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Stephen L. Baglione Saint Leo University Donald R. Tapia School of Business, Saint Leo Florida, USA |
Joanne, Snead Saint Leo University Donald R. Tapia School of Business, Saint Leo Florida, USA |
William Smith Saint Leo University Donald R. Tapia School of Business, Saint Leo Florida, USA |
Louis Tucci The College of New Jersey 2000 Pennington Rd, Ewing Township, New Jersey,USA |
Through a tradeoff analysis, the research question addresses the impact of invasive employment and pre-employment activities and how they are affected by monetary compensation. Employment activities were separated into during and after work hours. Compensation was more important than employment and pre-employment conditions. Activities during work were slightly less important than salary while pre-employment and activities during the workday were much less important. Respondents were willing to accept invasive and potentially illegal activities. Respondents also evaluated the activities on whether they were invasive and legal. Less than half of the activities were viewed as invasive and slightly more than half were viewed as illegal.
Keywords: Workplace monitoring, electronic monitoring, performance management, job attitudes, compensation, employment practices, and conjoint analysisWith global competition, an aging population, limits to immigration,and a smaller worker supply (Blank, 2016), human capital is becoming even more valuable as America's most critical competitive advantage (Fareed et al., 2016; Kucherov and Manokhina, 2017). All things being equal, the organization with the most talented and committed human resources is most likely to be the most successful. In an age of ruthless, high-stakes competition, firms must recruit, train, and retain the most productive workforce. Recruitment influences the quality and quantity of applicants (Rivera, 2013). The human resources function must concentrate its efforts more than ever on hiring employees with needed talents to perform today's tasks and those of tomorrow (Rivera, 2013). The hiring process needs to concentrate further its efforts on hiring quality employees who will demonstrate loyalty to their work and the firm.Upfront investment in the screening during the hiring process reaps long-term financial and performance benefits for the firm. Every dollar effectively invested in the organization's human resources will pay substantial long-term
dividends by increasing productivity, morale, and company reputation while reducing training costs (Fatemi, 2016). According to the Department of Labor, a bad hire costs the organization 30 percent of the employee's first-year earnings (Fatemi, 2016). For that reason, today's successful organizations are making every effort to screen job applicants with the goal of hiring employees who possess the needed technical and operational skills and knowledge for the present and future position(s), but are also a good “fit” with the organization's performance expectations and culture. Organizations have become well aware of the cost of hiring the wrong person and its potential damage. Hiring does not ensure success; good employees must be trained, retained and poor performance or misbehavior eliminated. Business has for the past few decades invested heavily in productivity-enhancing technologies in every sector of the firm's non-human operations. A growing tool is employee monitoringto purportedly improve productivity and reward employee effort and success (Moore, 2000). Organizations have come to rely on monitoring. Employee monitoring can include video, audio, computer tracking, and sensors (Warren, Moffitt, and Byrnes, 2015). Technologies are quickly becoming indispensable components in the human resources activities of hiring, retaining, and enhancing employee productivity and reducing labor cost (Herath and Wijayanayake, 2009) and contribute to a firm's ability to compete globally.
The application of these technologies provides improved performance measures when it is consistent and objective (Alder and Tompkins, 1997). Aiello and Kob (1995) reinforce that monitoring technologies result in objective information and improvement of employee productivity. The technology available to businesses today in the hiring and oversight of employees creates both opportunities for ensuring employees “fit” with the values and expectations of the business, as well as, what others may view as the overreaching role of management in what some perceive as an intrusion into the lives of the employees.
Our research is basedon social exchange theory (SET). SET was developed in 1959 (Thibaut and Kelley, 1959) and postulates that human behavior is an exchange of rewards that breaks down when not reciprocated resulting in an imbalance (Pillay and James, 2015). Obligations can create aneconomic exchange (materialistic) or social (trust and reciprocity) (Blau, 1964). Exchanges are consummated because they are perceived to be mutually advantageous through the exchange of valuable resources.
More valuable rewards increase the likelihood of exchange. Perceived valuable resources include, for example, recognition (Shore and Shore, 1995), work-life benefits (Muse et al., 2008), fair treatment (Kacmar and Carlson, 1997), and training (Tian, Cordery,and Gamble, 2016).When people perceive the organization is supporting and valuing them, they will be motivated to reciprocate (Colquitt et al., 2013). Employees can develop an emotional attachment or affective commitment (Eisenberger, Fasolo and Davis-LaMastro, 1990) for the organization. Enduring long-term relationships result from greater reciprocity. Finally, strong organizations have a positive correlation between perceived organizational support and high-performance HR practices (Allen, Shore, and Griffith, 2003; Zhang and Jia, 2010). We now examine monitoring in the workplace.
Global pre-employment screening services are expected to grow from $3.74 to $5.46 billion USD by 2025 (Business Wire, 2018). One important tool is behavioral technologies which are employed to identify candidates that fit the organization's profile of what they believe will be successful employees. A longitudinal Australian study of police officers found psychological profiling reduced dropout and serious on-the-job errors (Lough and Ryan, 2010). The Employee Polygraph Protection Act “prohibits most private employers from using lie detector tests, either for pre-employment screening or during the course of employment,” although it is permissible for security firms and pharmaceutical manufacturers, distributors and dispensers (United States Department of Labor, 2016). A quarter of experts and more than half of laypeople have moderate or serious concerns about polygraph tests (Myers et al., 2006). Drug testing, both before and during employment, is designed to avoid negative consequences (e.g., productivity problems, morale issues, cost, and safety issues). Positive drugs tests usually decline after implementation (Delogu, 2007). Alcohol and drug policies were most effective when employers showed a broader concern for employee health and wellbeing and which were designed to be safe (Brown, Bain, and Freeman, 2008). Four percent of workers failed drug tests in 2015; the highest level in a decade (Farber, 2016). In 2017, Federal Reserve chairwoman, Janet Yellen, “linked increased opioid abuse to declining participation in the labor force among prime-age workers” (Schwartz, 2017). Excluding lost productivity, opioid abuse alone cost the U.S. economy $78.5 billion in 2013 (Schwartz, 2017).
Like the drugs they test for, these policies have consequences. Attitudes about pre-employment drug testing in the full-service restaurant industryhasled to extremes between those strongly agreeing and disagreeing with it which could not be separated by employee level or whether the organization uses it (Kitterlin and Moll, 2013). Racial differential implementation of drug testing may exist with blacks being more likely to be employed in a workplace that employs drug testing even after adjustments for demographics and occupations (Becker et al., 2014).
It has been estimated that these non-business work interruptions cost U.S. businesses $650 billion annually in lost employee productivity consuming almost 28 percent of the workday (Jackson, 2008). During the NCAA tournament alone, better known as March Madness, distracted or unproductive workers cost employers almost $1.9 billion (Challenger, Gray, and Christmas, 2015). Employers also have new technologies that allow management to monitor employees to ensure that outside factors are not diminishing the employees work productivity (Ciocchetti, 2011). The potential conflict occurs when employees have easy access to computers which are used for personal reasons while on company time(Ciocchetti,2011). Today's computer software can alert managers when employees are surfing the Internet instead of engaging in productive work (Businessweek, 2008). It is not merely using the Internet at work for personal use but the misuse of it. When employees misuse e-mail services and Internet access at work, employers may be held liable(Borstorff et al., 2007). Monitoring employee's email has become a common practice, with a reported 40 percent of employers conducting such surveillance (Ciocchetti, 2011). Currently, computer monitoring programs can record every command and keystroke made by the user, translate them into data, and remotely transmit the data to the employer. Software can now review emails and texts to identify disgruntal employees who may want to harm the organization (Parloff, 2016). Twenty-eight percent of employers who monitor employee emails have admitted to terminating an employee because of inappropriate email sent while at work (DePree and Jude, 2006). Many of these technologies are not new, but their application to the work environment is expanding. Company vehicles can easily be monitored to determine their location, as well as, the time at each location visited. McNall and Stanton (2011) studied GPS monitoring
through company-owned cellphones and privacy. Respondents indicated no difference when exposed to monitoring designed for punitive actions or productivity improvements. Related to GPS monitoring is awareness models. Improving collaboration among geographically dispersed workforces has resulted in awareness models, which indicate when a colleague is available. Prior research has found these models result in perception by employees of their inter-personal space being violated (Zweig and Webster, 2002). These technologies are evolving rapidly. Deloitte tested wearable employee badges to measure employee contributions to meetings and leadership (Kimura, 2015). An American company microchipped (voluntary) 72 of its 90 employees (Saner, 2018). Employee emotions are being monitored (Saner, 2018). Finally, post-accident drug testing has been shown to reduce workplace injury claims (Morantz and Mas, 2008).
Cohen and Cohen (2007) had graduate students in a human resource class evaluate monitoring policies. Discussion posts were evaluated as supporting, opposing, or neutral (containing positive and negative comments) on HR policies. The most “polarizing” policies were prohibiting off-duty smoking and requiring certain personal grooming; however, weight restrictions received the least positive comments. Respondents were concerned that allowing off-duty restrictions would mean encroachment on other off-duty activities. The most positive monitoring activities were GPS and Internet and email monitoring.
Another method of monitoring employee behavior outside the scope of normal employment are wellness programs. The United States spends over 15 percent of G.D.P on health and health-related services, but it consistently ranks low compared to developed countries on quality of health care (Rubenstein, 2009). These costs are rising for the nation and employers. Employees covered by health care at work receive at least half of their premiums from employers. Many companies are implementing wellness programs to mitigate rapidly increasing costs. The price increases come amid a broader debate about climbing health care costs and high premiums for Obamacare coverage. In 2016, the average family's health care plan increased 3.4 percent, faster than wage growth, to $18,142 (CNN.com, 2016). United Parcel Service initiated a wellness program that decreased absenteeism, increased productivity and resulted in a 60 percent reduction in on-the-job injuries (Bloom, 2008). Companies with the best wellness programs spend about $2,000 less per worker on health
costs (Clancy, 2015). Many use biometrics-driven software to identify problems before they arise, for example, blood pressure to waist circumference. Some firms charge less for health insurance for nonsmokers or employees who complete weight-loss programs. A national survey found 53 percent of Americans found this practice fair (Steinbrook, 2006). Motivating employee participation is easier when employees' education exceeds high school (Healey and Marchese, 2006). A potential cost-saving method would be genetic testing. Knowing who is predisposed to illness could lead to preventive medicine or nefariously job offers rescinded; however, it is illegal under the Genetic Information Nondiscrimination Act (Hudson, Holohan, and Collins, 2008). The United States House of Representative is considering a bill where companies could force employees into wellness programs that require genetic testing (Aronson, 2017). Many employers ban workplace romance because of the potential sexual harassment. After a romance ends, reconciliation can be perceived as harassment; this is especially true if there is a superior-subordinate relationship (Boyd, 2010). Boyd (2010) argues that workplace romance is not the problem but the behavioral consequences of it: harassment, conflict, and low productivity. Almost a quarter of Americans between the ages of 18 and 50 have a tattoo,and a positive association was found between tattoos and drinking and drug use (Laumann and Derick, 2006). When employees interact with customers, tattooed employees may be perceived differently than non-tattooed employees (Elzweig and Peeples, 2011). Almost a third of non-tattooed respondents believe those with tattoos are more likely to be involved in deviant behavior (The Harris Poll, 2012). Companies may have a legitimate reason for restricting tattoos, but that conflicts with people's self-expression demonstrated through those tattoos.
Monitoring has been shown to reduce turnover (Haley, Flint, and McNally, 2012), improve employee behavior (Pierce, Snow, and McAffee, 2015) and increase job satisfaction (Samaranayake and Gamage, 2012). As one might imagine, many employees resent the monitoring of their work. It is a constant, pervasive, permanent, and unblinking reality (Aiello, 1993; Aiello and Kolb, 1995; DelVecchio, 2014). Electronic monitoring can supply with what, in effect, is their “electronic footprint.” Numerous critics contend that this level of employee monitoring creates stress, turnover, reduced trust, and job dissatisfaction (Aiello, 1993; Aiello and Kobl 1995; Alder,
Noel, and Ambrose, 2006; Batt, Colvin and Keefe, 2002; Chory, Vela, and Avtgis, 2016; Holland, Cooper, and Hecker, 2015; Jeske and Santuzzi, 2015; Kizza and Ssanyu, 2005; Smith and Tabak, 2009; Zweig and Webster, 2002). Other negative effects that have been reported include a reduction in social concern for others, fewer interpersonal work relationships and a feeling of workplace isolation (Grant and Mayer, 2009; Jeske and Santuzzi, 2015). Less communication among employees may affect knowledge sharing and productivity (Kizza and Ssanyu, 2005). Affective commitment and job satisfaction were also reported to decline with close performance monitoring (Jeske and Santuzzi, 2015). For potential employees, active monitoring has been shown to reduce perceptions concerning organization's ethics, job acceptance,and satisfaction. Higher pay increased job acceptance, but only moderately changed job satisfaction (Holt, Lang, and Sutton, 2017).
Surveillance can change behavior; it can result in negative or deviant work behavior (Jensen and Raver, 2012). Ajzen's (1991) theory of planned behavior supports a link between surveillance and behavior. Higher surveillance has been shown to lead to more counterproductive work behaviors (Martin, Wellen, and Grimmer, 2016). To redress lost freedom from surveillance, employees retaliate (Lawrence and Robinson, 2007). One reality that must be considered is that an employee who has been “taking advantage” of a lax and unmonitored work environment will find fault with a performance monitoring system that is highly accurate. Many employees find personal web usage (PWU) or cyberloafing permissible and beneficial (Anandarajan, Simmers, and D'Ovidio, 2011). It is a respite from stress that allows employees to relax and enhance problem-solving skills. Eighty-two percent of computer-using employees report engaging in PWU (Garrett andDanziger, 2008). What we see is the evolution of human resources practices that are dramatically more intrusive into the lives of both potential employees and those currently employed. These practices can have deleterious effects. Van Gramberg, Teicher, and O'Rourke (2014) summarized a potentially irreconcilable dilemma: Monitoring tools may increase employee value to the organization, but these tools may impinge upon perceived autonomy and fairness jeopardizing productivity. As technology reduces boundaries between home and work, the balance between control and autonomy conflict increases. Management could view private correspondence on Facebook through a friend of a friend (Sprague, 2012).
Technology has supplemented and supplanted many pre- employment and retention programs. Electronic monitoring is ubiquitous and will only grow in the future. Can intrusive electronic monitoring be moderated or compensated by salary (Holt, Lang, and Sutton, 2017; White, 2004)? Our research focuses on two issues from an employee's perspective: perceived importance and invasiveness of human resource practices and tradeoff analysis among practices and salary. These activities encompass pre-employment, employment in the office, and employment outside the office (see Table 1).
We combine restrictive recruitment tools and workplace policies to see whether money will entice applicants to accept invasive human resource practices. Our research focused on screening and workplace activities common in the literature that may be deemed invasive. How important are these invasive practices to job applicants and do tradeoffs exist among these activities and monetary compensation? Our study attempts to determine whether employees are willing to accept these human resource practices based upon salary. Could firms offer a higher salary and receive approval from employees to undertake monitoring? A tradeoff analysis (conjoint) will be used to estimate this. To determine levels, we conducted two pretests. To determine the appropriate categories for salary, students were asked the dollar amount required to choose between two jobs that they equally preferred; they were provided with eight categories in $1,000 increments from one to eight. Twenty-four undergraduate students completed the pre-test with an average required salary of $4,416. Four
thousand was chosen for the incremental increase in salary to entice movement from one job to another. The second pre-test examined whether respondents believed the attributes measured were legal. Many features influence job satisfaction; however, we are focused here on money. Factors influencing job satisfaction include autonomy and influence; skill use; goals and challenge; variety; clarity; social relationships; money; physical security; significance; supportive supervision; career outlook; and fairness (Warr, 2007). In one study across 21 countries, the most important factors of job satisfaction are: income; advancement; security; independent and interesting work; and good relationships with managers and coworkers (Sousa-Poza and Sousa-Poza, 2000). Restrictive controls may influence locus of control and adversely affect employees (Elias, 2009). Intrinsic motivation (e.g., enjoy work, engaging, and fun) has been shown to affect job performance (Aryee, Walumbwa, Mondejar, and Chu, 2015). With wages relatively flat (Shambaugh et al., 2017) and artificial intelligence and robots potentially replacing many human jobs (Kak, 2018, Petroff, 2017), it is believed that salary will be the most important determinant; however, monitoring, from the literature, has been viewed by employees to be an invasion of privacy. Finally, we expect only genetic and polygraph testing to be viewed as illegal.
H1: The activities used in the analysis will be deemed an invasion of privacy. H2: Salary will be the most important determinant of job acceptance when compared with monitoring. H3: Nine of the 11 activities will be viewed as legal.
The survey was developed through multiple iterations among colleagues. It was pretested among faculty, staff, and students. It was administered at a southeastern non-secular universityto undergraduate students in eight business classes. Respondents completed the survey in class. Frequencies were examined to ensure no data was outside the range of feasible answers. Pairwise deletion was used (i.e., deleted by individual by question). Multivariate outliers were tested for through Mahalanobis distance. Tests were conducted at the .05 significance level. Conjoint analysis was chosen because of its extensive use in hundreds of decision-making studies over the last four decades. It also has been used in human resource studies (Bullinger and Treisch, 2015). The technique is used to measure consumer tradeoffs (Louviere, 1988). It is based on the notion that for many respondents, multi-attribute choices may be unmeasurable when examined individually according to each alternative's attributes, but they are
measurable when considered jointly in an overall evaluation (Green and Rao, 1971). This overall evaluation is statistically decomposed into separate and compatible part-worth estimates which when combined, usually by summing, give the "best" estimate of the respondent's overall evaluation, because part-worthsare measured in a common unit. Respondents evaluated 16 calibration profiles to estimate the conjoint model and two holdout samples, not used in the estimation, to assess model validity. The holdout profile scores are correlatedwith the calibration estimates. The number of profiles is determined through experimental design where attributes are orthogonal (i.e., zero correlation) and factional (i.e., respondents do not evaluate all possible attribute-level combinations). Main effects for attribute level are estimated. Each profile consists of one level of the four variables: pre-employment; employment at the office; employment outside the office; and salary. Profiles were evaluated on a 1 (definitely would not accept) to 10 (definitely would accept) scale (see Table 1 in Appendix for an example of the profile).
Individual utility scores are examined to determine the most important attribute and levels within attributes. The correlation between the observed and estimated preferences is estimated (Pearson's R), which measures model validity. Model fit is estimated through Kendall's tau, which is the correlation between the observed and estimated preferences of the holdout sample (non-parametric comparing rankings).
T h e s u r v e y i n s t r u m e n t w a s c o m p l e t e d b y 174undergraduate students in a southeastern non-secular university. Four surveys were unusable because of incomplete data or failure to take the exercise seriously (n=170). No question had more than 11 missing values.
Our sample is predominately business majors (97%) who live on campus (62%) with a third participating in intercollegiate sports (32%), and 75 percent belonging to a student organization (Table 2). They are mostly male (56%). Students are juniors (38%) followed closely by sophomores (32%) and seniors (27%). Half (50%) considered themselves as residing in a suburban environment, followed by approximately a quarter in urban (26%) and rural (23%) environments. They work an average of 13 hours (including internships and work-study) and spend 11 hours studying or doing homework. On average, they pay for 37 percent of total college expenses. The average GPA (self-reported) is 3.18
More than half of respondents believe the following activities are an invasion of privacy: genetic and polygraph testing; GPS monitoring at work; prohibiting romantic relationships between employees; andprohibiting dangerous activities outside of work. Hypothesis one is partially supported (five of 11 are viewed as an invasion of privacy). Respondents are more tolerant than anticipated, although 25 percent of respondents evaluate all as invasive except for drug testing. Hypothesis three is partially
supported (six of 11 viewed as legal). Drug testing, psychological testing; reviewing of company-owned computer hard drives; video surveillance at work; prohibitingvisible tattoos; and prohibiting romantic relationships between employees are deemedlegal. Genetic testing was deemed legal by 50 percent of respondents. A third of respondents found all activities illegal.
The conjoint analysis results for the relative importance of the attributes indicate salary has the most influence on overall preference for employment, followed closely by employment at the office. Pre-employment and employment outside the office were comparable and of lesser importance (Table 4). (Note: Relative importance weights sum to 100 and part-worth within a category sum to one.)
Examining the importance scores, which sum to one, because they take the utility range for each factor separately and dividing by the sum of the utility ranges for all factors (averaged across all respondents), shows that salary has the most influence on overall preference, while outside employment is least important in determining overall preference (Table 5).If respondents are paid enough, they are willing to accept intrusive practices into their work life and outside work. Hypothesis two is supported.
Pearson's R is the correlation between observed (rated by respondents) and estimated preference (computed by part-worths). Kendall's tau measures the correlation between the observed (rated by respondents) and estimated preferences (computed by part-worths) for the holdout profiles (not used in estimating utilities). Both correlations are high, indicating good model fit (Table 4). Holdouts always produce smaller correlations than the calibration model.
The utility (part-worth) scores indicate a preference for individual levels of variables. Higher utility indicates greater preference.Salary is the most important variable and $12,000 above the industry average for that position (.676) is the highest utility followed by $8,000 (.308), $4,000 (.174), and comparable to the industry average (-1.16) (Table 6). For pre-employment tests, respondents indicated that genetic tests for diseases and illnesses (-.336) had the highest negative utility among the four levels. Using a polygraph (-.150) and a psychological test (-.185) both had negative utilities. Drug testing had a positive utility (.671). Once hired (employment at the office), monitoring company-owned computers (.555) and video surveillance (.442) in all areas (including offices and excluding bathrooms) produced positive utilities. Monitoring employees during the workday using an app on their smartphone produced a high negative utility (-1.07).
The prohibition of tattoos and body piercings (excluding earrings for women) was positive (.071). Subjecting employees (employment outside the office) to mandatory drug testing (urine) every six months (.098), prohibiting romantic relationships among co-workers (.265), and mandatory wellness programs (paid by employer), which includes 60 minutes of exercise weekly outside work (unpaid) and annual physicals (.122) all produced positive utility.Prohibiting dangerous off-duty activities (e.g., skydiving and bungee jumping) (-.485) resulted in negative utility.There is a positive relationship with salary: higher salary indicates higher utility; however, only a salary of $12,000 above the industry average produced a positive utility.
Since the utilities are all expressed in a common unit, each can be added to give the total utility of any combination. The highest utility profile is a salary of $12,000 with drug tests before employment, reviewing company-owned hard drives through monitoring software, and the prohibition of romantic relationships between employees. The lowest utility profile is a salary comparable to the industry average for that position with genetic testing as a pre-condition of
employment, monitoring employee movement during the workday using GPS through an app on his or her smartphone, and prohibiting dangerous off-duty activities. Finally, a salary of $12,000 has a utility greater than each negative activity except GPS monitoring. The results in Table 6 are shown graphically in Figures one through four.
In today's increasingly globally competitive environment where many positions are being replaced by artificial intelligence and robots (Kak, 2018;Petroff, 2017) and wages are relatively flat (Shambaugh et al., 2017), college students are discovering an increasingly intrusive workplace environment where organizations are employing newer human resources technologies to ensure that employees remain highly productive.Increasingly sophisticated monitoring technologies are becoming a managerial tool in the achievement of maximizing employee productivity to compete globally. Potential employees reported that these objectionable workplace restrictions would be acceptable if higher compensation was offered. Compensating them above the average for that position proves to be the most significant factor in the identification of potential employees' willingness to accept many of these restrictive human resources policies. This confirms prior research where higher pay increased job acceptance, although it did not increase job satisfaction (Holt, Lang, and Sutton, 2017). Based on social exchange theory, reciprocity exists because employees are being compensated for accepting intrusive human resource practices (Blau, 1964; Thibaut and Kelley, 1959). Exchange occurs because it is seen as mutually advantageous. Restrictive on-the-job activities were second most important. Pre-employment and outside-the-office activities were markedly less important.The latter contradicts prior research where off-duty activities were polarizing (Cohen and Cohen, 2007).
Almost half of the activities were viewed as an invasion of privacy, which is more tolerant than anticipated for these constant, pervasive, and permanent technologies (Aiello, 1993; Aiello and Kolb, 1995; DelVecchio, 2014). Its negative effects have been well-documented in the literature (Aiello, 1993; Aiello and Kobl 1995; Alder, Noel, and Ambrose, 2006; Batt, Colvin and Keefe, 2002; Chory, Vela, and Avtgis, 2016; Holland, Cooper, and Hecker, 2015; Jeske and Santuzzi, 2015; Kizza and Ssanyu, 2005; Smith and Tabak, 2009; Zweig and Webster, 2002).Slightly more than a third were viewed as illegal: polygraph; GPS monitoring; mandatory wellness; and prohibiting off-duty activities. From that list, polygraph; GPS monitoring; and prohibiting off-duty activities were invasive and illegal. Respondents appear prepared, at least intellectually, to enter a workforce where monitoring is ubiquitous. With pre-employment activities, drug and psychological testing were viewed as legal but not invasive (higher than 50%) while genetic testing and polygraph were seen as
invasive and illegal. The values for polygraph were within three points,and half found genetic testing legal. The conjoint study found drug testing acceptable (positive utility) and the others unacceptable with generic testing the most unacceptable. Drug testing was acceptable, non-invasive, and legal. Prior research on drug testing indicated it might lead to employee attrition (Mastrangelo and Popovich, 2000; Smither, et al., 1996); however, our results show positive results for drug testing for during pre- and ongoing-employment. The misgivings about polygraphs in prior studies is confirmed (Myers et al., 2006). Differences between invasive and legality continued with employment at the office. Over 70 percent of respondents, viewed computer and video monitoring and tattoo prohibition as legal but not invasive. The conjoint results support this will all three having positive utility. With a quarter of 18 to 50 year-olds having tattoos (Laumann and Derick, 2006), a higher proportion in our study (43%) found the prohibition of them invasive. Monitoring their location through their smartphone was seen by almost all as invasive, legal by a smaller percentage, and providing negative utility in the conjoint analysis. Since this would require an app on an employee's smartphone and smartphones are for many an extension of their lives, this overwhelming disdain is not unexpected. A recent study in Australia found users under 36 years of age used their cellphone more than three hours daily (Andrews, 2017). This confirms McNail and Stanton's (2011) results that GPS monitoring can result in invasiveness if employees cannot turn their smartphones off.
Out-of-the-office activities indicate two of three are invasive and one legal. (Note: This includes drug testing which we evaluated in pre-employment.) Prohibiting romantic relationships between employees is viewed as invasive, legal, and had positive utility. This is because prohibiting dangerous off-duty activities had very high negative utility; it also was viewed as invasive and illegal. The other three activities had positive utility. Mandatory wellness programswere not viewed as invasive but illegal. Our study confirms prior research will show a positive relationship between education and willingness to participate in a worksite wellness program (Healey and Marchese, 2006).
This research addresses some organizational issues which are currently evolving. Businesses have begun to take enhanced efforts to address the issue of employee productivity. Research has clearly identified significant levels of lost employee productivity due to employees
simply ignoring current organizational policies or management's lax enforcement of these policies. Organizations' investments in monitoring technology is attempting to address this. Technologies that allow companies to monitor employee work productivity are both being improved and are becoming increasingly affordable. The stage has now been set for a 21st-century redefinition of workplace culture and employee performance expectations. Employers wish to modify workplace behaviors to address the causes of insufficient employee productivity. These workplace policies are very likely to be viewed by some as an intrusion into both their work and personal lives. Overlaying this is a workforce facing stagnant wages and fearful of replacement by artificial intelligence, robots,and globalization.
This research involved millennials who are preparing to enter the workforce. Their survey responses are a projection of how this generation will likely respond to the work rules and policies which they will encounter in their first full-time employment. The research attempted to discover those organizational and human resources policies that they find to be reasonable or acceptable and those which they believe to be overly restrictive or a violation of their privacy rights.This research studied the impact of salary on the acceptance or rejection of organizational and human resources policies.As the potential employee is offered an increasingly higher salary, the research identifies the policies which become increasingly acceptable. Financial compensation serves to modify the respondent's attitudes regarding managements' rights to intrude into their work and private lives.
Workplace satisfaction is multi-faceted (Warr, 2007; Sousa-Poza and Sousa-Poza, 2000). Our research only addresses monetary compensation and invasive human resource, but we did not measure employee stress, turnover, and job dissatisfaction directly.Nor did we measure the potential for deviant and counter-productive behavior from the stress of surveillance (Jensen and Raver, 2012; Martin, Wellen, and Grimmer, 2016). Respondents' perceived organizational support, locus of control, and intrinsic motivation were not measured. Did these intrusive high-performance human resource practices engender employee perceptions that the organization does not value and nor care for its employees (Eisenberger, Huntington, Hutchinson, and Sowa, 1986)? Did respondents link these practices with assisting the organization in achieving its objectives? Can trust in management be enhanced through explaining its actions (Chen and Ross, 2007)? Halpern et al. (2008) believe
showing communication practices in the firm will engender commitment from employees to monitoring. We could have been more explicit in our description of intrinsic motivation. Restrictive controls may influence locus of control and adversely affect employees (Elias, 2009). Intrinsic motivation (e.g., enjoy work, engaging, and fun) has been shown to affect job performance (Aryee et al., 2015). Strong organizations have a positive correlation between POS and high-performance HR practices (Zhang and Jia 2010; Allen, Shore, and Griffeth 2003).
Organizations have a plethora of data available on employees: demographics, disciplinary actions, dispute resolutions, education, employment history, hours worked and productivity, skills, supervisor evaluations, etc., and this could be combined with “big data” from blog postings, emails, electronic calendars, geo-location data from cellphones, text messages, Word files, etc. (Angrave et al., 2016). Technology is advancing in this area. Rasmussen and Ulrich (2015) show an offshore drilling company examined leadership and turnover to reduce accidents and maintenance time and improve productivity and customer satisfaction. Future research can trace through problem formulation to solution.
Our sample included traditional-aged college students who have limited full-time experience. For many, their knowledge on the topic is vicarious. Are the results generalizable to other age groups and industries? The design was limited to main effects; thus, no interaction effects were tested. We did not directly measure why practices were deemed unacceptable. Advocates of monitoring believe it increases productivity and lessens potential liability (e.g., litigation). Our study does not measure whether productivity is improved or diminished through monitoring, which relates to internal validity. Internal validity also is affected since no factors (e.g., personality) were controlled for. The legality of an activity will vary across states. For example, California's Constitution says “No person or entity in this state shall use an electronic tracking device to determine the location or movement of a person" (Bloomberg Businessweek, 2015). Finally, the conjoint study part-worths and demographics could be examined in a latent class clustering to determine segments. Segment-level results were not analyzed because the sample size was too small.
Aiello, J.R. (1993), “Computer-Based Work Monitoring: Electronic Surveillance and Its Effects,” Journal of Applied Social Psychology, 23(7), 499–507.
Aiello, J.R. and K.J. Kolb (1995), 'Electronic Performance Monitoring: A Risk Factor inWorkplace Stress', in S.L. Sauter and L.R. Murphy (editors), Organizational Risk Factors for Job Stress (Washington, DC: American Psychological Association), pp. 163–179. Ajzen, I. (1991), “The Theory of Planned Behavior,” Organizational Behavior and the Human Decision Processes, 50(2), 179-211. Alder, G. Stoney, Terry W. Noel, and Maureen L. Ambrose (2006), “Clarifying the Effects of Internet Monitoring on Job Attitudes: The Mediating Role of Employee Trust,” Information & Management, 43(7), 894-903. Alder, G. Stoney and Phillip K. Tompkins (1997), “Electronic Performance Monitoring: An Organizational Justice and Concertive Control Perspective,” Management Communication Quarterly, 10(3), 259-288). Allen, David G., Lynn M. Shore, and Rodger W. Griffeth(2003),“The Role of Perceived Organizational Support and Supportive Human R e s o u r c e P r a c t i c e s i n t h e Tu r n o v e r Process,”Journal of Management, 29(1), 99–118. Anandarajan,Murugan,Claire A. Simmers,and Rob D'Ovidio (2011), “Exploring the Underlying Structureof Personal Web Usage in the Workplace,” Cyberpsychology, Behavior, and Social Networking, 14(10), 577-583. Andrews, Kylie (2017), “Smartphone Survey: The Fascinating Differences in the Way We Use Our Phones,” ABC Science, http://www.abc.net. au/news/science/2017-10-13/smartphone-survey-results-show-fascinating-differences-in-usage/9042184, retrieved March 4, 2018. Angrave, David, Andy Charlwood, Ian Kirkpatrick, Mark Lawrence, and Mark Stuart (2016), “HR and Analytics: Why HR is Set to Fail the Big Data Challenge,” Human Resource Management Journal, 26(1), 1-11. Aronson, Louise (2017), “Don't Discriminate Against Mutants Like Me,” New York Times, retrieved March 23, 2017, https://www.nytimes.com /2017/03/23 /opinion/sunday/dont-discriminate-a g a i n s t - m u t a n t s - l i k e - m e . h t m l ? a c t i o n = click&pgtype=Homepage&clickSource=story-h e a d i n g & m o d u l e = o p i n i o n - c - c o l - r i g h t - region®ion=opinion-c-col-right-region&WT .nav=opinion-c-col-right-region
Aryee, Samuel, Fred O. Walumbwa, Reuben Mondejar and Chris W.L. Chu (2015), “Accounting for the Influence of Overall Justice on Job Performance: Integrating Self-Determination and Social Exchange Theories,” Journal of Management Studies, 52(2), 231-252. Batt, Rosemary, Alexander J.S. Colvin and Jeffrey Keefe (2002), “Employee Voice, Human Resources Practices, and Quit Rates: Evidence from the Telecommunications Industry,” Industrial & Labor Relations Review, 55 (4), 573-595. Becker, William C., SalimahMeghani, Jeanette M. Tetrault, and David A. Fiellin (2014), “Racial/Ethnic Differences in Report of Drug Testing Practices at the Workplace Level in the U.S.,” The American Journal on Addictions, 3, 357-362. Blank, R. M. (2016), “What Drives American Competitiveness?,” Annals of the American Academy of Political and Social Science, 663(1), 8-30. Blau, Peter M. (1964), Exchange and Power in Social Life, New York: Wiley. Bloom, Sarah (2008), “Employee Wellness Programs,” Professional Safety, 53 (8), 41-42. Bloomberg Businessweek (2015), “Testing Workers' Digital Privacy Protections,” May 25, p. 33-34. Borstorff, Patricia C., Glenn Graham and Michael B. Marker (2007), “E-Harassment: Employee Perceptions of E-Technology as a Source of Harassment,” Journal of Applied Management and Entrepreneurship, 12 (2), 44-61. Boyd, C. (2010), “The Debate Over the Prohibition of Romance in the Workplace,” Journal of Business Ethics, 97, 325-338. Brown, Stephen K., Paul Bain, and Melanie Freeman (2008), “Employee Perceptions of Alcohol and Drug Policy Effectiveness: Policy Features, Concerns about Drug Testing, and the Key Role of Preventative Measures,” Drugs, Education, Prevention, and Policy, 15(2), 145-160. Bullinger, B. and Treisch, C. (2015), “Herding Cats – Future Professionals' Expectations of Attractive Employers,” German Journal of Research in Human Resource Management, 29(2), 149-177. Business Wire (2018), “$5.46 Billion Employment Screening Service Market to 2025 - Global Analysis & Forecasts by Services & Application,” February 2, ResearchAndMarkets.com
Businessweek, “A Way to Tell If They're Slaving Away or Surfing,” January 14, 2008, 56. CNN.com (2016), “Health Care Costs Rise by Most in 32 Ye a r s , ” h t t p : / / m o n e y . c n n . c o m / 2 0 1 6 /09/16/news/economy/health-care-costs-rise-most-in-32years/,retrieved December 17, 2016. Challenger, Gray,and Christmas (2015), “It's March Madness: This Year's Madness Could Cost $ 1 . 9 B , ” h t t p s : / / w w w. c h a l l e n g e r g r a y. com/press/press-releases/its-march-madness-years-madness-could-cost-19b, retrieved September 2, 2015. Chen, Jengchung V. and William H. Ross (2007), “Individual Differences and Electronic M o n i t o r i n g a t Wo r k , ” I n f o r m a t i o n , Communication & Society, 10 (4), 488-505. Chory, Rebecca M., Lori E. Vela, and Theodore A. Avtgis(2016), “Organizational Surveillance of Computer-Mediated Workplace Communication: Employee Privacy Concerns and Responses,” Employee Responsibilities and Rights Journal, 28, 23-43. Ciocchetti, Corey A. (2011), “The Eavesdropping Employer: A Twenty-First Century Framework for Employee Monitoring,” American Business Law Journal, 48 (2), 285-369. Clancy, Heather (2015), “Gym Class Heroes,” Fortune Magazine, September 1, 2015, p. 42. Cohen, Cynthia F. and Murray E. Cohen (2007), “On-Duty and Off-Duty: Employee Right to Privacy and Employer's Right to Control in the Private Sector,” Employee Responsibilities Rights Journal, 19(4), 235-247. Colquitt, Jason A., Brent A. Scott, Jessica B. Rodell and Michael J. Wesson (2013), “Justice at the Millennium, a Decade Later: a Meta-Analytic Test of Social Exchange and Affect-Based Perspectives,” Journal of Applied Psychology, 98(2), 199–236. Delogu, Nancy N. (2007), “Essential Elements of a Drug-Free Workplace Program,” Professional Safety, 52 (11), 48-51. DePree, Chauncey M., Jr. and Rebecca K. Jude (2006), “Who's Reading Your Office E-mail? Is That Legal?,” Strategic Finance, 87 (10), 44-48. DelVecchio, Susan K. (2014), “The Duality of E-Monitoring: Harmful or Helpful,”The Marketing Management Journal, 24(2), 186-205. Elias, Steven M. (2009), “Restrictive Versus Promotive
Control and Employee Work Outcomes: The Moderating Role of Locus of Control,” Journal of Management, 35(2), 369-392. Eisenberger, Robert, Robin Huntington, Steven Hutchinson, and Debora Sowa (1986),“Perceived Organizational Support,”Journal of Applied Psychology, 71(3), 500–507. Farber, Madeline (2016), “The Number of Workers Flunking Drug Tests Is at a 10-Year High,” Fortune, September 15, http://fortune.com /2016/09/15/drug-tests-positive-work/ Fareed, Muhammad, Wan S.W.M. Noor, Mohd F.M. Isa, and Sri S.M.M. Salleh (2016), Developing Human Capital for Sustainable Competitive Advantage: The Roles of Organizational Culture a n d H i g h - P e r f o r m a n c e Wo r k S y s t e m , International Journal of Economic Perspectives, 10(4), 655-673. Fatemi, Falon (2016), “The True Cost Of A Bad Hire -- It's More Than You Think,” Forbes, September 28, 2 0 1 6 , a c c e s s e d o n M a y 2 5 , 2 0 1 8 , https://www.forbes. com/sites/falonfatemi /2016/09/28/the-true-cost-of-a-bad-hire-its-more-than-you-think/#156624174aa4 Garrett, R. Kelly and James N. Danziger (2008), “Disaffection or Expected Outcomes: U n d e r s t a n d i n g P e r s o n a l I n t e r n e t U s e DuringWork,”Journal ofComputer-Mediated Communication,13(4), 937–958. Grant, Adam M. and David M. Mayer (2009), “Good Soldiers and Good Actors: Prosocial and Impression Management Motives as Interactive Predictors of Affiliative Citizenship Behaviors,” Journal of Applied Psychology, 94(4), 900-912. Green, Paul E. and Vithala R. Rao (1971), "Conjoint Measurement for Quantifying Judgmental Data," Journal of Marketing Research, 8 (August), 355-363. Halpern, David, Patrick J. Reville, and Donal Grunewald (2008), “Management and Legal Issues Regarding Electronic Surveillance of Employees in the Workplace,” Journal of Business Ethics, 80(2), 175-180. Haley, Lynn M., Douglas Flint, and Jeffrey J. McNally (2012), “The Effects of Employee Perceptions of Monitoring Procedures on Turnover Problems and Perspectives in Management, 10(3), 52-59. Healey, Bernard J. and Marc Marchese (2006), “The Use of Marketing Tools to Increase Participation in
Worksite Wellness Programs,” Academy of Health Care Management Journal, 2, 75-83. Herath, H. M. P. S. and W. M. J. I. Wijayanayake (2009), “Computer Misuse in the Workplace,” Journal of Business Continuity & Emergency Planning, 3(3), 259–270. Holland, Peter J., Brian Cooper, and Rob Hecker (2015), “Electronic Monitoring and Surveillance in the Workplace,”Personnel Review, 44(1), 161–175. Holt, Matthew, Bradley Lang, and Steve G. Sutton (2017), “Potential Employees' Ethical Perceptions of Active Monitoring: the Dark Side of Data Analytics,” Journal of Information Systems, 31(2), 107-124. Hudson, Kathy L, M.K. Holohan and Francis S. Collins (2008), “Keeping Pace with the Times—The Genetic Information Nondiscrimination Act of 2008,” The New England Journal of Medicine, 358 (25), 2661. Jackson, Maggie (2008), “May We Have Your Attention, Please?,” Businessweek, June 23, 55-56. Jensen, J. M., & Raver, J. L. (2012). When self-management and surveillance collide: Consequences for employees' trust, autonomy, and discretionary behaviors. Group & Organization Management, 37, 308–346. Jeske, Debora and Alecia M. Santuzzi (2015), “Monitoring What and How: Psychological Implications of Electronic Performance Monitoring,” New Technology, Work, and Employment, 30(1), 62-78. Kacmar, K. Michele and Dawn S. Carlson (1997), “Further Validation of the Perceptions of Politics Scale (POPS): A Multiple Sample Investigation,” Journal of Management, 23(5), 627–658. Kak, Subhash (2018), “Will Robots Take Your Job? Humans Ignore the Coming AI Revolution at Their Peril,” NBC News, February 7, accessed M a r c h 4 , https://www.nbcnews.com/think/opinion/will-robots-take-your-job-humans-ignore-coming-ai-revolution-ncna845366 Kimura, Tara (2015), “How New Data-Collection Technology Might Change Office Culture,” http://www.cbc.ca/news/technology/how-new-data-collection-technology-might-change-office-culture-1.3196065 Kitterlin, Miranda and Lisa Moll (2013), “Employee Attitudes toward Pre-employment Drug Testing
in the Full-service Restaurant Industry,” Journal of Foodservice Business Research, 16(3), 313–326. Kizza, J.M. and J. Ssanyu (2005), “Workplace Surveillance,” in Electronic Monitoring in the Workplace: Controversies and Solutions,” Editor John Weckert, Hershey PA: Idea Group Publishing, 1-18. Kucherov, Dmitry and Daria Manokhina (2017), “Evaluation of training programs in Russian manufacturing companies,” European Journal of Training and Development, 41(2), 119-143. Laumann, A. E., and Derick, A. J. (2006), “Tattoos and body piercings in the United States: A national data set,” Journal of the American Academy of Dermatology, 55(3), 413-421. Lawrence, Thomas B. and Sandra L. Robinson (2007),“Ain'tMisbehavin: Workplace Deviance as OrganizationalResistance,”Journal of Management, 33(3), 378–394. Lough, Jonathan and Michael Ryan (2010), “Research Note: Psychological Profiling of Australian Police Officers: A Three-Year Examination of Post-Selection Performance,” International Journal of Police Science & Management, 12(3), 480-486. Louviere, Jordan J. (1988), Analyzing Decision Making: Metric Conjoint Analysis. Newbury Park, Sage Publications. Martin, Angela J.,Jackie M. Wellen, Martin R. Grimmer ( 2 0 1 6 ) , “ A n E y e o n y o u r Wo r k : H o w Empowerment Affects the Relationship between Electronic Surveillanceand Counterproductive Work Behaviours,”The International Journal of Human ResourceManagement, 27(21), 2635-2651, Mastrangelo, P., & Popovich, P. (2000). Employees' Attitudes TowardDrug Testing, Perceptions of Organizational Climate, and Withdrawal from the Employer,” Journalof Business and Psychology, 15(1), 3–18. McNall, Laurel A. and Jeffrey M. Stanton (2011), “Private Eyes Are Watching You: Reactions to Location Sensing Technologies,” Journal of Business Psychology, 26(3), 299-309. Moore, A.D. (2000), “Employee Monitoring and Computer Technology: Evaluative Surveillance v. Privacy,” Business Ethics Quarterly, 10(3), 697-709.
Morantz, Alison D. and Alexandre Mas (2008), “Does Post-Accident Drug Testing Reduce Injuries? Evidence from a Large Retail Chain,” American Law and Economics Review, 10(2), 246-303. Muse, Lori, Stanley G. Harris, William F. Giles, and Hubert S. Field (2008), Work-Life Benefits and Positive O rg a n i z a t i o n a l B e h a v i o r : I s T h e r e a Connection?,” Journal of Organizational Behavior, 29, 171-192. Myers, Bryan, Rachel Latter, M. Kathrine Abdollahi-Arena (2006), “The Court of Public Opinion: Lay Perceptions of Polygraph Testing,” Law Human Behavior, 30(4), 509-523. Pierce, Lamar, Daniel Snow, and Andrew McAfee (2015) Cleaning House: The Impact of Information Technology Monitoring on Employee Theft and Productivity, Management Science, 6(10), 2299-2319. Pillay, Soma and Reynold James (2015), “Examining Intercultural Competency through Social Exchange Theory,” Journal ofTeaching and Learning in Higher Education, 27(3), 320-329. Piotrowski, Chris and Terry Armstrong (2006), “Current Recruitment and Selection Practices: A National Survey of Fortune 1000 Firms,” North American Journal of Psychology, 8 (3), 489-496. Parloff, Roger (2016), “Spy Tech That Reads Your Mind,” Fortune, http://fortune.com/insider-threats-email-scout/, retrieved August 1. Petroff, Alanna (2017), “U.S. Workers Face Higher Risk of Being Replaced By Robots. Here's Why,” CNN.com, retrieved March 24, 2017 http://money.cnn.com/2017/03/24/technology/robots-jobs-us-workers-uk/index.html Rasmussen, Thomas and Dave Ulrich (2015), “Learning from Practice: How HR Analytics Avoids Becoming a Fad,” Organizational Dynamics, 44(3), 236-242. Rivera, Ray (2013), “HR Analytics And Competitive Advantage,” Forbes, access 5/2/2018, https://www.forbes.com/sites/sap/2013/02/05/hr-a n a l y t i c s - a n d - c o m p e t i t i v e - advantage/#3322fc68ce14 Rubenstein, Daniel Charles (2009), “The Emergence of Mandatory Wellness Programs in the United States: Welcoming, or Worrisome?,” Journal of Health Care Law & Policy, 12(1), 99-118. Samaranayake, Viraj and ChandanaGamage (2012), “Employee perception towards electronic
monitoring at workplace and its impact on job satisfaction of software professionals in Sri Lanka,” Telematics and Informatics, 29(2), 233-244. Saner, Emine (2018), “Employers are Monitoring Computers, Toilet Breaks – Even Emotions. Is Your Boss Watching You?,” The Guardian, May 14, https://www.theguardian.com/world /2018/may/14/is-your-boss-secretly-or-not-so-secretly-watching-you, retrieved May 24. Schwartz, Nelson D. (2017), “Economy Needs Workers, but Drug Tests Take a Toll,” New York Times, accessed 5/2/2018, https://www.nytimes.com/ 2017/07/24/ business/economy/drug-test-labor-hiring.html Shambaugh, Jay, Ryan Nunn, Patrick Liu, and Greg Nantz (2017), “Thirteen Facts about Wage Growth,” Brookings, September 25, accessed March 4, 2018, https://www.brookings. edu/research /thirteen- facts-about-wage-growth/ Shore, Lynn McFarlane and Ted H. Shore (1995), “Perceived Organizational Support and Organizational Justice,” In Russell S. Cropanzano and K. Kacmar (Eds.), Organizational Politics, Justice, and Support: Managing Social Climate at Work (149–164), Westport, CT: Quorum Press. Smith, William P. andFilizTabak(2009), “Monitoring Employee E-mails: Is There Any Room for P r i v a c y ? , ” A c a d e m y o f M a n a g e m e n t Perspectives, 23(4), 33–48. Smither, James W., Roger E. Millsap, Ronald W. Stoffey, Richard R. Reilly and Keenneth Pearlman (1996), “An Experimental Test of the Influence of Selection Procedures on Fairness Perceptions, Attitudes about the Organization, and Job Pursuit Intentions,”Journal of Business and Psychology, 10(3), 297-318. Sousa-Poza, Alfonso and Andres A. Sousa-Poza, A. (2000), “Well-being at Work: A Cross-National Analysis of the Levels and Determinants of Job Satisfaction,” Journal of Socio-Economics, 29(6), 517–538. Sprague, Robert (2012), “Facebook Meets the NLRB: Employee Online Communications and Unfair Labor Practices,” University of Pennsylvania Journal of Business Law, 14(4), 957-1011. Steinbrook, Robert (2006), “Imposing Personal Responsibility for Healthy,” The New England Journal of Medicine, 355 (8), 753-756.
The Harris Poll (2012), “One in Five U.S. Adults Now Has a Tattoo,” February 23, https://theharrispoll .com/new-york-n-y-february-23-2012-there-is-a-lot-of-culture-and-lore-associated-with-t a t t o o s - f r o m - a n c i e n t - a r t - t o - m o d e r n - expressionism-and-there-are-many-reasons-people-choose-to-get-or-not-get-p/ Thibaut, John W., and Harold H. Kelly(1959),TheSocial Psychology of Groups, New York: John Wiley& Sons, Inc. Tian, Amy Wei, John Cordery and Jos Gamble (2016), “Returning the Favor: Positive Employee Responses to Supervisor and Peer Support for Training Transfer, “International Journal of Training and Development,” 1-16. United States Department of Labor (2016), “Employee P o l y g r a p h P r o t e c t i o n A c t ( E P PA ) , ” https://www.dol.gov/ whd/polygraph/retrieved February 26, 2018. Van Gramberg, Bernadine, Julian Teicher, and Anne O'Rourke (2014), “Managing Electronic Communications: A New Challenge for Human Resource Managers,” The International Journal of Human Resource Management, 25(16), 2234-2252. Wagner, Tom (2007), “Winning the Workforce Competition; Winning the Competition for Recruiting, Hiring and Retaining Talent Delivers a Competitive Advantage to any Business, and This Contest Begins with Recruiting,” South Central Construction, 56 (2), February 1, 33. Warren, J. D., Jr, K. C. Moffitt, and P. Byrnes. 2015. How Big Data will change accounting. Accounting Horizons 29(2): 397–408. Warr, Peter B. (2007), Work, Happiness, and Unhappiness. Mahwah, NJ: Lawrence Erlbaum. White, Tiffany B. (2004), “Consumer Disclosure and Disclosure Avoidance: A Motivational Framework,” Journal of Consumer Psychology,
14 (1&2), 41-51. Zhang, Zhe and Ming Jia (2010), “Using Social Exchange Theory to Predict the Effects of High-Performance Human Resource Practices on Corporate Entrepreneurship Evidence from China,” Human Resource Management, 49(4), 743-765. Zeidner, Rita (2007), “How Deep Can You Probe,” HR Magazine, 52 10), 57-60. Zweig, David and Jane Webster (2003), “Personality as a Moderator of Monitoring Acceptance,” Computers in Human Behavior, 19(4), 479-493. Zweig, David and Kristyn Scott (2007), “When Unfairness Matters Most: Supervisory Violations of Electronic Monitoring Practices,” Human Resource Management Journal, 17(3), 227-247. Zweig, David and Jane Webster (2002), “Where is the Line between Benign and Invasive? An Examination of Psychological Barriers to the Acceptance of Awareness Monitoring Systems,” Journal of Organizational Behavior, 23(5), 605-633.
How likely would you be to accept the following position? The position is with a company you want to work for in a preferred location pursuing a career you enjoy. (Please circle the number.) * You will be subjected to a drug test (urine test) before being hired. * `Employer will have video surveillance in all areas (including offices) except bathrooms. * Employees must complete a mandatory wellness program (paid by employer), which includes 60 minutes of exercise weekly outside work (unpaid) and annual physicals. * Your salary is $12,000 above the industry average for that position
