Internet Privacy: Corporate Data Collection Versus Privacy Rights Of Individuals

“If you’re not paying for a product, then you are the product.” In the digital era where technology provides much easier access to information than before, questions can be answered with a simple Google search and click. On the other hand, free social media websites give users easier opportunities for communication and building connections with others all around the world. Despite the convenience, privacy comes at a cost of using free internet services. Behind the websites, corporations are using programs that track their users’ every move, from google searches to sensitive information such as credit card numbers which is advertised and sold to other companies. With that being said, we are the products that are being sold to several different companies. Often, we turn a blind eye when we interact with social media and do not realize the risks we put on ourselves when we input our personal information into the hands of these multi-million dollar corporations. While acquiring information on what a user is interested in or searches is not typically harmful, it is personal information that needs to be collected with caution and with the acknowledgment and consent from the individual. The monopoly that corporations have on their customers’ data needs to have certain restrictions for the sake of privacy, employee profiling, cybersecurity risks, and the dangers of being stalked and harassed.

Collecting data is an essential strategy for corporations to ensure customer satisfaction and revenue. Collecting starts the moment users sign up for an account and use their features. From there, companies track the content they like, follow, and share, and if they choose to purchase something from the website, they can save their credit card information and shipping address. Not only are social media websites are doing this, but devices like Google Home, the Apple iPhone, and FitBits also track data about their users through use facial and voice recognition, cloud backups, internet browsing history, and more. With this information, companies can determine the interests of their users and display the types of ads that cater to those interests, boosting customer satisfaction.

So what is the issue with this? The problem lies with what kind of data corporations are collecting from their customers which they are not aware of, and how they handle the information.. Policies such as the Fair Crediting Reporting Act of 1970 and the Privacy Act of 1974, which are, according to the U.S. Department of Justice, meant to protect the records of individuals and “prohibit unauthorized disclosures” of those records. However, despite these policies that are meant to mandate the use of data collecting, new computer technologies continue to be evasive and share sensitive information without the peoples’ acknowledgment or consent.

Whatever data is being inputted into the internet, multiple third-party software can collect that information about its users and put it out into the public. In an article written by Steven Melendez and Alex Pasternack, the authors claim that “By buying or licensing data or scraping public records, third-party data companies can assemble thousands of attributes each for billions of people” (2019).

People search websites and database marketing companies such as Whitepages, Spokeo, and Acxiom, publicly release records of individuals which reveal their full name, age, birthday, family members, and more. Moreso, there is “premium” content that includes more valuable documents such as a home address, phone number, and criminal records. Anyone can pay to have access to have the premium records, which can be potentially dangerous for the individual, as they are at risk of being doxxed, stalked, or harassed.

Jameson Lopp, a Bitcoin engineer, states that, “You don’t know who you might piss off, especially if you’re active on social media… It’s just not possible to fully comprehend the thought processes of everybody else out there who’s on the internet who might read or hear something you might say and then what they might do as a result,” (Torpey, 2019). Massive corporations putting their users’ data out to the public creates potential danger. A negative interaction on the internet could put someone at risk of being harassed or stalked. There is no way of telling what type of person from the other side of the screen is like. There is a probability that it could be a hacker who could spitefully compromise the other’s data and use it against them, over an interaction on social media. There have been many instances of stalking and harassing which resulted from the lack of internet privacy. Sarthak Grover and Roya Ensafi, Ph.D. students from Harvard University, also argue about the lack of security for private data. In their report, it writes that simple “user interactions” with today’s high-tech gadgets can “generate traffic signatures that reveal information” (Risen, 2016). However, the data encrypted in this technology is poorly secured and can easily be breached by hackers. Both Lopp, Grover, and Ensafi agree that corporations collecting their users’ private information and displaying it to the public without their discretion lure hackers to compromise the data and use it for malicious reasons.

Besides, there have been multiple data breaches that have occurred since 2010. The most notable of this decade was the Facebook data breach from 2018, where nearly 50 million users had their data acquired by hackers. Guy Rosen, who is the Vice President of Facebook’s Product Management, stated that “hackers tried to harvest people’s personal information, including name, sex, and hometown” (Isaac). Prior to the September breach, Mark Zuckerberg, the CEO of Facebook, was involved in a scandal in which the data of 87 million users were compromised by the Cambridge Analytica. The Cambridge Analytica defined by an article from The New York Times, is “a political data firm hired by President Trump’s 2016 election campaign” which examined the information of the 87 million people and spread misinformation about the 2016 election to influence voters and to further promote the Republican party (Granville). With the recent scandals that surround Facebook, there are a raise of concerns about cybersecurity and the overwhelming power that corporations have on consumer data and government elections.

Although Rosen claims that he was unable to locate “the extent of the hacker’s access to third-party accounts,” it has been found that the information of 87 million hacked accounts are being sold through a website called Dream Market, which can only be accessed through the dark web. According to an article by The Independent, “the value of the stolen data on the black market would be somewhere between $150m and $600m” (Cuthbertson). It can then be sold in bitcoin to criminals who use that information to their advantage by committing illegal activities from identity fraud to terrorist attacks. In general, the harvesting of data, especially data that is confidential, puts the company and its customers at risk. The lack of protection and cybersecurity could lead to a catastrophic snowball effect, ruining the lives of the individual and the company itself.

Data collecting is also a threat to people’s privacy rights and can result in the act of consumer and employee profiling. Employers scout the social media pages of potential employees to determine whether or not they would make a good fit for the company. However, according to a journal by the Proceedings of the National Academy of Sciences, they found that the interests and likes that a person displays on social media affects the probability of being hired. In addition, Michal Kosinski, a data scientist and computational psychologist found that people’s likes ‘can be used to automatically and accurately predict a range of highly sensitive personal attributes” (Tinker). Employers use this information to make certain assumptions about their potential employees, such as their personality traits, political standing, sexual orientation, habits, and so forth. For example, if employers were to see that the applicant is interested in anime or RPG games, then they can determine that the employer is shy or introverted. These conjectures, in turn, can either make or break their chances of employment.

The problem with predicting a customer’s capability of excelling in the workspace based on Facebook likes is that it is entirely based on shallow presumptions of the individual which does not accurately determine the person as a whole. Just because an employee partakes in casual video gaming does not always equate to laziness or introversion. This cheap, personality assessment is also built on stereotypes about groups of people who share common interests. Evaluating these people based on those interests can prevent them from future employment. Instead of stalking an applicant’s privacy through social media to evaluate their character, they should instead focus on their work ethics and skills that can be beneficial for their company. By focusing on that, this can limit the possibilities of employee profiling.

Despite the risk of private information being in the hands of hackers or employers costing your job over Facebook likes, detractors find that data collecting is beneficial for them. Manan Kakkar, a writer for ZDNet, expresses his argument against online privacy and how social media corporations, such as Facebook, can use their users’ data to cater to their interests. Kakkar states that, “Facebook collects user data to study user behavior then shares this data with advertisers who then show you with results that might be relevant and useful to you” (Kakkar, 2011). This, in turn, improves customer satisfaction and benefits both the users and Facebook. However, while Facebook or Instagram is providing users with advertisements that meet their interests, it comes with the cost of the consumer’s privacy. Users are being constantly being surveilled through the screen by software engineered by the companies and being recorded for every interaction on the internet. The American Civil Liberties Union, which advocates for privacy rights and consumer protection, stated, “innocent individuals have found themselves unable to board planes, barred from certain types of jobs, shut out of their bank accounts, and repeatedly questioned by authorities” (ACLU). Companies spread misinformation and target individuals based on vague information that they have collected from tracking them, even though they have legally done nothing wrong. In addition to this, an article by The Conversation says, “[corporate surveillance] have a limiting effect on personal freedom by eroding privacy and forcing people to self-censor – hiding details of their lives that, for example, potential employers may find and disapprove of” (2019). Surveillance restricts the freedom of individual in fears that they may be judged or disapproved of. Both the ACLU and Specht believe that the convenience of having free personalized content that suits the interests of the customer may be satisfying, but it comes at a cost of their privacy. Constant spying and tracking every movement of an individual can never be truly erased, and if it were to be seen by others, it could potentially sabotage their reputation.

In all, corporate data collection has been done for decades, and with new and advanced technology, it challenges the privacy rights of individuals. People tend to overlook the long-term impacts of entrusting their personal information to big corporations like Facebook who may lack the security measures of protecting such data. To prevent this, companies should limit the amount of data they collect on their consumers to evade database attacks that can lead to hackers mishandling private information. Moreover, limiting data harvesting can stop the information from being spread to the public and sold on the deep web for illicit uses. If sensitive data is required to proceed, then there needs to be a full acknowledgment of what specific data is being collected and how the company will use it, as well as an agreement from the consumer, who must have the right to completely withdraw if they are uncomfortable. If companies were more transparent about what data they are gathering, then this can limit the consequences. After all, consumers are not products, but they are human beings who are entitled the right of their privacy.

Exploring YouTube Data: A Data Driven Approach

Abstract

Video watching had emerged as one of the most frequent media activities on the Internet. Yet, little is known about how users watch online video. Using two distinct YouTube datasets, a set of random YouTube videos crawled from the Web and a set of videos watched by participants tracked by YouTube developer App, This paper examine whether and how indicators of collective preferences and reactions are associated with view duration of videos. This paper also shows that video view duration is positively associated with the video’s view count, the number of likes per view, and the negative sentiment in the comments. These metrics and reactions have a significant predictive power over the duration the video is watched by individuals. Our findings provide a more precise understandings of user engagement with video content in social media beyond view count.

Introduction

Video watching is perhaps the most popular web-based activity, through video hosting and sharing services such as YouTube, Facebook, Netflix, Vimeo, and others. As of 2015, YouTube alone has more than 1 billion viewers every day, watching hundreds of millions of hours of content. It is forecasted that video will represent 80 percent of all traffic by 2019. Yet, little is known about how users engage with and watch online video. We use two distinct datasets from YouTube to investigate how users’ engagement in watching a video (i.e., view duration) is associated with other video metrics such as the number of views, likes, comments, and the sentiment of comments. A number of research efforts have investigated view count as a key indicator of popularity or quality of video—particularly looking at its relationships with other popularity or preference metrics (e.g., the number of likes and comments). For example, the number of comments/favourites/ratings and average rating are significant predictors of video view counts on YouTube; the sequence of comments and its structure are strongly associated with view counts and view counts can be predicted through socially shared viewing behaviours around the content such as how many times a video was rewound or fast-forwarded as well as the duration of the session in a tool that allows people watch videos together in sync and real time. Although views, likes, comments, and other such measures can be considered as indicators of general popularity and preferences, there has been growing interest in using deeper post-click user engagement (e.g., how long a user watched a video) to estimate more accurate relevance and interest and to improve ranking and recommendation. For example, YouTube has started to use ‘dwell time’ (the length of time that a user spends on a video, e.g., video watching session length) instead of click events to better measure the engagement with video content. Beyond video, Facebook is using dwell time on external links to combat Clickbait—stories with arousing headlines that attract users to click and share more than usual, but are not consumed in depth.

Data Collection

For the Individual Logs dataset, our view duration dependent variable was computed differently. In this case, we have used an individual, but approximate, view duration measurement. In particular, we used the user’s dwell time for each video on the video’s page, as was measured by the extension, as an approximation for the actual view time for the video by that user. We modelled the data by automating queries and keyword-based searches to gather videos and their corresponding comments. Python scripts using the YouTube APIs were used to extract information about each video (comments and their timestamps). We collected 1000 comments per video (YouTube allows a maximum of 1000 comments per video to be accessed through the APIs), and used keywords like ‘Federer’, “Nadal’, “Obama’ etc., to collect the data for specific keywords. The timestamp and author name of each video were also collected. The final dataset used for the sentiment analysis had more than 3000 videos and more than 7 million comments. We performed data pre-processing on the collected comments. YouTube comments comprise of several languages depending on the demography of the commenter. However, to simplify the sentiment analysis, we modified the data collection scripts to collect only English comments. From the collected English comments, only comments in the standardUTF-8 encoding were selected in order to remove comments with unwanted characters. The steps below explain the procedure to collect the comments with their respective timestamps and author names for the keywords specified by the user. In steps 2-4, the Google APIs for YouTube are used to configure the query with the number of videos to be fetched, the language of interest for comments, the search keyword, and how the comments are to be sorted. Step 5 collects the IDs of the videos related to the specified keyword. Steps 6 and 7 collect the comments associated with these videos and extract the timestamps, author names and comment text from the comment entries. All the comments for a single keyword are aggregated into one dataset which is used as the test set as explained in the following:

  • Step 1: Prompt the user to specify the search keyword (keywords) and number of videos (numVideos)
  • Step 2: Set maxNumVideos = max(50; numVideos) (As Google limits the maximum number of videos fetched in one iteration to 50)
  • Step 3: Set up the YouTube client to use the YouTube-Service() API to communicate with the YouTube servers
  • Step 4: Use the YouTubeVideoQuery() API to set the query parameters like language, search keyword,etc
  • Step 5: Perform successive queries to get the videoID of each video related to the keyword
  • Step 6: Collect the comments associated with each videoID using the GetYouTubeVideoCommentFeed() API (maximum limit of comments per video is 1000)
  • Step 7: Extract the comments with their respective timestamps and author names

From 105 days of observation , it can be seen ,that a particular video trended for 14 days at most. We can see there are 604 videos those had appeared in the YouTube trending video list for only once. One of the interesting point I would like to share, correlation between likes &comment count = 0.71 . And correlation between dislikes & comment_count = 0.83 . So we can claim, that more people involved in conversation when they were disliking a video rather than liking a video. Most of these cases ,video might be controversial or a fake news,etc.

From the above plot we can see, there is a very strong relationship between views & likes. And the value of the correlation between them is 0.82. Since log10 applied on the x-axis & and there are few videos in YouTube trending list with 0 likes, thats why we have to pass the variable (likes+1) instead of likes into the scale_x_log10() function. That would help to overcome infinite values(since log10(0) = Inf). Therefore in the above plot on x-axis scale 1 represent 0. We can see there are many outliers on y-axis for x = 1. Many of those video authors might be disabled video rating ,so users can’t like or dislike the video. Another point to see, after 10^4=10000 likes ,variance of likes decreases as views increases.

References

  1. Alhabash, S.; Baek, J.-h.; Cunningham, C.; and Hagerstrom, A.2015. To Comment or Not to Comment?: How Virality, Arousal Level, and Commenting Behavior on YouTube Videos Affect Civic Behavioral Intentions. Computers in Human Behavior.
  2. Arapakis, I.; Lalmas, M.; Cambazoglu, B. B.; Marcos, M.-C.; and Jose, J. M. 2014. User Engagement in Online News: Under the Scope of Sentiment, Interest, Affect, and Gaze. Journal of the Association for Information Science and Technology.
  3. Baym, N. K. 2013. Data Not Seen: The Uses and Shortcomings of Social Media Metrics. First Monday.
  4. Berger, J., and Milkman, K. L. 2012. What Makes Online Content Viral? Journal of Marketing Research.
  5. Chatzopoulou, G.; Sheng, C.; and Faloutsos, M. 2010. A First Step Towards Understanding Popularity in YouTube. In Proc. Of INFOCOM.
  6. Cisco. 2015. Global IP Traffic Forecast. http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-indexvni/ index.html.
  7. Cramer, H. 2015. Effects of Ad Quality & Content-Relevance on Perceived Content Quality. In Proc. of CHI.
  8. De Choudhury, M.; Sundaram, H.; John, A.; and Seligmann, D. D. 2009. What Makes Conversations Interesting?: Themes, Participants and Consequences of Conversations in Online Social Media. In Proc. of WWW.
  9. El-Arini, K., and Tang, J. 2014. News Feed FYI: Clickbaiting. http://newsroom.fb.com/news/2014/08/news-feed-fyiclick-baiting.
  10. Hutto, C., and Gilbert, E. 2014. Vader: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. In Proc. Of ICWSM.

Data Privacy Protection From Government, Business And Individual’s Perspective

Data privacy is defined by Techopedia as information that contains private and usually personal data about an individual. (Techopedia) Being able to properly and secure this data is an important factor that the government, business and individuals that must be considered and be taken seriously. This is because the data stored contains personal information that is highly confidential and with today’s technological advancements this data have become more and more vulnerable to threats of unauthorised access by hackers and other activities that would otherwise prove illegal. As such rules and regulations have been introduced so that we are all protected when providing personal details in every transaction that requires us to give out personal information. Personal data and other records (whether medical or financial) are usually collected by many organisations in order to improve their service, advertise or to help identify individuals as quickly as possible. They are an important part of our today’s society that is required by the ongoing technological advancements that is happening around the world. Data privacy management requires each and every one of us to play an important role and responsibility towards maintaining our privacy information in check whether it’s from government, business and or individual’s perspective. Not being able to do so, may result in serious consequences or jeopardise an entity.

Firstly, the government has a critical role to make sure that only those with authorised access can view an individual’s personal data. According to (Bennett 2009) the concept of [data privacy] is fused with data protection, which interprets privacy in terms of management of personal information”(Bennett 2009). The Privacy Act 1988, regulates how organisations and individuals handles personal information that helps identify a person. This could be through a phone number, signature or date of birth, all of which are personal details. Under this law outlines the Australian Privacy Principles (APPs) that covers all relevant information that must be followed. (Australian Government, Office of the Australian Information Commissioner, OAIC). The Privacy act 1988, acts and enforces the guidelines to which an extent of whether an individual can maintain “anonymous or using a pseudonym where necessary” (OAIC). The government sets out the data security standards that many businesses and individuals has to follow so that they are protected and in control of the usage of their information. They also educate the community regarding privacy issues, undertake investigations and also develop legislations that will ensure the security of data. The government is the governing body that allows all this to be successfully enact throughout Australia. They acquire data for the sole purpose of identifying an individual so that they can provide the proper service when necessary.

Secondly, businesses must make sure that they are complying with privacy laws and etiquettes regarding the handling of data. According to General Data Protection Regulation (E. Marshall, 2018) “the ready availability of cheap data storage has created a situation where companies can store … [and stockpile] data” which has become a strategy for a range of companies. (E. Marshall, 2018). This data can be a valuable asset for many businesses as data collected can be used for the purpose of advertising, providing better service and identify its market diversity so that they can become more competitive than its competitors. In Accordance to GDPR guidelines (E. Marshall, 2018), businesses must ensure that their use of customer’s data must be ‘lawful’ and indicate the sole purpose of data gathering to the individual. (E. Marshall, 2018) They must also make sure that the data is secured properly and only those with authorisation can access the data. Businesses must commit towards developing a “clear and updated privacy policy”. (Australian Government, 2018) Customers trust that their personal data and financial data is kept safe by them when they are provided with goods or services. Any breaches or leaks of data from businesses can be costly and may even result in legal actions, loss of trust from customers but most importantly can damage their overall reputation. That is why, it is also their responsibility to have appropriate breach protocols and action plan should a situation like this may arise.

Thirdly, while we can’t control the outcomes from third party safety protocols on data privacy, individuals may stride to develop their own safety system so that they can protect themselves by setting up passwords and being careful online whenever you’re on social media or purchasing goods or services on online platforms. Developing a password that isn’t too ‘predictable’ is one way that you, yourself as an individual can do. By using longer passwords and random letters and number combinations can make it hard for anyone to access your information. Being on social media can also make an individual vulnerable to random pop up ads that requires you to sign up to their services or purchasing goods, that is why it’s really important that research on the companies should be done to make sure that they are legitimate and can keep your information safe. (N. Lord, 2019). It is also the individual’s responsibility that they constantly update their protection system which can be simply as changing passwords every 3-6 months. Being careful on what you do online can really make a difference in achieving and developing a safer system that will keep your personal information safe. (N. Lord, 2019)

In conclusion, in order to achieve a safer protection of privacy data, the roles and responsibility of the government, businesses and individuals must be shared amongst them and each plays an important role so that our personal information are being protected and trusted. Data collection is a way for many businesses and government body to help improve services, to identify market diversity but most importantly help identify individuals through the use of personal information. Keep in mind that failing to follow the privacy policy and laws can result in serious consequences. Our society is constantly changing and technologies are evolving rapidly which makes us vulnerable to attacks and unauthorised access from hackers, that is why we need to constantly adopt towards developing better systems to protect ourselves online. Thus making our future uncertain and full of doubts on the issue surrounding data privacy and protection.

References

  1. Bennett, L. (2009). ‘Reflections on privacy, identity and consent in on-line services.’ Information Security Technical Report 14(3): 119-123.
  2. Australian Government, Office of the Australian Information Commissioner, OAIC. The Privacy Act 1988 (Australian Privacy Principles), https://www.oaic.gov.au/privacy-law/privacy-act/australian-privacy-principles
  3. Marshall, GDPR, 2018. General Data Protection Regulation- GDPR: data security is the responsibility of companies by Emmanuel Marshall on 03 July 2018 09:50:06 AEST https://www.mailguard.com.au/blog/gdpr-security-responsibility
  4. Australian Government, 2018 Protecting your customers’ information -Last Updated: 15 August 2018 https://www.business.gov.au/risk-management/cyber-security/protecting-your-customers-information
  5. N. Lord, 2019. 101 Data Protection Tips: How to Keep Your Passwords, Financial & Personal Information Safe in 2019 by Nate Lord on Wednesday May 15, 2019https://digitalguardian.com/blog/101-data-protection-tips-how-keep-your-passwords-financial-personal-information-safe
  6. Techopedia. Information privacy- – What does Information Privacy mean? https://www.techopedia.com/definition/10380/information-privacy

Overview of Data Collection Methods for Obtaining Employee and Organizational Health Information

In this paper, I will examine the data collection methods used at work and how they are used. Then choose a problem in our business to discuss and identify a method to collect this data. Lastly, identify two critical variables whose relationship is critical for our company’s success. Also, identify a moderator that changes the dynamics of the two critical variables. I see right away that my company uses industrial and organizational psychology concepts in their management tool bag. The employees at Altria should know data is constantly being collected and they are the primary date source, but the use of the data to address opportunities has been mixed.

I work for Philip Morris USA (PMUSA), but the parent company is Altria owns many companies. The Altria Human Resources Department initiates and approves all data collections company wide. PMUSA also, has a communication group that works hand in hand with the parent company to execute and coordinated data collections. This is a luxury that PMUSA has over other operating companies.

My favorite company data collection tool is the Glint Surveys. We expect to get at least one survey a year. We completed the Glint Survey late 2018 then the company made some significate changes to the organization. So, the 2019 Glint Survey was intended to gauge how employees are doing after the recent organizational changes. The survey, in my opinion, was flawed. Management changes the questions for salaried operating companies, salaried services companies, salaried headquarters and hourly employees. The results were not representative of what I expect from talking to piers. The questions were around diversity compliance, corporate direction, process compliance, and recognition. The survey summary always compares our answer with answers from other companies. This comparison gives senior leaders further insight on the direction we are headed good or bad.

Although observational data collection is not as formally announced, the company actively uses observation. Safety uses this tool to monitor safe behaviors and logs the data as safe or unsafe. Of course, if bad behaviors are identified we all get to re-do safety training. This usually is prompted by an incident. The factory equipment is operated and repaired by hourly employees and they are constantly observed for efficiency improvements through improving processes. Maybe a little short of scientific, but the company does collect data through observation.

Like surveys, we collect data through ‘how are you feeling’ kiosks. The communications group can pose one question and push them to all the kiosk locations. The employee can hit the ‘happy’ button or ‘sad’ button or do nothing at all. After the organizational changes the message was, ‘are you happy today’. The questions are always simple and not much critical thinking involved. The employees collecting data measure the amount of responses and the answers. The communication group stays clear of political questions and other hot topics. The data is used to gain employees mindset whether it abruptly changes, responses pick up, or slow way down.

PMUSA is a converter. We don’t market, sell, distribute, or any other corporate function. We have two large objectives to manage. The fist is keeping conversion costs low or within budget. Production is the other key objective, where we must hit our target. If production is up then cost will drop, but with tightening margins cost are watched closely. The problem is we are not great at controlling costs. The company is regulated by the FDA and external pressures are constantly changing our manufacturing plan. The factory is trying to hit the original operating budgeted production number and internal news of curtailment days coming because of over production. Operators and maintenance are of the opinion that because we don’t need the production, they will slow down the machines. The company has over capacity capability, so they turned on more machines. This whole situation translates to higher costs of running more machines that budgeted.

In this experiment we should use both qualitative and quantitative data. Gathering information from machine (system) generated data to isolate specific operators or machines, observation, and interviews. I did not intend to have three collection points, but we use all of them in operations. We have regular planned meetings with operators, so conversations and information sharing are a normal part of the business. There are supervisors, process managers, area managers all watching individual performance, machine performance and organizational performance.

The independent variables for this analysis would be production target, information sharing, external pressures, and tenure with company. Production targets can get manipulated during the year but gets compounded by information sharing. Information sharing could be just a rumor or valid information taken out of context. Same I true for external pressures like new or changing regulations. Tenured and non-tenured employees may react differently. Like in the Hawthorne Study where the group set their own target, we may be seeing the same phenomenon.

The dependent variables for this analysis would be cost reduction, machine efficiency, and reduction in production capacity. Machine efficiency in relation to operators setting their machines to target instead of making their own target. Reduction in capacity is a stretch, but if we can reduce the dependence on more machines to do the same work, we could save money and space. We are a converter so costs should be our focal point.

The two most important variables are production target and cost. The moderator that changes this relationship is target incentives for meeting goal. Incentives have been custom designed shirts that say, ‘I meet goal’ or maybe a little catchier. This incentive was meant for employees who like this type of recognition. The employees that are just trying to stay under the radar have something else as motivation. The production data collected per module (machine), per bay (eight bays), and per shift (three shifts) will be displayed locally and on the company network. I personally will work for the shirt, but no one wants to negatively stick out in a crowd.

In summary, I talked about the data collection that Altria and PMUSA uses to gain knowledge of employee and organizational health. Surveys are the biggest tool, but observation is more frequently used. Operators creating their own production target numbers which increased cost of conversion. They could have changed the target numbers as a form of job security, tipped off that production was not needed, or some other reason. Cost of conversion and production target are our critical variables with incentives as the moderators. In our case, there is a positive and negative incentive. Industrial and organizational psychology concepts are in play at my company and I look forward to finding these hidden relationships.