Growth Need Strength (GNS) is a personality variable that has earned much attention by many organizations in recent years. People’s GNS scores have been the focus of several studies (de Jong, Rendel 2001, Fok, Lillian Y. 2000), although none seem to look for a relationship between Internet use and these scores. Media exposure has affected society in many ways over the years. The Internet has especially been a big media influence on the lives of millions of people every year.
Growth Need Strength
Growth Need Strength is a personality variable that explains to which degree individuals have needs for accomplishment, for learning, and for personal development. Individuals with high growth needs can also be predicted to develop high internal motivation when working on a complex, challenging job (Hackman and Oldham, 1980).
GNS scores have been studied in the past to assess its moderating effects on different variables. Saavedra and Kwun (2000) studied GNS testing it as a moderator for the relationship between job characteristics and pleasant or unpleasant affects. Job characteristics were basically divided into five sections: skill variety, task significance, task identity, autonomy and feedback. High-GNS managers reported more pleasant affect as well as less activated unpleasant affect in conjunction with enriched work (Saavedra and Kwun, 2000). By enriched work, the authors mean work that offers more use of skill variety, task significance, task variety, autonomy and feedback.
Another study, in which GNS scores were considered, tested the moderating effects of GNS and context satisfactions (pay, job security, benefits, etc.) on the relations of the Job Characteristics Model. This study assessed people’s job satisfaction using the Job Diagnostic Survey. This survey was developed by Hackman and
Oldham (1975) and was based on the Job Characteristics Model (JCM). This study found that the participants’ growth need strength score was not related to the context satisfactions (Tiegs, Tetrick, and Freid, 1992). In other words, peoples growth need score does not determine whether or not factors like competitive pay, pay raises, and job security will make them a satisfied employee.
In another study, Graen, Scandura, and Graen (1986) examined the moderating effect of GNS on production. In this study, three groups consisting of high, medium, and low GNS scorers’ performance were compared after receiving a new leadership intervention. Their performance was evaluated on the grounds of quality and quantity. The leadership intervention was based on the LMX (leader-member exchange). This model is designed to result in managers and subordinates working more interdependently (Graen, Scandura, and Graen, 1986). This study found that the high GNS group reacted more positively to the new leadership than did the low GNS group. In fact, the leadership intervention led to a 55% increase in their quantitative productivity.
A somewhat different study used GNS as an independent variable. Subjects were 80 males all preselected based on their GNS scores (Cellar, Frust, Vavra, and Fulton, 1992). In this study, the participants were divided into groups of high-GNS scorers and low-GNS scorers. They were asked to come into a room and make a spaceship out of an erector set. The participants were allowed 15 minutes each then were left alone and told they could, but did not have to, work on the erector ship. Then they were asked to recognize events that happened during the session. Afterwards, they were given feedback. The results found that high-GNS scorers were more likely to spend their “free time” working on the erector ship. These high-GNS scorers also most accurately recalled the events in the session. Low scorers took their free time reading a magazine and sometimes staring blankly. This informs employers what a low-GNS scorer would do in their downtime (or unsupervised time) in comparison to a high-GNS scorer.
A study by Britt and Teevan (1989) explored the relationship between GNS scores and fear of failure. The 57 participants were voluntary pool subjects from the psychology department. They were given two questionnaires that measured their fear of failure. Then the participants were given the GNS section of the Job Diagnostic Survey. Results in this study report that the male participants, who scored high on the GNS survey, had very little fear of failure. Females showed no significant correlation. Also noted is that individuals with low growth need strength would prefer gradual change at the workplace (Britt and Teevan, 1989).
Job Characteristics Model
The Job Characteristics Model is a model of work motivation that Hackman and
Oldham (1980) discuss in their book, Work Redesign. This important aspect of GNS research is a popularly used model of work motivation that considers five core job characteristics: skill variety, task significance, task identity, autonomy, and feedback. Skill variety is the domain of skills needed to perform a specific task. Task significance is how important an individual feels his/her role is at the organization. Task identity, is said to be “ the degree to which a job requires completion of a ‘whole’ and identifiable piece of work, that is, doing a job from beginning to end with a visible outcome (Hackman and Oldham, 1980). Autonomy refers to the amount of freedom an individual is given at the workplace. In other words, more autonomy means the less management. Finally, feedback is the degree to which an individual is evaluated and reported to by the organization. This model led to the development of the Job Diagnostic Survey, which assesses employee job satisfaction in relation to GNS.
Internet Use
Internet use and the effects of media exposure have been the focus of a great deal of research in past years. The Internet and media has been said to have positive and negative effects. Internet use can also be related to a number of factors. It can be used to for learning, teaching, or training in an organization. Research also suggests that children (the future of the workforce) are using the Internet at a much larger rate. Overall, findings suggest that the media does influence the behavior of society.
The majority of studies on the effect of media show the negative effect it has on society. Media refers to television, music, literature, and the Internet. Some studies show that after watching violent television programs participants become more aggressive. Others suggest that listening to violent music makes people more aggressive.
One study that suggests that Internet use can have detrimental effects on social involvement and psychological well being (Kraut, Lundmark, Patterson, Kiesler, Mukopadhyay and Scherlis, 1998). This study looked at Internet use over the course of 12 and 24 months. Findings suggest that the participants who used the Internet more became less socially involved and participant’s psychological well being actually decreased. Those participants who spent more time online reported a decrease in psychological well-being and increased levels of depression and loneliness. This study found that teenagers spent more time online than adults and there was no difference in the number of hours whites and minorities spent on the Internet.
One study by Scheuermann and Langford (1997) reported that Internet use was being abused at the workplace. In this study by Scheuermann and Langford (1997), researchers found that employees were using the Internet to waste time at work. Some employers did not mind because they thought their employees were learning something by using the Internet. They have a solution to go with the problem. The authors suggest, “Managing activities and requiring employees to be accountable form the appropriate route to productive Internet use” (Scheuermann and Langford, 1997).
The Internet has become so widely used that it has become a problem for some. Only one study, by Davis, Smith, Rodrigue, and Pulvers (1999), which was conducted on two college campuses, suggested that students did not overuse the Internet. This study found that average students used the Internet 6.9 hours per week (Davis, Smith, Rodrigue, and Pulvers, 1999). On the other hand, many studies suggest that some people become addicted. One by Pratarelli, Browne, and Johnson (1999), recognizes that some people have obsessive-like characteristics related specifically to their computer/Internet use, and these people also prefer on-line interactions as opposed to face-to-face interactions.
The goal of this study will be to see if people who use the Internet more have a higher growth need strength. If a relationship is found, employers could indirectly assess the growth need strength of prospective employees. Enabling organizations to assess this variable indirectly and minimize bias will help them to place the right employees in the right job positions. As a result, well-placed employees should have a lower number of voluntary turnovers, should have a lower absenteeism rate and overall be more satisfied with their job. By reducing turnover, absenteeism and increasing satisfaction, organizations will ultimately make more money.
Methods
Participants
Participants will be selected using a convenience quota sampling method. Participants will be 100 volunteers from the MTSU campus. Of these 100 participants, twenty-five will be white males, twenty-five will be black males, twenty-five will be white females, and twenty-five will be black females. The ages of all participants will be between 18 and 40 years. All subjects will sign a consent form prior to research (see Appendix A).
Materials/Apparatus
The materials used in this study will be a survey packet consisting of a consent form, the Computer Use Survey (Pratarelli, Browne, and Johnson, 1999) and the Job Diagnostic Survey (Hackman and Oldham, 1979). The Computer Use Survey, developed by Pratarelli, Browne, and Johnson (1999) is a survey used to assess the number of hours spent on the Internet and the types of activities people take part in while on the Internet. Only the information related to number of hours on the Internet will be relevant to this study. The Job Diagnostic Survey is a survey that is designed to assess how people react to their jobs. Only two sections of the Job Diagnostic Survey, sections six and seven, will be used in this study. The sections that are important to this study are used to assess the Growth Need Strength of individuals.
The consent form (Appendix A) will inform the participants that this study is to learn more about people’s Internet use. They will also be informed that they may leave the experiment at any time. Following the consent form, there is a short demographics form (see Appendix B) that questions age, sex, race and year of study.
The Computer Use Survey (see Appendix D) was developed in order to assess the number of hours spent by individuals on the Internet as well as other information about Internet use. This survey takes about twenty minutes and asks questions such as where the individual connects, how many hours per week he/she spends on personal email, how many hours he/she spends connected to a website (www.), and also questions the participant’s reasons for using the Internet. This survey was used by Pratarelli, Browne, and Johnson (1999) to assess Internet use for the purpose of studying Internet addiction. In considering the answers, the experimenters were able to determine whether or not an addiction was present. The important questions on this survey that concern the stated hypothesis are the questions dealing with the number of hours spent online.
The next survey that will be used will come from the Job Diagnostic Survey (see Appendix C). The Job Diagnostic Survey (JDS) was developed by Hackman and
Oldham (1979) and is provided in the back of their book. Two sections of this survey are dedicated to the assessment of Growth Need Strength. The first section (section six) to assess Growth Need Strength uses the Likert style, “Would like” format. Six questions from this section are directly related to GNS. This format asks participants to rate how much he/she “would like” to do something. Blanks are provided to the side of each statement. Numerical values are placed on the responses so that an answer of 4 reflects that the participant “would like” having a certain characteristic of a job only a little. A response of 10 reflects that the participants “would like” having this characteristic much more often. A selected six of the “would like” style questions are related to Growth Need Strength. In scoring this section, responses from item numbers 2, 3, 6, 8, 10, and 11 are averaged. Before averaging, each numbered response is to have 3 subtracted from it. Then the responses can be averaged.
The next section (section seven) of questions that assess GNS uses the “Job Choice” format. In section seven of the survey, twelve questions relate to GNS. In this format, participants are given two statements related to job types. The first statement is named A and the second statement is called B. The responses are based on a Likert type scale that ranges from 1-“strongly prefer A” to 5-“strongly prefer B”. If a participant responds with a 1, then he/she would strongly prefer the job described in statement A. An “A” statement is a statement such as: a job that pays good; whereas, a “B” statement is a statement such as: a job where creativity is important. In scoring this section of the survey, this particular section can be difficult. First, questions numbered 1, 5, 7, 10, 11, and 12 are given the value of the response provided by the participant. For example, if the participant responded 4, then the score for that question is 4. On the other hand, for items numbered 2, 3, 4, 6, 8, and 9, the response provided is reverse scored and will be subtracted from 6. For example, if a participant provides a response of 5, then the scorer will set up the equation: 6 – 5 = and the score for that item will be 1. Then the sum of theses scores is determined by averaging the points earned in section six with the points earned in section seven, but first some adjustments must be made.
In final scoring of the GNS scale, the “job choice” section must be converted from a five point scale to a seven point scale. In order to do this, the scores from the items provided in section seven must be entered in the formula: Y= 1.5X – .5. So, if a score of 7 is provided, then this digit will be entered in the place of Y. After the summary score from the “job choice” section is determined, it must be averaged with the summary score from the “would like” section of the questionnaire. This provides an overall GNS score. The higher the score, the higher the participant’s Growth Need Strength.
Procedures
To begin, experimenters will stand on campus at
Middle
Tennessee
State
University and ask random people to volunteer to take these surveys. They will first fill out a consent form (see Appendix A) and will be informed that they can quit at any time. They will also be informed that they will be told that they are helping organizations everywhere as well as mankind. In selecting participants, experimenters will be looking to meet the quota required, which is twenty-five white males, black males, white females and black females.
When the subjects are approached, they will be greeted as follows, “Hello, my name is (State name of experimenter) and I am gathering research about Internet use.” Then they will be given a chance to respond and no matter what they say, they are to be told, “You are helping mankind.” Next, if the participant agrees, they will fill out a consent form and be informed that there are two surveys along with a demographics sheet. Then they will be told that this will take about thirty minutes, probably less. Participants are also to be informed that the consent forms and the survey packets are collected and kept separate. Upon signing the consent form, the participants will be told that they may withdraw from the study at any time. If in agreement, the experimenter will proceed with the survey packet.
The survey packet contains a consent form, a demographics form, part of the Job Diagnostic Survey and the Computer Use Survey. Investigators will provide a pencil for the participants as well. When the investigators collect the consent forms, they will put them into one large envelope and the survey information will be put in a separate envelope, keeping the two separate. Then the data will be returned.
After signing the consent form, participants will be given two questionnaires, one that assesses the number of hours they spend online and one that will assess Growth Need Strength. Appendix D contains a copy of the Computer Use Survey and Appendix C contains the GNS section of the Job Diagnostic Survey. Participants will receive both of these surveys in a stapled packet. The participants will be told this survey is for a study of Internet use and what characteristics people like in their jobs. Participants will then fill out the surveys and will not be timed. The participants will be asked to return their surveys, but they may go away from the experimenter to answer the surveys. Once collected, the data can be analyzed as a whole and analyzed separately in the quota groups.
Results
The descriptive statistics have been placed on Table 1 and Table 2. The 100 participants will be broken down into four groups. These four groups will consist of 25 white males, 25 white females, 25 black males, and 25 black females. The scores for each group have been separated on the two tables (Tables 1 and 2). Table 1 will have the groups’ scores on the Internet use survey. Table two will have the scores for the GNS section they will take. Although these groups have been separated at this time, the reason for this is only for future research purposes. In this study, there will only be two groups, the high Internet use group and the low Internet use group. These groups’ Growth Need Strength scores will be considered, looking for a relationship between the two sets of scores. The high Internet use group will consist of those people who report to use the Internet 12 or more hours per week. The low Internet group will consist of the participants who report using the Internet less than 12 hours per week.
The correlation between the predictor variable (Internet use scores) and the criterion variable (GNS scores) will be calculated using the Pearson Product-Moment Correlational Coefficient. To get this statistic, the mean (X) scores for the Internet use survey will be placed on the X-axis, the mean scores reported on the GNS surveys will be placed on the Y-axis and the scores will be plotted on a scattergram. A sample scattergram has been provided (Figure 1) as a format for what the actual scattergram might resemble.
Discussion
The purpose of this study is to see if people who use the Internet more have a higher growth need strength. The statistical findings will determine the truth behind this idea. If significant evidence supports the hypothesis, that people who use the Internet more have a higher growth need strength, then organizations and employers in the future can determine whether or not applicants will be right for certain positions requiring high growth need strength. As of now, the only way to assess growth need strength is by having current employees complete the Job Diagnostic Survey. There, as of now, no way
to determine someone’s GNS scores when he/she is applying for employment at the organization. If this hypothesis is strongly supported, organizations could assess someone’s growth need strength prior to employing that individual, thus reducing turnover and potential risks to the organization.
It can be very important to assess applicant’s growth need strength. Jobs requiring high growth need strength are usually highly autonomous and provide little supervision or management. Therefore, placing someone in one of these positions who is not suited to be there could be costly and risky to an organization. The individual, if not properly evaluated beforehand, could damage the organization by costing them money, employees, or clientele. In the long run, the out-of-place employee will cost the organization a great deal of money or quit (increasing organizational turnover).
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