ERROR MNSQ ZSTD MNSQ ZSTD | |-----------------------------------------------------------------------------| | MEAN 187.7 49.0 -2.70 .19 1.02 -.2 1.00 -.2 | | S.D. 18.9 .0 .71 .02 .42 2.2 .41 2.1 | |-----------------------------------------------------------------------------| | REAL RMSE .21 ADJ.SD .68 SEPARATION 3.20 Person RELIABILITY .91 | +-----------------------------------------------------------------------------+

Person RAW SCORE-TO-MEASURE CORRELATION = .99 CRONBACH ALPHA (KR-20) Person RAW SCORE RELIABILITY = .92

SUMMARY OF 49 MEASURED Items

+-----------------------------------------------------------------------------+ | RAW MODEL INFIT OUTFIT | | SCORE COUNT MEASURE ERROR MNSQ ZSTD MNSQ ZSTD | |-----------------------------------------------------------------------------| | MEAN 275.8 72.0 .00 .16 .99 -.2 1.00 -.2 | | S.D. 57.0 .0 1.57 .01 .35 2.0 .36 2.1 | |-----------------------------------------------------------------------------| | REAL RMSE .17 ADJ.SD 1.56 SEPARATION 9.16 Item RELIABILITY .99 | +-----------------------------------------------------------------------------+

B. Fit Statistics Summary fit statistics of academic staff and items measures

were next captured. The result will explain whether the data fit a construct [24]. Rasch experts examine item fit by looking at two types of fit values known as infit and outfit [23]. Item fit is an index of how well items function in reflection of the trait [23, 24]. Rasch analysts typically examine infit scores, which indicate how well the observations fit the Rasch modeled

Cognitive Style

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Gender Age Experience

Usage

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expectations [24] whereas “outfit” is less threatening to measurement and easier to manage.

Table 1 shows comprehensive information about whether the data showed acceptable fit to the model. The mean square (MNSQ) infit and outfit for academic staff and items are expected to be 1.0. Evaluation of MNSQ infit and outfit were between 0.8 and 1.2 [24]. Thus this suggests that the provided data exhibited fit the model and supported the unidimensionality. Note that the mean of items is 0.0. The measurement shown in Table 1 reveals that the academic staff mean, µacademic staff = 2.08 logits which suggests these items, on average, were easy to answer.

Table 2 presents items in order of difficulty. Entry number is the item’s location in this scale of 22 items. Raw score is the total number of academic staff who obtained correct for the corresponding item. Count tells us that 22 academic staff responded to the items. Measure is the logit position of the item, the more positive the value indicates more difficult the item. For this data, mean square (MNSQ) infit of all items less than 1.20 (20% variance), which was recommended by [22, 23]. Therefore we can consider the items fit well.

The next overall statistics investigated is separation. Item separation is the distance in logits between items of varying difficulty [23, 24]. The greater values of separation represent spread of items along the continuum and lower the values indicate redundancy in the items [27]. The item summary gives a good summary with separation, G = 9.16. This value indicates that the items were sufficiently well separated in difficulty. For academic staff, separation is 3.20.

Table 2: Hierarchy of relationship

+-------------------------------------------------------------------------------------------+ |ENTRY RAW MODEL| INFIT | OUTFIT |PTMEA|EXACT MATCH| | |NUMBER SCORE COUNT MEASURE S.E. |MNSQ ZSTD|MNSQ ZSTD|CORR.| OBS% EXP%| Item | |------------------------------------+----------+----------+-----+-----------+--------------| | 22 257 72 .53 .15|1.81 4.1|1.83 4.1|B .56| 29.2 47.6| CPrudent | | 26 203 72 1.70 .14|1.64 3.6|1.73 4.0|C .00| 38.9 43.7| CIdea | | 10 239 72 .94 .15|1.65 3.5|1.67 3.6|D .15| 34.7 45.6| SInfluence | | 9 258 72 .51 .15|1.56 3.0|1.53 2.8|E .22| 41.7 47.9| EOperate | | 21 260 72 .46 .16|1.51 2.7|1.55 2.9|F .56| 31.9 48.1| CCreate | | 8 247 72 .76 .15|1.47 2.6|1.51 2.8|G .27| 41.7 46.6| EEasy | | 46 294 72 -.46 .18|1.51 2.6|1.43 2.3|H .65| 48.6 56.3| CFit | | 2 292 72 -.40 .17|1.42 2.2|1.37 2.0|I .30| 52.8 55.8| UsePortal | | 27 217 72 1.41 .14|1.41 2.4|1.41 2.4|J .24| 36.1 43.6| CPerspective | | 11 283 72 -.14 .17|1.37 2.0|1.31 1.7|K .29| 52.8 54.7| SImportant | | 3 272 72 .16 .16|1.34 1.8|1.28 1.6|L .29| 40.3 51.6| PUseful | | 29 242 72 .87 .15|1.20 1.2|1.26 1.6|M .38| 44.4 46.1| CChanges | | 49 515 72 -6.77 .14|1.23 1.3|1.24 1.4|N .16| 31.9 47.2| CPredict | | 15 272 72 .16 .16|1.22 1.2|1.22 1.3|O .29| 40.3 51.6| FKnowledge | | 18 273 72 .13 .16|1.11 .7|1.08 .5|P .60| 52.8 51.8| CConfirm | | 19 238 72 .96 .15| .97 -.1|1.05 .4|Q .38| 51.4 45.5| CThink | | 25 258 72 .51 .15| .87 -.8| .98 -.1|R .49| 61.1 47.9| CBoss | | 1 318 72 -1.28 .20| .96 -.2| .90 -.5|S .34| 69.4 57.3| FreqAcc | | 34 289 72 -.31 .17| .94 -.3| .92 -.4|T .64| 62.5 55.5| CDisagree | | 28 266 72 .31 .16| .92 -.4| .94 -.3|U .61| 48.6 50.1| CRoutine | | 23 294 72 -.46 .18| .92 -.4| .86 -.8|V .75| 55.6 56.3| CAct | | 41 265 72 .34 .16| .91 -.5| .88 -.7|W .66| 56.9 49.9| CMethod | | 38 264 72 .36 .16| .81 -1.2| .90 -.5|X .47| 56.9 49.5| CMaster | | 17 286 72 -.22 .17| .88 -.7| .84 -.9|Y .72| 56.9 55.2| FProblem | | 44 271 72 .18 .16| .87 -.7| .87 -.7|x .62| 52.8 51.5| CImpose | | 24 281 72 -.08 .17| .87 -.7| .82 -1.1|w .66| 62.5 54.3| CSeek | | 47 261 72 .43 .16| .80 -1.2| .85 -.9|v .54| 54.2 48.7| CNeeds | | 30 252 72 .65 .15| .78 -1.4| .84 -1.0|u .56| 56.9 47.3| CThorough | | 36 239 72 .94 .15| .78 -1.4| .83 -1.1|t .37| 58.3 45.6| CTeam | | 32 247 72 .76 .15| .81 -1.2| .83 -1.1|s .62| 55.6 46.6| CNewIdeas | | 14 245 72 .81 .15| .80 -1.3| .81 -1.2|r .52| 59.7 46.6| FResource | | 5 286 72 -.22 .17| .81 -1.1| .78 -1.3|q .41| 69.4 55.2| PProductivity| | 12 272 72 .16 .16| .75 -1.5| .76 -1.5|p .51| 52.8 51.6| SHelpful | | 16 252 72 .65 .15| .75 -1.7| .75 -1.6|o .49| 61.1 47.3| FCompatible | | 6 297 72 -.55 .18| .73 -1.6| .72 -1.7|n .49| 75.0 56.3| EInteraction | | 31 260 72 .46 .16| .72 -1.9| .73 -1.8|m .61| 56.9 48.1| CPlodder | | 42 261 72 .43 .16| .70 -1.9| .72 -1.8|l .59| 55.6 48.7| CRisk | | 35 272 72 .16 .16| .71 -1.8| .70 -1.9|k .65| 62.5 51.6| CStimulate | | 45 254 72 .60 .15| .69 -2.1| .71 -1.9|j .46| 51.4 47.1| CProtect | | 33 279 72 -.03 .17| .71 -1.8| .69 -1.9|i .68| 56.9 53.4| CConsistent | | 7 300 72 -.65 .18| .69 -1.9| .70 -1.9|h .39| 75.0 56.2| ESkillful | | 13 273 72 .13 .16| .69 -2.0| .68 -2.1|g .50| 51.4 51.8| SSupport | | 37 250 72 .69 .15| .61 -2.8| .66 -2.4|f .45| 59.7 46.7| COriIdeas | | 43 263 72 .39 .16| .65 -2.3| .65 -2.4|e .56| 55.6 48.9| CDeviation | | 4 293 72 -.43 .17| .62 -2.5| .60 -2.7|d .41| 69.4 55.8| PQuickly | | 40 238 72 .96 .15| .61 -2.8| .61 -2.8|c .62| 59.7 45.5| CPrefer | | 39 259 72 .48 .15| .54 -3.3| .54 -3.3|b .62| 65.3 48.1| CProliferate | | 20 258 72 .51 .15| .52 -3.5| .54 -3.4|a .59| 62.5 47.9| CDetail | |------------------------------------+----------+----------+-----+-----------+--------------| | MEAN 275.8 72.0 .00 .16| .99 -.2|1.00 -.2| | 53.3 50.1| | | S.D. 57.0 .0 1.57 .01| .35 2.0| .36 2.1| | 11.2 3.9| | +-------------------------------------------------------------------------------------------+

VII. IMPLICATIONS TO THE THEORY AND BODY OF

KNOWLEDGE The following discussion outlines the contribution this paper can make in two interdisciplinary areas of entrepreneurship and higher education. A. Implications for policy and practice This paper seeks to find out whether cognitive styles play a significant role in influencing the I-Staff portal acceptance. It was found that that the formulated model which synchronized UTAUT and cognitive style is sufficient to have influence in predicting the effects of cognitive style in web portal acceptance. The results from this paper strengthened the empirical findings of [13] which shows that UTAUT variable is capable to measure the success for new technology introductions and assist them understand the drivers of acceptance. This is also supported by a significant result achieved when UTAUT model is adopted by [18, 19]. However, none of the literature had discussed the issue of individual portal usage affected by individual cognitive styles particularly in making decision in accepting a portal. Findings from this paper contribute a significant effect when UTAUT model is extended with another construct, cognitive styles. Limitations Using the academician and non-academician staff only in the context of UiTM Pahang as a study subjects might have limitations to generalize the results to the Universiti Teknologi MARA system. Based from experience conducting this research, if the study is conducted to the whole Universiti Teknologi MARA campuses, it can be predicted that the outcomes may produce a better result in understanding the portal acceptance and usage as a whole.

Recommendations for Future Research

There are three recommendations of the findings that can be highlighted. The first recommendation is that on the population. It is recommended to conduct a wider range of survey on the whole Universiti Teknologi MARA campuses so that it can cover a bigger population which could produce the whole picture of the acceptance and usage of the I-Staff portal. The second recommendation is on the instrument of measuring cognitive styles. For this purpose of paper, Kirton Adaptor-Innovator Inventory (KAI score) is used as a tool to measure cognitive styles. On the other hand, different instruments to gauge the cognitive styles like Cognitive Style Index, Assimilator – Explorer, Field Dependency - Independency can be used to compare the findings. The third recommendation is to explore other variables that can contribute to the prediction model by conducting another research involving other technology acceptance model. As a

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conclusion, the web portal application among UiTM Pahang staff is widely used regardless of the job category either they are innovator or adaptor user.

VIII. CONCLUSIONS

This paper provides a practical contribution to UiTM especially to the Center Information Integrated System Department in identifying I-Staff portal usage among users in UiTM. In sum, majority of the respondents accepts the portal as a useful tool in their daily job. Furthermore, cognitive styles influence the method of learning among users. Although innovators do not easily accept a system but they are good learners. System developers can use these findings to understand the usage of the I-Staff portal. By understanding the styles of users in accepting a system, the designers can come up with a design that fulfills the styles of users in Universiti Teknologi MARA.

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