The measures

A summary of the variables that were examined as part of this research project, the measures exploring those variables and the corresponding survey questions can be found in Table 5.2. The table presents first the criterion variables with the respective measures used and corresponding question numbers. The criterion variables are then followed by a listing of the predictor variables with their measure descriptions and question numbers. The predictor variables are presented in the order of the interaction clusters described in chapter 4. The description of each influence identified as a ‘driver influence’ in that chapter, is followed by those of the associated, hypothesised moderator variables. For the complete wording and actual placement of the survey questions please refer to a copy of the survey in Appendix C.

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Table 5.2: Variables, measures and corresponding survey questions

Variable Measure Question numbers

Criterion variables

Personal Web Use (PWU)

Past frequency of PWU Frequency past 2 weeks 1

Habitual use Average weekly habit 2

Average duration of PWU Average weekly duration 3

Activity Type Contact-related (CPWU) 4, 5, 6, 9, 12

Information-seeking (IPWU) 7, 10, 13, 14

Predictor variables

Participant's moral norms regarding PWU Moral norm 34, 35, 36 Acceptance of the ledger strategy

of neutralisation Neutralisation acceptance 79, 80, 81, 82, 83, 84

Work group's norm regarding PWU Work group norm 39, 40, 41, 42 Knowledge of PWU rules and procedures Rules knowledge 50, 54, 55, 56, 89, 90

Perceived workload Quantitative Workload Inventory 69-73 Attitude towards work effort in general Equity Preference Questionnaire 91-106

‘taker’ Scale 91, 92, 93, 94, 95, 96

‘giver’ Scale

102, 103, 104, 105, 106

Trait reactance Part of Merz's Trait Reactance

Scale 107-123

Perceptions of supervisor treatment Parts of Lim's interactional

fairness measure 85, 86, 87, 88

Attitude towards control of PWU Attitude to PWU procedures 51, 52, 57, 58

Boredom at work Boredom at Work Scale 59-66

Job status Current occupational level 133

Tenure in the current job Length of service 130

Social loafing risk Social loafing risk Index 55, 56, 74, 75, 76, 77,

78, 131, 134

Items additional to the proposed model (As discussed in chapter 8)

Consequences of PWU in situations of PWU control

Critical incidents – positive

outcome 135

Critical incidents – negative

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5.3.1 The criterion variables

5.3.1.1 Frequency of PWU in the two weeks prior to completion of the questionnaire (Q1)

How often the participant estimated he or she engaged in PWU in the two weeks before completion of the survey, was explored through one multiple choice question. (“Can you please give an estimate how often you have accessed the Internet for persona interest during work time over the past 2 weeks?”). This question offered the respondents 11 answer options ranging from ‘never’ to ‘over 30 times’. A similar question asking respondents to estimate their Internet access has been used by Anandarajan, Simmers and Igbaria (2000), however with less detailed answer options. In this research the choice to give respondents more response options was made in an effort to encourage them to focus on details of their PWU right from the beginning of the survey.

The period of two weeks prior to completion of the survey was chosen as a reasonable timeframe for which one can estimate the frequency of a behaviour one might have engaged in. Because it is expected that PWU is a behaviour some people engage in frequently, it was perceived it to be too arduous for participants to estimate actual

frequencies of their PWU for periods of one to twelve months in the past, as is typical for surveys exploring past behaviour within the model of the theory of planned behaviour (e.g. Conner & McMillan, 1999; Perugini & Bagozzi, 2001).

Examination of the measure’s skewness statistic and normality plots indicated that it was desirable to recode the data for this measure into four categories, rather than the original 11 categories suggested by the 11 answer options. These new categories were labelled almost never (1&2), low frequency (3&4), high frequency (5-9) and very high frequency (10+) of use. The recoding increased the degree of normality in the data distribution of the measure as is required for the criterion variables in the intended data analyses. High scores in this recoded measure indicated high frequency of PWU; low scores indicated low frequency of PWU. The recoded measure was used in the data analyses described later in this chapter.

The use of single-item questions in surveys is frequently debated in psychometrics (e.g. Braithwaite & Scott, 1991). In spite of the disadvantages that tend to be associated with them, single-item measures are regularly utilised, for example, as measures of job satisfaction (Nagy, 2002). These measures can, when used appropriately, reduce

ambiguity and the need for second guessing for the respondent, without having appreciable measurement loss (Barrett, 2002). Several comments made by participants in the pilot phases of the survey development showed that respondents of the lengthy survey had little patience for questions that appeared to repeatedly ask the same question, but in different

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ways. As one respondent put it even in the final version of the survey: “Although I support

your need for data and wanted to participate, I got fed up with your repeated questions asking very nearly the same thing, and which require a very careful (and therefore slow) reading of the question to discern the appropriate answer. I do not have the time to dredge through this kind of minutia [sic]. Sorry.” Therefore, it was judged it to be appropriate to

measure frequency and duration of PWU, as well as PWU out of habit, with single items.

5.3.1.2. Habitual frequency of PWU in an average week prior to completion of the survey (Q2)

How often the participant engaged in personal web use (PWU) during an average week out of habit, was explored with a single item (“In an average week, how often do you access the Internet at work for personal interest during work time, just out of habit?”). This question gave the respondents 11 answer options ranging from ‘never’ to ‘over 30 times’. The question posed in this survey is a modified version of a similar question used as habit indicator by Conner and McMillan (1999). The authors did not specify a timeframe in their habit-assessing question, assuming that once the habit is formed (in their case marihuana use), regular and consistent frequencies of engaging in it are achieved. The choice made in my research of asking about habitual PWU in a typical week, rather than a typical day, was made based on the notion that PWU habits can conceivably differ

depending on the day of the week, which may in turn influence the weekly habitual average.

The frequency steps in the response options used matched those of Q1 to allow for comparison and validity checks, as well as to retain participant focus on PWU details.

Examination of the habitual PWU measure’s skewness statistic and normality plots indicated that it was desirable to recode the data for this measure into six frequency categories of habitual use (1, 2, 3, 4&5, 6-8, 9+), rather than the original 11 frequency categories of the answer options. These six categories were chosen to allow the habitual frequency data to retain its character, while at the same time increasing the normality of the distribution, as it is necessary for the criterion variables in the intended data analyses. This collapsing of the categories also acknowledges that individuals may not be very precise in their frequency estimations, and therefore probably increased the reliability of the measure. High scores in this recoded measure indicated high frequency of use; low scores indicated low frequency of PWU. The recoded measure was used in the data analyses described later in this chapter.

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5.3.1.3. Average duration of PWU in one week (Q3)

The participants’ estimate of how long, in an average week, they engage in PWU was explored through a single-item measure. Respondents were prompted to fill in their estimate of the average weekly PWU duration in hours and minutes. For ease of use in further analyses involving the measure of the average PWU duration, these responses were subsequently recalculated into minutes only (“ Can you please give an estimate of how much time, on average, you spend accessing the Internet for personal Interest during work time in one week?”). The question was designed especially for this project.

Anandarajan, Simmers and Igbaria (2000) used a similar measure of time spent on the Internet, as did Lee, Lee and Kim (2004) for PWU duration. Anandarajan and his team, however, used a six-point duration scale (from ‘almost never’ to ‘more than three hours per day’). Because of the repeated media reports about excessive PWU duration in

organisations, the aim was to avoid reinforcing the impression that a particular duration is ‘a lot’ through placing it at one end of a fixed-point scale. This impression could possibly have encouraged social desirability biases or a central tendency bias of responding. Therefore, the decision to give respondents an open-ended response option was made, allowing each person to estimate his or her PWU duration without being forced to compare it to a

perceived norm.

The respondents were asked to estimate the average duration of their PWU in one week, based on similar arguments to those mentioned already. Estimation of PWU duration for one day would possibly have been more accurate, especially if one had asked for estimation of PWU duration the previous day, due to ease of recall. However, it is

questionable if this specific day had been representative of typical PWU duration. Although asking participants to estimate the duration of their PWU in an average/typical week may not result in the most accurate time estimate possible, it may be a more realistic estimation of their average PWU duration than reporting that of a single day. Additionally, past

research has suggested that individuals tend to be quite accurate in their estimation of average Internet use, when compared to data gathered through electronic monitoring of their work stations (e.g. Stanton, 1998).

For the intended data analyses it was necessary to achieve a reasonably high degree of normality in the data distribution of the criterion variables. Therefore the data obtained through this measure was recoded into five categories (virtually no use, up to half hour use, up to one hour, up to two hours and over two hours) which increased the

normality of distribution while maintaining the character of the information contained in the data. High scores in this recoded measure indicated longer periods of use; low scores

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indicated a shorter duration of PWU. The recoded measure was used in the data analyses described later in this chapter.

5.3.1.4. Activity type while engaging in PWU in the two weeks prior to completion of the survey (Qs 4-14)

The activities engage in by participants while using the Internet at work for personal interest were explored through the listing of ten possible online activities. The list of online activities was based on similar lists presented in previous research studies (e.g. Blau, Young, Ward-Cook, 2004; Mahatanankoon, Anandarajan and Igbaria, 2004; Polzer- Debruyne, 2002; Rotunda, Kass, Sutton & Leon, 2003), and in popular publications (e.g. Malachowski 2005; Malachowski & Simonini, 2007; MSNBC.com, 2006). On a five-point Likert scale the participants were asked to indicate how often they had engaged in each of these activities on a computer at work during the two weeks prior to completion of the survey. For example: “Please indicate for each activity below, how often during the last two (2) weeks you engaged in these activities on a computer at work: used personal web-based email such as hotmail, yahoo, etc… conducted personal, external business. The frequency response options ranged from ‘never’ to ‘very frequently’ and did not require the detail of frequency estimation that was requested in the answers to Q1 and Q2. This was consistent with previous research (e.g. Lim, 2002; Mahatanankoon, Anandarajan & Igbaria, 2004). While it may be possible to estimate precise frequencies for PWU in general, it is not reasonable to expect survey respondents to be able to estimate engagement in specific activities to a similar degree, without having asked them to keep a diary of their activities.

Again the timeframe of two weeks was chosen (as in Q1) to avoid capturing possible extreme circumstances in the single week prior to completion of the survey.

The item exploring the frequency of scanning through adult-oriented (sexually explicit) websites was subsequently eliminated from further analysis due to the relatively low number of responses (174 / 284) to this activity option. This exclusion of sexually explicit websites from an Internet activity list has previously been practised by Mahatanankoon, Anandarajan, and Igbaria (2004), and is therefore not seen as problematic in the context of this research.

The results of principal component analysis (PCA) with direct oblimin rotation suggested that the remaining nine listed activities fell into two separate components explaining 36.6% and 11.4% respectively of the total variance. The first component

encompassed activities that are related to interpersonal communication (CPWU). Six items loaded onto this factor with loadings between .79 and .50. The second component

described activities that involve the seeking and viewing of information (IPWU). Four questions loaded onto this factor with factor loadings between .83 and .43. The two factors of communication-related PWU (CPWU) and information-seeking PWU (IPWU) were moderately correlated (r = .407) and are similar to those reported by Mahatanankoon,

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Anandarajan and Igbaria (2004). For the protocol for the PWU investigations please refer to Appendix E. A summary of the factor loadings can be found in Table E5.2.1, in Appendix E.

Two new variables (CPWU and IPWU) were created through adding the scores of the questions loading highly onto each of these two factors and then creating the mean score. Higher values represented higher frequencies of engagement; lower values represented lesser frequencies of engagement in the respective activity types.

The reliability of the variable CPWU was acceptable with an alpha coefficient of .698, as well as for the variable IPWU which had an alpha coefficient of .635.

To address the skewness of CPWU (1.114) the data was recoded to regroup the lower end values, resulting in six frequency categories (0-1.2, 1.21-1.4, 1.6-1.8, 2-2.8, 3- 3.8, 4+). Higher values represented higher frequencies of access; lower values represented lower frequencies of access. This recoded measure was used in the data analyses

described later in this chapter.

To address the skewness of IPWU (.841) the data was also recoded to regroup the lower end values, resulting in six frequency categories (0-1.24, 1.25-1.33, 1.5-1.99, 2-2.99, 3-3.99, 4+). This recoded measure was used in the data analyses described later in this chapter.

In subsequent analyses the online activities that participants engaged in during personal web use were explored through two separate measures: that of communication- related PWU (CPWU) and that of information-seeking PWU (IPWU).

5.3.2. The predictorvariables

5.3.2.1 Moral norms regarding PWU (Qs 34, 35, 36)

The research model depicts the moral norms a person holds regarding PWU as the driver influence of the first described interaction cluster influencing PWU. The moral norms include the degree to which the participant perceives PWU being morally wrong, as going against his or her principles, and how guilty he or she feels when engaging in it. Three questions, exploring these three aspects of moral norms, asked the participants to indicate on seven-point Likert scales their agreement with different statements concerning PWU. The three questions were modified versions of similar questions used to measure moral norms in previous studies (Beck & Ajzen, 1991; Conner & McMillan, 1999), accounting for the topic area of PWU (e.g. “I intend to access the Internet at work for personal interest during work time in the next week.”) High scores indicated a high degree of personal norms against PWU; low scores indicated a low degree of personal norms against PWU.

PCA showed that all three questions loaded highly onto one single factor (factor loadings .93 to .92) and explain 85% of variance. For a summary of the factor loadings please refer to Appendix E, Table E5.2.2.

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The scores of the measure were calculated by adding the scores of the three individual items and creating the mean.

The coefficient alpha of this measure in the current study was .912, indicating the measure’s high reliability.

Closer examination showed the variable skewed (.253) with a relatively high number of low values, indicating that personal norms for a high number of people (22.1%) suggest that there is nothing wrong with PWU in the workplace. In an effort to regroup the lower end scores and to normalise the distribution, the data were recoded into seven categories (0-1, 1.33-1.99, 2-2.99, 3-3.99, 4-4.99, 5-5.99, 6+). This recoded measure was used in the data analyses described later in this chapter.

5.3.2.2 Acceptance of ledger neutralisation strategy (Q’s 79-84)

The research model suggests that the relationship between personal norm and the PWU criteria is moderated by the degree to which a person accepts the use of the ledger neutralisation strategy when engaging in PWU. For example: “In my opinion it is fair for me to use the Internet for non-job related reasons if I have put in extra work because I do not receive enough help and equipment.” The degree of the ledger strategy acceptance was measured through six questions, asking the participants to indicate on seven-point Likert scales their agreement with six statements concerning situations in which they perceive PWU to be fair and acceptable.

These questions were taken from Lim (2002), who adapted a measure developed by Hollinger (1991) to assess participants’ degree of neutralisation when engaging in PWU. The original measure assessing the use of the ledger strategy (Hollinger, 1991) had been altered by Lim (2002), and in this research project, through the addition of a conditional sentence: “In my opinion it is alright for me to use the Internet for non-job related reasons if …” The addition of this sentence resulted in the measure being one of principal acceptance of using the ledger strategy if one is in a described situation, rather than being a measure of actual use of the strategy in PWU situations. This alteration was seen as appropriate to accurately reflect the fact that unless the person is currently in a situation requiring neutralisation, statements made in hindsight tend to reflect rationalization rather than neutralisation. As previously argued (see chapter 3) using a measure of the principle acceptance of the ledger neutralisation strategy is the closest one can come to measuring its use of after the fact.

Principal component analysis, requesting a one-factor solution, indicated that one factor explained 56.1% of the variance. All six items of the scale had factor loadings above .40. The four items with the highest factor loadings (.89 to .76) represent the idea that one would be positively inclined to use the ledger strategy if one feels that a lot of time and effort has been given to the company without adequate rewards or because of some

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perceived shortcomings from the side of the organisation. The two lower loading items (.69, .49) represent the notion that one would have a positive attitude towards using the strategy if either stressed or bored at work, and had thus apparently paid one’s dues to the