Impact Thread

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Mentorship

A study presented by the U.S. Bureau of Labor predicts that millennials are increasingly making up more of the workforce. In the same vein, another  study predicts that millennials will compose 50% of the global workforce by 2020. As the composition of the workforce changes, mentorship programs are gaining in value because these programs are important to millennials. In fact, a recent study by Deloitte states that millennials are more likely to stay with an organization if they have a mentor. 98% of millennials believe that having a mentor relationship is important to career success according to the 14th Annual Global CEO Survey by PricewaterhouseCoopers. Perhaps because of the desire to retain millennials, or perhaps because of the many other advantages, companies are trending towards having mentorship programs. The majority of Fortune 500 companies, as many as 71%, have mentorship programs (American Society for Training & Development).  Among the Fortune 500 companies with mentorship programs are companies like General Electric, Intel, and Google to name a few.

Recruiting millennials is only one of many reasons for why companies should incorporate mentorship. Mentorship is beneficial for the organization and individuals alike. Mentorship can be used for instruction (developing technical skills and knowledge), building leaders (mentors teach leadership skills to their mentee), and planning for succession (prime mentees to step into advanced positions). Reverse mentoring is also worthwhile as it allows younger generations to pair with older ones so that the older generation can be instructed in newer technology and skills. Overall, mentorship relationships improve the socialization experience of the mentee and increases the attachment and engagement of employees.

Unfortunately, despite the clear worth of mentorship programs, real world applications of these programs do not always incorporate the best practices suggested by research. We recommend trying to incorporate these empirical findings to maximize returns from these programs.

Research findings worth consideration:

·      Both the mentor and the mentee benefit more from the mentorship relationship when they have input in how they are matched (Allen, Eby, & Lentz, 2006).

·      More is learned when the participants of the mentorship relationship are more similar (Allen & Eby, 2003).

·      Informal mentorship is the best form of mentorship, but more formalized programs are better than no mentorship at all (Chao, Walz & Gardner, 1992).

·      Different types of mentorship can be harnessed different to yield different outcomes.

·      Psychosocial mentoring (including behaviors like counseling, role modeling, and providing acceptance) improved self-esteem and job satisfaction while career mentoring (i.e., coaching, protection, and exposure) was also related to improved task performance and objective measures of career success such as salary and promotions (Allen, Eby, Poteet, & Lentz, 2004).

·      Mentors benefited from personal gratification and greater leadership skills (Eby & Lockwood, 2005).

·      Mentorship benefits exist past the initial intervention (Chao, 1997). Specifically, mentorship programs are believed to reduce turnover – not just immediately but even up to 10 years later (Payne & Huffman, 2005)

·      Mentor training is a key aspect to keeping mentors involved in the program and providing psychosocial support (Allen, Eby, & Lentz, 2006). 

 

Perhaps because of their missions, social impact organization can uniquely benefit from mentorship programs. For instance, millennials are often especially drawn to these organizations and are more likely to stay with organizations when they have mentors. Moreover, many of these organizations often have a more flexible and amorphous structure where mentorship programs may be especially helpful. The psychosocial forms of mentorships are also beneficial for cultivating the attitude of service that exist in social impact organizations.

            The good news is that mentorship programs are not difficult to implement. Mentors and mentees need only be voluntarily solicited, matched based on their respective needs and desired outcomes, and monitored to make sure they are cultivating the types of relationships they desire. Programs can be formalized such that they have requirements about types and frequency of interaction, or they can be allowed to develop naturally. Either way, it is wise to check in with your participants about how the program is operating to measure success. 

For more information:

Heffernan, M. (2015, June). Forget the pecking order at work [Video file]. Retrieved from http://www.ted.com/talks/margaret_heffernan_why_it_s_time_to_forget_the_pecking_order_at_work

Sinek, S. (2014, March). Why good leaders make you feel safe [Video file]. Retrieved from http://www.ted.com/talks/simon_sinek_why_good_leaders_make_you_feel_safe

Allen, T., Eby, L., & Lentz, E (2006).  Mentorship behaviors and mentorship quality associated with formal mentoring programs: Closing the gap between research and practice. Journal of Applied Psychology, 91(3), 567-578. 

Broder-Singer, R. (2011).  Why mentoring matters. Retrieved from http://bus.miami.edu/businessmiami/fall2011/features/mentoring_matters.html

Schooley, C. (2010).  Drive employee talent development through business mentoring programs.  Retrieved from http://www.bu.edu/questrom/files/2013/07/Forrester-Research-Report-Drive-Employee-Talent-Development-Through-Business-Mentoring-Programs.pdf

 

Engagement and Job Demands

Work engagement is the topic de jour. Kahn (1990) originally pioneered the concept, proposing that engaged employees are physically, cognitively, and emotionally involved in their work roles. To date we know that engagement is shown to have strong statistical relationships to meaningful organizational outcomes: performance (e.g., Saks, 2006, Crawford, LePine, & Rich, 2010), profitability (e.g., Harter et al., 2002; Harter, Schmidt, Killham, & Agrawal, 2009), psychological safety (e.g., Harter et al., 2009; Nahrgang, Morgeson, & Hofmann, 2011; Wachter & Yorio, 2014; Zohar, 2000), customer satisfaction (e.g., Coffman & Gonzalez-Molina, 2002), and lower turnover and intention to leave (e.g., Bakker, Demerouti, & Euwema, 2005; Brunetto et al., 2014; Harter et al., 2002; Saks, 2006; Schaufeli & Bakker, 2004). In addition, employee engagement at the business unit level has been connected to customer satisfaction, productivity, profit, employee turnover, and accident rates (e.g., Harter et al., 2002; Schneider, Macey, Barbera & Martin, 2009; Xanthopoulou, Bakker, Heuven, Demerouti, & Schaufeli, 2008).

If you’re thinking, “Wow!” we definitely agree with you. Since Kahn brought engagement to our attention, researchers from all corners of the world have suggested varying definitions and strategies for measurement (e.g., Hakanan & Roodt, 2010), but the most popular and well-researched model to date is the Job Demands-Resources model (JD-R; Bakker & Demerouti, 2007, 2008). The idea behind the JD-R is that job demands balanced by job and personal resources drive engagement. For Reference, Job demands are: the workload, emotional and cognitive demands of a job.  Resources that can moderate these demands of daily work include job resources (feedback, social support, and developmental opportunities) and personal resources (self-esteem, self-efficacy, resilience, and optimism).

The combination of the two types of resources lead to engagement whereas few resources and high work demands lead to burnout. If you work at a nonprofit or social impact org, you know the risk for burnout is high (Schaufeli, Leiter, & Maslach, 2009). What if we focused on crafting our workplaces to facilitate high engagement while simultaneously avoiding burnout? Engagement is important in every sector of every industry, but we see the heart of NGOs and social impact orgs, and we want the missions that drive you to be complemented by the workplaces that pay you.

 The following interventions have been empirically tested and compared by researchers Knight, Patterson, and Dawson (2016). According to their meta-analytic study,

·      Building personal and job resources, having leadership training, as well as promoting health show promise as the most successful intervention types for building engagement.  Building personal resources typically involves finding ways to increase an individual’s self-efficacy (the belief that someone has the ability to meet a given challenge). 

·      Building job resources may look like increasing the autonomy an employee has at work.  While building personal and job resources is important, having training for the leadership of an organization allows for a “trickle down” effect to happen. By increasing the knowledge of leadership, workers will perceive an effect of increased job resources. Though a word of caution is given to make sure the training is specific to the organization’s needs and not a general training. 

·      Lastly, by promoting healthy behaviors at work, employees can benefit from the physiological effects of exercise such as reduced stress and positive mental health states. Starting by building in elements of mindfulness into the work place are a simple, low investment way to reap these benefits. 

There are many different ways to work towards building increased employee engagement at work.  Choosing one or two strategies from the list above to discuss with your employees is a great first step. 

 

References

Bakker, A. B., & Demerouti, E. (2007).  The job demands-resources model: State of the art. Journal of Managerial Psychology, 22(3), 309-328.

Bakker, A. B., & Demerouti, E. (2008).  Towards a model of work engagement. Career Development International, 13(3), 209-223.

Bakker, A. B., Demerouti, E., & Euwema, M. C. (2005).  Job resources buffer the impact of job demands on burnout.  Journal of Occupational Health Psychology, 10(2), 170.

Brunetto, Y., Shacklock, K., Teo, S., & Farr-Wharton, R. (2014). The impact of management on the engagement and well-being of high emotional labour employees.  The International Journal of Human Resource Management, 25(17), 2345-2363.

Coffman, C., & Gonzalez-Molina, G. (2002). A new model: Great organizations win business by engaging the complex emotions of employees and customers.  The Gallup Management Journal, 12-21.

Crawford, E. R., LePine, J. A., & Rich, B. L (2010).  Linking job demands and resources to employee engagement and burnout: A theoretical extension and meta-analytic test.  American Psychological Association, 95(5), 834-848.

Hakanan, J. J., & Roodt, G. (2010).  Using the job demands-resources model to predict engagement: Analysing a conceptual model.  In A. B. Bakker, & M. P. Leiter (Eds.). Work engagement: A handbook of essential theory and practice (pp. 85-101). Hove, East Sussex, UK: Psychology Press.

Harter, J. K., Schmidt, F. L., & Hayes, T. L. (2002).  Business-unit –level relationship between employee satisfaction, employee engagement, and business outcomes: A meta-analysis. Journal of Applied Psychology, 87(2), 268-279.

Harter, J. K., Schmidt, F. L., Killham, E. A., & Agrawal, S. (2009).  Meta-analysis: The relationship between engagement at work and organization outcomes. II Gallup Inc., Princeton.

Kahn, W. A. (1990).  Psychological conditions of personal engagement and disengagement at work.  The Academy of Management Journal, 33(4), 692-724.

Knight, C., Patterson, M., & Dawson, J. (2016).  Building work engagement: A systemic review and meta-analysis investigating the effectiveness of work engagement interventions. Journal of Organizational Behavior.

Nahrgang, J. D., Morgeson, F. P., & Hofmann, D. A. (2011).  Safety at work: A meta-analytic investigation of the link between job demands, job resources, burnout, engagement, and safety outcomes.  Journal of Applied Psychology, 96(1), 71-94.

Saks, A. M. (2006).  Antecedents and consequences of employee engagement.  Journal of Managerial Psychology, 21(7), 600-619.

Schaufeli, W. B., & Baker, A. B. (2004).  Job demands, job resources, and their relationship with burnout and engagement.  A multi-sample study.  Journal of Organizational Behavior, 25(3), 293-315.

Schaufeli, W. B., Leiter, M. P., & Maslach, C. (2009).  Burnout: 35 years of research and practice.  Career Development International, 14(3), 204-220.

Schneider, B., Macey, W. H., Barbera, K. M., & Martin, N. (2009). Driving customer satisfaction and financial success through employee engagement.  People and Strategy, 32(2), 22.

Wachter, J. K., & Yorio, P. L. (2014).  A system of safety management practices and worker engagement for reducing and preventing accidents: An empirical and theoretical investigation.  Accident Analysis & Prevention, 68, 117-130.

Xanthopoulou, D., Baker, A. B., Heuven, E., Demerouti, E., & Schaufeli, W. B. (2008). Working in the sky: a diary study on work engagement among flight attendants. Journal of Occupational and Health Psychology, 13(4), 345.

Zohar, D. (2000). A group-level model of safety climate: Testing the effect of group climate on microaccidents in manufacturing jobs. Journal of Applied Psychology, 85(4), 587-506. 

 

Polling Data: The good, the bad, and the ugly

Polling Data: The good, the bad, and the ugly

We are data nerds. Much of what we do is based on an understanding of how statistics are run and applied. Naturally, we spent time ruminating about how the presidential election polls could be so wrong. We are offering some thoughts here:

Since the inception of elections people have been trying to predict the winner. However, polling data and data accuracy can be one of ambiguity and heavily debated. As Nate Silver recently stated on The Daily Show, “Polling is like democracy: it’s the least worst system ever invented.”

If you are interested in the nitty gritty of how polling data is collected, here is a comprehensive reference.

The quick and dirty version is that polling data is often subjected to a fair amount of methodological issues, they are based on constantly changing public opinion, often miss large amounts of the population, and the analytics used are based on past information and patterns that are not always adequate in predicting future trends.

First, questions are often worded differently for different polls and not worded optimally for “accurate” results.  

Consider the following example (borrowed from electoral-vote.com):

      If the Nevada Senate election were held today, would you vote for the Democrat or the Republican?

      If the Nevada Senate election were held today, would you vote for the Republican or the Democrat?

      If the Nevada Senate election were held today, would you vote for Catherine Cortez Masto or Joe Heck?

      If the Nevada Senate election were held today, would you vote for Joe Heck or Catherine Cortez Masto?

      If the Nevada Senate election were held today, would you vote for Democrat Catherine Cortez Masto or Republican Joe Heck?

      If the Nevada Senate election were held today, would you vote for Republican Joe Heck or Democrat Catherine Cortez Masto?

      If the Nevada Senate election were held today, for whom would you vote?

First, there is inherently a lot of error (random and systematic) associated with surveying and polling. An example of systematic error in the case of the questions above could be that the questions were asked in a different order leading to different estimations of candidate support (Hillygus, 2011). Another example is in the format of the questions. Some of these questions have specific options listed, some provide time frames, and some are open ended. People will respond to these questions very differently depending on a host of variables (not excluding whether or not they had gotten around to a cup of coffee that morning.) Often times, the data collected from these different questions is aggregated to provide a much simpler picture than the questions above provide on their own. What you have probably guessed by now is that aggregating the data based on different questions does not always play well statistically.

Second, the “Bradley effect,” or a manifestation of wanting to behave in a socially desirable way, led those being polled to answer in ways that weren’t a true reflection of their voting intentions. In other words, when a pollster asks an individual if they are voting for the black candidate and they say “undecided,” they may not be undecided at all. They may have no real intention of voting for that candidate but do not want admit it (Hillygus, 2011).  Said another way, participants said what they thought the pollster might want to hear and ushered in an unprecedented amount of error into aggregated polling data (Trunde, 2016). 

Third, many of the polls also did not ask questions about the third party candidates. This is a vital piece of the puzzle that was missed. Last, thinking about who is likely to answer random phone calls, or stop and take a poll on the street? Probably not much of that population that came out in droves to vote on Nov 8th (Kurtzleben, 2016). 

So what can we do better? First, we should consider a more widely agreed upon question format. Second, we should institute a stronger emphasis on word choice and a more representative method of collecting data rather than relying on what is most accessible. Third, we should learn as much about a poll’s construction as possible before consuming the information they provide. In the meantime, we advise taking this data with a hefty grain of salt.

References

·       Hillygus, D. S. (2011).  The evolution of election polling in the United States.  Public Opinion Quarterly, 75, 962-981. 

·       Kurtzleben, D. (2016).  4 Possible reasons the polls got it so wrong this year.  NPR.  Retrieved from: http://www.npr.org/2016/11/14/502014643/4-possible-reasons-the-polls-got-it-so-wrong-this-year

·       Trunde, S. (2016). It wasn’t the polls that missed, it was the pundits.  RealClear Politics.  Retrieved from: http://www.realclearpolitics.com/articles/2016/11/12/it_wasnt_the_polls_that_missed_it_was_the_pundits_132333.html

 

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