The Executive Leadership Council Journal by elcinfo

FALL 2023

The Executive Leadership Council Journal A Research Journal for Black Professionals

The Executive Leadership Council Journal

A Research Journal for Black Professionals Fall 2023

Contents Letter from the Editor….....................................................................7 Artificial Intelligence: Considerations For Black Executives….............9 by Robert P. Singh, Ph.D. Did firms change their tax avoidance practices after the Tax Cuts and Jobs Act of 2017? ….........................................................21 by Yan Jin, Ph.D., CPA and Tao Zeng, Ph.D. Erasing Loneliness Among Black Executives….................................41 by Samuel Akin, Ph.D. The ROI of Rapport: 4 Steps to Build Your Confidence and Career Trajectory without Telling Everyone All of Your Personal Business….49 by Shamis Pitts, MBA, CPC, PCC, SHRM-SCP About the contributors…..................................................................58

Copyright © 2023 by The Executive Leadership Council, Inc. All rights reserved. No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the publisher, except in the case of brief quotations embodied in critical reviews and certain other noncommercial uses permitted by copyright law. ISBN 979-8-218-32706-4

Letter from the Editor As we close another year, it is a time of reflection and looking forward. In this edition, we delve into topics that are not only pivotal in the current professional landscape but also hold significant implications for the future. In the article “Artificial Intelligence: Considerations For Black Executives,” we navigate the complex world of AI. As this technology continues to reshape industries, understanding its implications is crucial. This article zeros in on the specific considerations and decisions Black executives face as their companies roll out AI initiatives. This issue also explores the effects of legislative changes on corporate behavior in “Did Firms Change Their Tax Avoidance Practices after the Tax Cuts and Jobs Act (TCJA) of 2017?” It offers an insightful analysis of how the TCJA influenced corporate tax avoidance, providing valuable insights for those navigating the shifting sands of tax legislation and corporate governance. Loneliness is an emotion often unspoken about in executive roles. However, its presence, especially among Black executives, can be palpable. The article, “Erasing Loneliness Among Black Executives Through Self-Leadership and Super-Leadership”, takes a fresh approach to thisdilemma. While systematic changes in hiring practices are crucial, this piece takes a fresh approach to this dilemma by promoting individual-driven solutions as potential remedies for this isolating experience. Finally, “The ROI of Rapport” brings to the fore the intricate dance between authenticity and professionalism. As we navigate our way in the professional world, striking the right balance between opening up and maintaining privacy becomes vital. This piece provides a roadmap to building authentic interpersonal relationships at work while ensuring career growth. We invite you to dive deep into these pieces, reflect on their implications, and employ the insights in your professional journey.

Amanda Rey Director, Institute for Leadership Development & Research The Executive Leadership Council

Artificial Intelligence: Considerations For Black Executives by Robert P. Singh, Ph.D. In today’s Corporate Social Responsibility (CSR) era, post-George Floyd, many companies have increased their pledges to provide resources and opportunities to the communities that they serve. Sunshine Anderson’s song, “HEARD IT ALL BEFORE,” comes to mind. In its truest nature, CSR is an organization’s external focus on social justice issues that impact the ecosystem in which they operate. Robert P. Singh, Ph.D. is an Associate Professor and the Dr. Abraham Pishevar Endowed Chair of Finance and Entrepreneurship in the School of Business at Howard University. Dr. Singh’s research primarily focuses on issues facing Black and minority entrepreneurs, as well as macro-economic issues in the U.S. economy.

FOLLOWING A BRIEF DISCUSSION of AI’s growing influence on society, this article focuses attention on issues that Black executives should consider as they adopt and lead on AI initiatives. AI offers the potential for great improvements to productivity, efficiency, and profitability, but there are also significant risks to Black and minority stakeholders and communities. The benefits and risks are discussed and three recommendations for Black executives are offered which can mitigate risks to reduce the dangers posed by AI.

INTRODUCTION Depending on one’s perspective, the most exciting or alarming development over the last several years is the spread and use of artificial intelligence (AI) across society. While there are many definitions of AI, in a broad sense it represents the creation of machines which can simulate intelligent human behavior with minimal human intervention. 1 This is done through algorithms and programming which find ways to allow machines to emulate features of human intelligence such as learning, comprehension, and the ability to solve problems. 2, 3 The process of machine learning takes place as massive amounts of training data are used to hone the accuracy of the algorithms by giving AI systems the ability to detect patterns that are used for decisionmaking. 4 There is a wide range of opinions on AI. Some optimistic authors

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extol AI’s potential to improve efficiency and help humans do their jobs more easily 5, 6 which can increase per capita wealth. 7 Others are more sanguine and pessimistically warn of widespread disruptions to labor markets with massive unemployment resulting from AI powered robotics and automation. 8, 9, 10 It is worth noting that many of the world’s leading AI experts, such as OpenAI CEO Sam Altman and Google DeepMind CEO Demis Hassibis along with dozens of other notable individuals, are on record as warning about the potential existential threat posed by AI 11 as signatories of an online statement on the risks of AI posted by the Center for AI Safety (see https://www.safe.ai/statement-on-ai-risk#signatories). For those who are interested in a “balanced” discussion, Tegmark 12 provides an excellent discussion of potential pros, cons, and societal outcomes and how AI should be developed to maximize the potential positives. Debate about the wisdom of adopting various AI algorithms will continue and undoubtedly take place even as the economic efficiencies which create increased productivity and profitability are being implemented. For existing firms and new venture startups alike, the increased efficiencies and cost savings potential of AI offer the promise of ever-increasing profits. This will make it difficult to stop the rapid development of these tools. For all its potential benefits, it is important to recognize that AI is not free from bias, 13, 14, 15 nor does it ensure that decisions are morally sound. 16 It is tempting to believe that because AI decisions are made by automated machines and algorithms, they must be based on purely objective decisionmaking processes, but there is much evidence to the contrary. 17, 18, 19, 20 In fact, AI is likely to simply reflect the values and mirror the decisionmaking processes already in existence, even if those processes are systemically unfair and biased. 21, 22, 23, 24 The risks and implications of these biases are extremely serious, as they may further solidify systemically and structurally discriminatory processes which disproportionately affect Black and minority people, as well as females and those who are lower on the socio-economic ladder. This article’s purpose is to broadly discuss issues that Black executives must be aware of and be prepared to lead on with respect to AI development and adoption. Following a brief discussion of AI’s impacts on society and how it is and will continue to change business ecosystems, the article focuses attention on the risks that must be mitigated in order to reduce the potential for systemic bias to infect machine learning algorithms which drive AI development. Three specific recommendations are offered which can help to reduce the risks and maximize the utility and fairness of AI systems.

ARTIFICIAL INTELLIGENCE: CONSIDERATIONS FOR BLACK EXECUTIVES

AI’s IMPACTS ON SOCIETY AND THE WORKPLACE In his 2015 TED talk, noted Oxford professor and AI expert Nick Bostrom stated, “Machine intelligence is the last invention that humanity will ever need to make. Machines will then be better at inventing than we are, and they’ll be doing so on digital timescales.” 25 Machines have not achieved super-human intelligence or achieved consciousness – yet – and there is certainly widespread debate about when or whether this will happen. Additionally, we do not yet know what the full potential and impacts of AI will be”. 26, 27 However, there can be no doubt that AI is a disruptive innovation that has developed rapidly over the last several decades and is now transforming every aspect of society. 28, 29, 30 To some extent, democratization of AI is taking place with the widespread access to ChatGPT. 31 Just as graphical user interface (GUI) operating systems such as Microsoft Windows and Apple Macintosh made home computers more useable to a broader audience and America Online (AOL), Netscape, and Microsoft’s Internet Explorer allowed for the expansion of web-based personal and business transactions to take place, we are now on the cusp of even more widespread personalized AI use. The collaboration between OpenAI and Microsoft has brought ChatGPT to the market and industry giants such as Google, Apple, and Meta, are all investing heavily in AI technologies to improve their services and bring new products to market. In fact, AI and AI-based systems are becoming ubiquitous. Millions of people now ask their technological personal assistants such Siri or Alexa about the weather forecast, to play a favorite song, or turn off lights. News and information feeds on social media platforms are algorithmically tailored to individual users likes, dislikes, and search behaviors. Airplanes carrying hundreds of passengers fly on autopilot. Amazon and most other retail websites automatically give customers product recommendations based on data analytics of millions of purchase histories. Customer service calls are increasingly answered by a disembodied voice that uses voice recognition technology to respond to customer questions or requests. Driver-assist technologies can be used to park cars or give warnings when a car is drifting out of its lane. These are all automated systems outside of direct human control, yet they are impacting human behavior on an everincreasing basis. While it remains to be seen whether AI will have a net positive or net negative effect on humanity, we can be sure that it will increasingly impact humanity. These new products and technologies will continue to shift business environments, labor markets, and entrepreneurial ecosystems.

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We will experience a new cycle of creative destruction 32 quite possibly unlike any other in history in terms of the scope and breadth of its impacts and the speed with which new technologies are implemented. AI is already transforming workplaces and the distribution of labor through increased automation. 33 AI systems will continue to impact the employment landscape with significant job replacement 34 and there are predictions that as much as a third of the most common jobs currently being pursued by U.S. students will be automated over the next decade. 35 While the Schumpeterian view would suggest that there will be new and better jobs created as the process of creative destruction takes place, it is unclear whether new job creation will be able to keep up with job losses resulting from AI automation. 36 Time will tell what happens to the overall labor market, but what is more certain is that automation and AI systems create enormous efficiencies and reduce labor costs which increases company profits. This is why companies are racing toward AI solutions. However, the most important benefit of AI is its ability to reduce uncertainty in decision-making by expanding what Simon 37, 38 referred to as bounded rationality. Individual human beings are limited in their ability to process and store information which results in bounded rationality. 39 Unfortunately, we cannot know everything personally. However, AI systems and technologies can help expand the boundaries of rationality by allowing access to knowledge that is revealed through automated processes which can sift through – and make sense of – enormous quantities of data. Unlocking hidden knowledge from data is where firms will find new and more unique ways to reduce inefficiencies and increase profitability. AI’S POTENTIAL RISKS OF CEMENTING SYSTEMATIC RACISM As mentioned earlier, it is tempting to believe that AI offers purely objective solutions since decision-making is done by algorithms that are trained using massive amounts of real-world data. Ideally, that would be true, but what happens when the data is already biased? As Courtland 40 points out, training data is rarely bias-free. Systemic biases within society are likely to be a part of the data that is used to train AI systems. 41, 42 We can see direct examples of this in facial recognition software. A recent National Institute of Standards and Technology (NIST) study tested and found various facial recognition algorithms to be significantly more accurate for White male faces than for those of minorities, women, and infants. 43, 44 The problem is primarily a result of biased or inadequate training data. If an AI algorithm used to recognize faces is trained with one million pictures of faces, but 90 percent of those faces are White and male, then it is likely

ARTIFICIAL INTELLIGENCE: CONSIDERATIONS FOR BLACK EXECUTIVES

that the algorithm will be better at identifying White male faces. The NIST study found that the algorithms made the most errors with Black female faces.45 With law enforcement and government security systems starting to use such systems, there are growing risks of false arrest and detention of innocent individuals, particularly minority individuals. Unfortunately, this has happened to at least one Black man incorrectly identified as a suspected thief by faulty AI facial recognition software. 46 There are many other examples of bias that we can find in the AI systems we use every day. A widely used algorithm that guided healthcare decisions misdiagnosed the health levels of Black patients. 47 The racial bias was a result of the algorithm being programmed to equate health costs with health needs. The problem was that racial differences in access to healthcare was not factored in and the system was essentially trained to believe that Black patients were inherently more healthy than White patients. The result was that White patients were recommended treatments that were not recommended for equally sick Black patients. Fortunately, most of the biases built into AI systems do not have life or death consequences. For example, male pronouns are the default setting in Google Translate even while translating gender-neutral language. 48 Let us recognize that AI is a tool, and it is a tool that is only as good as its programming and the data used to train it. The systemic biases that have been built into some AI systems are making decisions that are discriminatory toward minorities, women, and often poorer people. 49, 50, 51, 52 As AI is implemented to automate processes within companies, industries, and over wider segments of the economy and society, there are significant risks that structural racism and discrimination may become more entrenched rather than reduced. We know that for decades, cannabis use and distribution were criminal offenses that led to the disproportionate incarceration of Black individuals compared to White individuals. 53 If historical sentencing data were used to train an AI tasked with recommending jail time for lowerlevel drug offenses, one could easily imagine a scenario in which Black and minority individuals could be given unequal sentences to White individuals and unfairly punished. The use of historical data to train AI systems can be problematic. Beyond the examples just mentioned above, there is evidence of significant discrimination in lending practices such that assistance with the loan process, loan decisions, and terms such as interest rates on those loans have favored White borrowers. 54, 55, 56, 57, 58 It is likely that Black borrowers would continue to struggle to acquire fair and equal loans if an AI system to screen and recommend decisions on loan applications was trained with historical data without statistical controls and corrections to reduce and

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ARTIFICIAL INTELLIGENCE: CONSIDERATIONS FOR BLACK EXECUTIVES

eliminate bias. Instead of a decision being biased on unfair bank policies or an individual underwriter who knowingly or unknowingly discriminates against minorities, it would be through an automated AI system that was trained with faulty data.

at top ranked universities, there are many qualified graduates with technology degrees at regional colleges and Historically Black Colleges and Universities (HBCUs). Leading companies should continue to expand their recruiting efforts to these locations as well.

IMPORTANT ROLE OF BLACK EXECUTIVES TO ENSURE AI FAIRNESS As discussed above, AI is going to play an increasing role in society and the great potential of AI to help advance corporate profitability also comes with the great responsibility of ensuring that they are fair and free from bias in their decision-making programming. The risk of not doing so is that AI systems and processes could result in maintaining the status quo if they are only trained with historical data. For many Black, minority, and female stakeholders, the status quo is unacceptable and in need of change. To be clear, AI is a tool and in and of itself is not moral or right or wrong, and it is certainly not inherently racist. Any discriminatory or systematically racist tendencies come from the programming and training data used to help an AI system learn and develop its decision-making processes. It is up to the executives and managers within the companies and institutions that adopt AI systems, and in particular Black executives who may be more sensitive to potential biases within AI systems, to provide leadership and oversight over the new technology.

The other advantage of increasing the percentage of Black workers at leading technology firms is that it can help spur Black entrepreneurship in the lucrative AI and technology sector. This can help to build a critical mass of Black technology workers and entrepreneurs who work to address potential biases in AI systems in the future.

Three recommendations are offered below that Black executives should focus attention toward. These recommendations should not be considered exhaustive, but are of immediate and special importance to protecting and ensuring freedom from biases which may disproportionately impact traditionally disadvantaged stakeholders. WORK TO ACHIEVE DIVERSITY ON AI DEVELOPMENT TEAMS AI researchers, computer scientists and software developers are overwhelmingly male, and White or Asian. While just over 14 percent of the population is Black, just eight percent of technology workers is Black. 59 The percentages are even lower among the workforces at leading technology companies such as Google, which reports that just over four percent of their employees are Black. 60 This presents a major challenge as concerns about certain racial biases within AI may not be a priority among developers. Having a diverse development team does not ensure freedom from racial biases but having people from different backgrounds makes it more likely that different perspectives will be a part of the process. This can help identify and reduce the possibility of bias. Although the Supreme Court struck down affirmative action admissions programs, and this will likely have a chilling effect on minority admissions

RECOGNIZE AND WORK TO REMOVE BIASES IN AI TRAINING DATA This starts with recognizing and acknowledging that discrimination and racism exists and that it is often entrenched within existing data. Racism is both a group and individual phenomenon that functions on many levels and may be identified through institutional and systematic phenomenon. 61 Institutional racism refers to institutional and cultural practices that perpetuate racial inequality; it is the policies and practices of organizations or parts of societal systems (e.g. education, legal, financial systems) that create different outcomes for different racial groups. While individual prejudice and overt forms of racial discrimination may be easier to identify when they occur, structural racism is subtle and easily hides behind everyday activities, including rules and policies that claim to treat Blacks and Whites equally. 62 It is often overlooked and it is taken for granted 63 making it difficult to see the subtle ways in which it shapes our perspectives and systems, and is embedded within structures and institutions. 64 Since the reasons for disparate impact are often disputed, structural racism is frequently unacknowledged and/or denied, making it problematic to resolve. This makes it difficult to clearly identify bias within data and requires careful examination. Black executives are more likely to suspect and/or recognize issues of structural racism and work to address it. With respect to AI system development, before training data is utilized for any AI system, programmers must carefully consider sources of bias and whether the data utilized is as free from bias as possible. When bias is found, appropriate statistical corrections and data transforms may be necessary. ENSURE TRANSPARENCY AND DEVELOP FAIR APPEALS PROCESSES AI’s programming cannot remain in a “black box” that is not subject to any review. Companies should be able to maintain a certain level of propriety and security in their development processes; however, they must allow

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access to their source code similar to ATMs, voting machines, and electronic gaming machines used in casinos. AI experts must be given access to better understand the algorithms and training data used in developing AI systems. Transparency is a necessary condition to build acceptance in systems. The review process will also help to identify sources of bias. It should be recognized that review and understanding of how AI systems make decisions will become ever more difficult as neural network learning and future systems become more complex. 65, 66 How to proceed with oversight is already a challenge given the rapid changes already happening as a result of machine learning. 67, 68

ARTIFICIAL INTELLIGENCE: CONSIDERATIONS FOR BLACK EXECUTIVES

recommendations are offered for reducing the possibilities of bias and correcting errors that may emerge inadvertently as a result of unrecognized bias in training data, faulty programming, or even unknown reasons due to the complexities of neural network learning models. Without managing the potential for error, AI may only serve to solidify and further entrench structural racism within society. Black executives must remain vigilant to the risks and take a leading role in preventing historical biases to infiltrate these new AI systems.

Further, as illustrated by some of the examples earlier, AI does not always result in the best or most reliable decision-making. This makes transparency and critical evaluation of AI an even more pressing issue. 69 With critical internal and external reviews of AI programming, regulatory oversight, the adoption of industry standards and best practices, and other means of evaluating AI systems, there should also be fair and humandriven appeals processes to ensure that errors or biases which may creep into automated systems can be changed. Remaining open-minded about the possibility of mistakes with respect to AI decision-making processes can help to ensure fairness and acceptance of these processes. CONCLUDING THOUGHTS The rapid advances of AI technology and its adoption across all aspects of society will increasingly continue to impact humanity. Many of these impacts will be in ways that we likely cannot yet see. This is why there is a widespread debate of the efficacy and wisdom of AI – everything from warnings that humanity will soon be slaves to AI overlords or, on the other hand, to argue that we can look forward to a new golden age of milk and honey resulting from AI. Taking a more neutral approach, this article acknowledges the growing importance of AI while also identifying the potential risks of such systems. Examples already exist of AI making errors that impact people of color and women at a much higher rate than White males. This is largely due to inherent biases within training data utilized by AI systems to learn their decision-making processes. AI offers the tantalizing promise of democratizing information, expanding access to knowledge, and offering purely objective decision-making that is free from human frailties and biases, but only if the systems are developed carefully. In order to prevent the negative and potentially dangerous consequences of poorly designed AI systems discussed earlier, three

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Notes: 1. Haenlein, M., & Kaplan, A. J. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5-14. 2. Castelvecchi, D. (2016). Can we open the black box of AI? Nature, 538(7623), 20-23. 3. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (2006). A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Magazine, 27(4), 12-14. 4. Wang, Y. (2020). When artificial intelligence meets educational leaders’ data-informed decision making: A cautionary tale. Studies in Educational Evaluation, doi: 10.1016/j.stueduc.2020.100872 5. Autor, D. H. (2015). Why are there still so many jobs? The history and future of workplace Automation. Journal of Economic Perspectives, 29(3), 3-30. 6. Miller, B. & Atkinson, R.D. (2013). Are robots taking our jobs, or making them? The Information Technology and Innovation Foundation, Washington, DC. 7. Ibid 8. Barnhizer, D., & Barnhizer, D. (2019). The artificial intelligence contagion: Can democracy withstand the imminent transformation of work, wealth and the social order? Atlanta, GA, Clarity Press Inc. 9. Ford, M. (2015). The rise of the robots: Technology and the threat of mass unemployment. Oneworld Publications, London. 10. Zeira, J. (2018). Workers, machines, and economic growth. The Quarterly Journal of Economics, 113(4), 10911117. 11. Vallance, C. (2023). May 30. Artificial intelligence could lead to extinction, experts warn. BBC News. Available online: https://www.bbc.com/news/uk-65746524 12. Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Vintage Books. 13. Courtland, R. (2018). Bias detectives: the researchers striving to make algorithms fair. Nature, 558(7710), 357. 14. Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256-270. 15. Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair. Nature, 559, 324-326. 16. Wang, Y. (2020). When artificial intelligence meets educational leaders’ data-informed decision making: A cautionary tale. Studies in Educational Evaluation, doi: 10.1016/j.stueduc.2020.100872 17. Akselrod, O. (2021). How artificial intelligence can deepen racial and economic inequities. ACLU. Available online: https://www.aclu.org/news/privacy-technology/how-artificial-intelligence-can-deepen-racial-and-economicinequities 18. Lifshitz, B. (2021). Racism is systemic in artificial intelligence systems, too. Georgetown Security Studies Review. Available online: https://georgetownsecuritystudiesreview.org/2021/05/06/racism-is-systemic-in-artificialintelligence-systems-too/ 19. Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256-270. 20. Zou, J., & Schiebinger, L. (2018). AI can be sexist and racist—it’s time to make it fair. Nature, 559, 324-326. 21. Akselrod, O. (2021). How artificial intelligence can deepen racial and economic inequities. ACLU. Available online: https://www.aclu.org/news/privacy-technology/how-artificial-intelligence-can-deepen-racial-and-economicinequities 22. Courtland, R. (2018). Bias detectives: the researchers striving to make algorithms fair. Nature, 558 (7710), 357. 23. Murray, A. (2022). How artificial intelligence can help combat systemic racism. MIT News. Available online: https://news.mit.edu/2022/how-ai-can-help-combat-systemic-racism-0316 24. Whitfield-Anderson, A. (2023). Can artificial intelligence be racist? Yahoo News 360. Available online: https:// news.yahoo.com/can-artificial-intelligence-be-racist-172952795.html?fr=sycsrp_catchall 25. Bostrom, N. (2015). What happens when our computers get smarter than we are? TED Talk. Available online: https://www.ted.com/talks/nick_bostrom_what_happens_when_our_computers_get_smarter_than_we_are/transcript 26. Hauer, T. (2018). Society and the second age of machines: Algorithms versus ethics. Society, 55: 100–106. https://doi.org/10.1007/s12115-018-0221-6 27. Hauer, T. (2019). Society caught in a labyrinth of algorithms: disputes, promises, and limitations of the new order of things. Society, 56: 222–230. https://doi.org/10.1007/s12115-019-00358-5 28. Barnhizer, D., & Barnhizer, D. (2019). The artificial intelligence contagion: Can democracy withstand the imminent transformation of work, wealth and the social order? Atlanta, GA, Clarity Press Inc. 29. Brynjolfsson, E., & McAfee, A. (2017). The business of artificial intelligence. Harvard Business Review, July 18, Available online: https://hbr.org/cover-story/2017/07/the-business-ofartificial-Intelligence 30. Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Vintage Books. 31. Mollick, E. (2022). ChatGPT is a tipping point for AI. Harvard Business Review. Available online: https://hbr. org/2022/12/chatgpt-is-a-tipping-point-for-ai 32. Schumpeter, J. (1934). The theory of economic development. Cambridge, MA: Harvard University Press. 33. Frank, M.R., Autor, D., Bessen, J.E., Brynjolfsson, E., Cebrian, M., Deming, D.J., Feldman, M., Groh, M., Lobo, J., Moro, E. and Wang, D. (2019. Toward understanding the impact of artificial intelligence on labor. Proceedings of the National Academy of Sciences, 116(14), 6531-6539. 34. Brynjolfsson, E., & Mitchell, T. (2017). What can machine learning do? Workforce implications. Science, 358, 1530-1534.

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35. Zeira, J. (2018). Workers, machines, and economic growth. The Quarterly Journal of Economics, 113(4), 10911117. 36. Barnhizer, D., & Barnhizer, D. (2019). The artificial intelligence contagion: Can democracy withstand the imminent transformation of work, wealth and the social order? Atlanta, GA, Clarity Press Inc. 37. Simon, H. A. (1972). Theories of bounded rationality. Decision and Organization, 1(1), 161-176. 38. Simon, H. A. (1976). Administrative behavior, 3rd edition. New York: Free Press. 39. Ibidi. 40. Courtland, R. (2018). Bias detectives: the researchers striving to make algorithms fair. Nature, 558(7710), 357. 41. Akselrod, O. (2021). How artificial intelligence can deepen racial and economic inequities. ACLU. Available online: https://www.aclu.org/news/privacy-technology/how-artificial-intelligence-can-deepen-racial-and-economicinequities 42. Whitfield-Anderson, A. (2023). Can artificial intelligence be racist? Yahoo News 360. Available online: https:// news.yahoo.com/can-artificial-intelligence-be-racist-172952795.html?fr=sycsrp_catchall 43. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test part 3: Demographic effects (NISTIR 8280), National Institute of Standards and Technology, https://doi.org/10.6028/NIST.IR.8280 44. Meyer, C. (2020). Facial recognition error rates vary by demographic. Security Management, Available online: https://www.asisonline.org/security-management-magazine/articles/2020/05/facial-recognition-error-rates-vary-bydemographic/ 45. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test part 3: Demographic effects (NISTIR 8280), National Institute of Standards and Technology, https://doi.org/10.6028/NIST.IR.8280 46. Murray, A. (2022). How artificial intelligence can help combat systemic racism. MIT News. Available online: https://news.mit.edu/2022/how-ai-can-help-combat-systemic-racism-0316 47. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. 48. Prates, M. O., Avelar, P. H., & Lamb, L. C. (2020). Assessing gender bias in machine translation: A case study with Google translate. Neural Computing and Applications, 32, 6363-6381. 49. Grother, P., Ngan, M., & Hanaoka, K. (2019). Face recognition vendor test part 3: Demographic effects (NISTIR 8280), National Institute of Standards and Technology, https://doi.org/10.6028/NIST.IR.8280 50. Murray, A. (2022). How artificial intelligence can help combat systemic racism. MIT News. Available online: https://news.mit.edu/2022/how-ai-can-help-combat-systemic-racism-0316 51. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453. 52. Wang, Y. (2021). Artificial intelligence in educational leadership: a symbiotic role of human-artificial intelligence decision-making. Journal of Educational Administration, 59(3), 256-270. 53. ACLU (2020). A tale of two countries: Racially targeted arrests in the era of marijuana reform. ACLU Research Report. Available online: https://www.aclu.org/sites/default/files/field_document/marijuanareport_03232021.pdf 54. Bates, T., & Robb, A. (2013). Greater access to capital is needed to unleash the local economic development potential of minority-owned businesses. Economic Development Quarterly, 27(3), 250-259. 55. Cavalluzzo, K., & Cavalluzzo, L. (1998). Market structure and discrimination: The case of small businesses. Journal of Money, Credit, and Banking, 30, 62-87. 56. Gabriel, S. A., & Rosenthal, S. (1991). Credit rationing, race, and the mortgage market. Journal of Urban Economics, 29, 371-379. 57. Munnell, A., Browne, L., McEneaney, J., & Tootell, G. (1996). Mortgage lending in Boston: Interpreting HMDA data. American Economic Review, 86, 25-53. 58. Palia, D. (2016). Differential access to capital from financial institutions by minority entrepreneurs. Journal of Empirical Legal Studies, 13(4), 756-785. 59. Gonzales, M. (2022). The need for more Black workers in tech. Society of Human Resource Management (SHRM). Available online: https://www.shrm.org/resourcesandtools/hr-topics/behavioral-competencies/global-andcultural-effectiveness/pages/the-need-for-more-black-workers-in-tech.aspx 60. Ibid. 61. Stovall, D. (2005). A challenge to traditional theory: Critical race theory, African-American community organizers, and education. Discourse: studies in the cultural politics of education, 26(1), 95-108. 62. García, J. J., Gee, G. C., & Jones, M. (2016). A critical race theory analysis of public park features in Latino immigrant neighborhoods. Du Bois Review: Social Science Research on Race, 13(2), 397-411. 63. Lopez, G. R. (2003). The (racially neutral) politics of education: A critical race theory perspective. Educational Administration Quarterly, 39(1), 68-94. 64. Singh, R. P., & Nurse, S. (2023). Addressing the racial wealth gap and structural racism through black entrepreneurship: An entrepreneurial ecosystem perspective. 2023 U.S. Association for Small Business and Entrepreneurship Conference. Tallahassee, FL. January 18-22. 65. Barnhizer, D., & Barnhizer, D. (2019). The artificial intelligence contagion: Can democracy withstand the imminent transformation of work, wealth and the social order? Atlanta, GA, Clarity Press Inc. 66. Tegmark, M. (2017). Life 3.0: Being human in the age of artificial intelligence. Vintage Books. 67. Hauer, T. (2018). Society and the second age of machines: Algorithms versus ethics. Society, 55: 100–106. https://doi.org/10.1007/s12115-018-0221-6 68. Hauer, T. (2019). Society caught in a labyrinth of algorithms: disputes, promises, and limitations of the new order of things. Society, 56: 222–230. https://doi.org/10.1007/s12115-019-00358-5 69. Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: A new era has begun. Small Business Economics, 55, 529–539

Did Firms Change Their Tax Avoidance Practices after the Tax Cuts and Jobs Act of 2017? by by Yan Jin, Ph.D. and Tao Zeng, Ph.D. This article aims to examine whether firms changed their tax avoidance practices after the Tax Cuts and Jobs Act (TCJA) was enacted. Using listed companies during the implementation of TCJA, this study finds that firms engaged in less tax avoidance activities after TCJA, as evidenced by (1) a lower permanent book-tax difference; (2) a lower total book-tax difference; and (3) lower levels of current unrecognized tax benefits.

Yan Jin, Ph.D. is an assistant professor at Hampton University. Tao Zeng, Ph.D. is an associate professor at Wilfrid Laurier University. MOREOVER, this study further isolates the effect of TCJA on tax base avoidance and finds that larger, more profitable and/or leveraged firms are less likely to engage in tax base avoidance activities after TCJA.

1. INTRODUCTION The Tax Cuts and Jobs Act (TCJA) was signed by President Donald Trump at the end of 2017 and enacted in 2018. It substantially reduced the statutory federal corporate income tax rate from the previous 35% to 21%, introduced a minimum tax of 10% (5% in 2018) on domestic income earned by firms before deductions of certain imports from related foreign parties, as well as other changes that affect firms’ tax payments. The former change attempts to reduce the tax burdens of US firms and the latter aims to reduce the extent to which US firms shift income out of the country by using internal transfer pricing that inflates the cost of imports from related foreign parties. 1 There have been a number of studies examining the effects of TCJA on stock market, firm financing and investments. 2, 3, 4, 5, 6 Studies of TCJA on tax avoidance are limited, with the exception of a working paper by Dyreng et al.. 7 As argued by Wilde and Wilson, 8 it is time for tax avoidance research to examine beyond the micro level (what firms do) and focus on the macro level, such as the relation between a given tax policy and tax avoidance, and whether the tax policy achieves its goals. We are responding to their call and filling the gap by examining the impact

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

of TCJA on tax avoidance.

The rest of this paper is organized as follows. In section 2, we review the literature and develop hypotheses. In section 3, we specify our research methodology including data and sample selection, models, and variables. Our empirical results are presented in section 4. We conclude in section 5.

Using listed companies during the implementation of TCJA and multiple measures of tax avoidance including book-tax difference (BTD) and unrecognized tax benefit (UTB), this study found that firms engaged in less tax avoidance activities after TCJA, as evidenced by (1) a lower permanent book-tax difference; (2) a lower total book-tax difference; and (3) lower levels of current unrecognized tax benefits. Moreover, this article further isolates the effect of TCJA on tax base avoidance and finds that larger, more profitable and/or leveraged firms are less likely to engage in tax base avoidance activities after TCJA. We include several robustness tests in our study: (1) using GAAP and Cash Effective Tax Rates (GAAP and Cash ETRs) as alternative measures for tax avoidance, we draw the same inference as the main analysis; (2) following Dyreng et al., 9 we added Transition Year as an additional indicator variable and the results show that Transition Year did not capture the effect of TCJA; (3) we also added interaction terms of TCJA with main control variables such as SIZE, ROA and LEV to our regression models in the main analysis and the results show that larger, more profitable and/or leveraged firms are less likely to engage in tax base avoidance activities after TCJA; and (4) we also replaced current year UTB with current year UTB additions (ΔUTB) as the dependent variable and the results suggest that the TCJA did not trigger a timely change in firms’ tax positions. This article makes two contributions to the existing literature regarding the impact of government policy changes on corporate tax reporting practices. First, to the best of our knowledge, this paper is the first study that examines the effect of TCJA on corporate tax avoidance measured by BTD and UTB. Tax avoidance has become a high-profile agenda topic and has gained increasing attention from the public, regulators, and academics in recent years. Numerous studies attempt to explain why a wide variety of tax avoidance practices exist and examine what triggers these activities (see Wilde and Wilson 2018 for a review). 10 However, there are limited tax avoidance studies examining the impact of government policy changes on tax avoidance. This paper seeks to fill this gap by examining the impact of TCJA on tax avoidance. Second, following Lamoenius et al., 11 we decompose tax avoidance into two separate components: tax rate avoidance and tax base avoidance. Using the setting of TCJA, we find that larger, more profitable and/or leveraged firms are less likely to engage in tax base avoidance activities when the statutory tax rate is substantially reduced.

2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Corporate tax avoidance has drawn increasing attention from regulators, the public, and academics. Recent research documents that firms have successfully avoided more federal income taxes in recent years than in the past. Dyreng et al. provide evidence showing that cash effective tax rates of US corporations have declined by approximately 10 percentage points in the past 25 years. 12 A study by Gaertner et al. documents that there is an overall increase in the book-tax gap over the period of 2004 to 2013. 13 The Organization for Economic Co-operation and Development (OECD) emphasizes the importance of a cross-country exchange of tax information and argues that, as the global economy increases and crossborder activities are more common, tax administrations around the world should work together to ensure that taxpayers pay their fair amount of tax to the right jurisdiction. Among academics, a long line of research shows that corporate tax avoidance is associated with firm-level characteristics such as governance, executives’ incentives, financial constraints, foreign operations, and market power, among other factors. 14, 15 The literature studying tax avoidance in an international setting also finds that countrylevel factors associated with tax avoidance include legal and institutional environments, accounting standards, tax enforcement, and statutory tax rates. 16, 17, 18 The TCJA significantly reduced federal income tax rates on corporations from 35% to 21%. Some regulators and policy makers are concerned that firms would pay less tax and government revenue would be affected negatively by TCJA. Corporate tax payments can be simply calculated as taxable income multiplied by the tax rate. Therefore, tax paid by corporations could be reduced after the implementation of TCJA in the case where a lower corporate tax rate fails to attract new investments and boost corporate revenue and taxable income. On the other hand, supporters of the new tax law argue that TCJA might enhance tax payments through increasing taxable income. First, TCJA could reduce tax avoidance activities, including those leading to a reduction of taxable income. Second, TCJA could provide tax relief and leave more after-tax income with firms, which might stimulate more investments, in turn generating more revenue and taxable income in the future.

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

The statistics show that US corporate income tax revenues were $343.8 billion for 2015, $299.6 billion for 2016, $297 billion for 2017, $204.7 billion for 2018 (30% decrease from 2017) and $230 billion for 2019 (10% increase from 2018) respectively.19 The significant reduction of corporate income tax revenues in 2018 and 2019 reflected the impact of TCJA (i.e., 40% decrease in corporate tax rate from 35% to 21%).

3. RESEARCH METHODOLOGY 3.1. Measures of Tax Avoidance There are several tax avoidance measures developed in prior literature (see Hanlon and Heitzman 2010 for a summary of the measures).28 Dyreng et al. (2020) compare the effective tax rates and tax expenses before and after the TCJA. For this study, the key was to choose the measures that are appropriate for our research questions.

The statutory tax rate is an important component of the US tax system. Prior literature finds a positive relation between the statutory tax rate and tax avoidance practices among multinational corporations in the US where domestic tax rates were much higher than foreign tax rates. 20, 21 Beer et al. reviewed the existing studies on international tax avoidance by multinational corporations. 22 They find that for every 1% decrease in a country’s corporate tax rate compared to other countries, there is a 1.5% increase on income shifting. In addition, cross-country studies of tax avoidance also show that firms in a country with a higher statutory tax rate are more likely to engage in tax avoidance. 23, 24, 25, 26 On the contrary, a lower statutory tax rate leads to less savings from manipulating taxable income. Therefore, we propose our first hypothesis as follows to reflect the effect of TCJA on overall tax avoidance: Hypothesis 1: Firms are less likely to engage in tax avoidance following TCJA This study aims to explore whether TCJA affects firms’ tax avoidance practices when there is a change in the corporate tax rate. Lamoenius et al. developed an approach to decompose tax avoidance into two separate components: tax rate avoidance and tax base avoidance.27 We adopt the notion of tax base avoidance from Lamoenius et al. and attempt to isolate the effect of the corporate rate change due to TCJA, and focus on tax base (i.e., taxable income) avoidance in the second hypothesis. Some examples of tax base avoidance include decreasing taxable income through the acceleration of expenses or the deferral of revenues, bonus depreciation, and tax credits for research and experimentation. Because some of these tax avoidance strategies can be rather costly to set up or are associated with high uncertainty in one’s tax position, we argue that firms are less likely to engage in tax base avoidance when the corporate tax rate has been significantly reduced. Based on the above arguments, we specify the second hypothesis as follows. Hypothesis 2: Firms are less likely to engage in tax base avoidance following TCJA In the next section, we describe our research methodology including models, variables and sample data.

For the first hypothesis, we use unrecognized tax benefit (UTB) to capture overall tax avoidance. Before deciding to engage in tax avoidance, firms often conduct a cost-benefit analysis. The benefit from avoiding taxes is an increase in a firm’s after-tax cash flow. The potential cost includes chances of being audited and penalized by tax authorities, reputational damages, and rent extraction by managers using complex and hidden transactions. 29,30, 31, 32, 33 When firms engage in uncertain tax activities to avoid taxes, there is a probability that they will be audited by tax authorities or that they will be required to pay taxes related to these activities in the future. To account for this uncertain tax position and the associated contingent liability, firms can record an accounting reserve, which is called an unrecognized tax benefit (UTB). UTBs measure the uncertainty in firms’ tax positions and hence indicate some level of tax avoidance activities. A stream of existing studies show that UTB is indicative of tax avoidance engagements. 34, 35, 36 For instance, Lisowsky et al. (2013) report that UTB is positively associated with tax sheltering activities that have been identified by tax authorities. Gupta et al. (2014) further reveal that UTB is associated with tax avoidance at the state level. Lastly, Dyreng et al. (2019) find that tax avoiders (i.e., firms with relatively lower cash effective tax rates) bear significantly greater tax uncertainty than firms that have higher cash effective tax rates. Prior studies provide some evidence regarding the effects of TCJA on tax avoidance and corporate tax preferences. 37, 38, 39 Dyreng et al. (2020) found that the TCJA lowered the average firm’s GAAP (Cash) ETR by 11.4 (5.7) percentage points. In addition, the TCJA significantly reduced the extent to which profitable firms are tax favored and will reduce the corporate profits of U.S. multinational affiliates in haven countries by about 12–16 percent. 40 However, a lower ETR below the statutory tax rate does not necessarily indicate tax avoidance. 41, 42 Schwab et al. (2022) document that ETRs below 5% and above 40% are significantly influenced by other factors unrelated

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

to tax avoidance, such as valuation allowances and goodwill impairments. Valuation allowances (VA) related to prior-period losses downwardly bias GAAP ETRs and cash ETRs for both domestic and multinational firms.

problems (1,148 observations are dropped). We then removed 900 outliers (top and bottom 1% of ROA, LOSS, and LEV). There were 29,393 observations in our final sample (see Table 1 for details). We also include the industry breakdown in Table 1. The top 5 industries included in our final sample are financial services, manufacturing, information, real estate and mining.

GAAP ETR (Cash ETR) is expressed as the ratio of tax expense (cash taxes paid) to pre-tax book income. Even when firms present stable tax avoidance behavior, ETRs can change over time because of growth in pre-tax income. 43 Therefore, it is necessary to complement prior studies by adopting multiple measures other than ETRs to capture tax avoidance. When complying with TCJA, the most significant change to corporate taxation since the Tax Reform Act of 1986, firms face great uncertainty in their tax positions and tax avoidance attempts are likely to be triggered under such circumstances. In the face of this uncertainty, firms are likely to book UTBs as they engage in tax avoidance, and thus using UTBs to measure tax avoidance influenced by TCJA is suitable for our study. For the second hypothesis, we use book-tax differences (BTDs) measuring the difference between book income and taxable income to capture nonconforming tax base avoidance. ETRs show the mixed results of tax rate avoidance and tax base avoidance driven by tax expense (cash taxed paid) and pre-tax book income. On the other hand, BTDs only focus on tax base avoidance by isolating the impact of tax rate changes, which is appropriate for the setting of this paper (i.e., TCJA). Multiple studies confirm that BTDs capture some tax avoidance engagements. 44, 45 For example, Mills (1998) finds that firms with a large book-tax gap are more likely to be audited by tax authorities and make more proposed audit adjustments subsequently. 46 Desai (2003) documents that the increase of BTDs in the 1990s can be partially explained by tax shelters. 47 Other similar studies (Wilson 2009; Frank et al. 2009; Lisowsky 2010) show that firms involved in aggressive tax shelters have a larger book-tax gap, compared with their counterparts. 48, 49, 50 However, BTDs only capture some elements of tax base avoidance such as non-conforming tax avoidance activities. As Hanlon and Heitzman (2010) argue, it cannot capture conforming tax avoidance, which reduces both book income and taxable income. 3.2. Data and sample The sample data used in this study are US listed firms for the time period of 2015-2019 around the implementation of TCJA. A total of 31,441 observations were collected from the Compustat database. We removed firm-years with total assets less than $1 million to mitigate small deflator

3.3. Models and variables Tax avoidance is defined as any activity that reduces or defers tax liabilities based on Hanlon and Heitzman (2010). 51 We use unrecognized tax benefit (UTB) to measure overall tax avoidance, and book-tax difference (BTD) to measure tax base avoidance. To test the two hypotheses, we implemented the following models where we evaluated the impact of the TCJA on tax avoidance measured by UTB and BTD. UTB it = α 0+α 1 TCJA it + δY it + Industry dummies + ε it BTD it = α 0+α 1 TCJA it + δY it + Industry dummies + ε it

(1) (2)

Where UTB: unrecognized tax benefits, measured as unrecognized tax benefit deflated by total assets. BTD: book-tax difference, can be measured by two proxies – permanent BTD (PBTD) and total BDT (TBTD). PBTD is measured as pre-tax earnings net of taxable income, where taxable income is estimated as the total income tax expenses over the statutory tax rate; and TBTD is measured as pre-tax earnings net of taxable income, where taxable income is estimated as the current income tax expenses over the statutory tax rate. TCJA: an indicator variable, equal to 1 for years 2018 and 2019, and 0 otherwise. Y: a set of control variables We controlled for certain firm characteristics found in prior studies that potentially influenced a firm’s tax avoidance (see Wilde and Wilson 2018 for a review). 52 These control variables include firm size (SIZE), calculated as the log of total assets; profitability defined as return on asset (ROA), calculated as net earnings over total assets; leverage (LEV), the ratio of short-term and long-term debts to total assets; capital intensity (PPE), the ratio of fixed assets to total assets; goodwill (GW), the ratio of goodwill to total assets; inventory intensity (INV), the ratio of inventory to total assets; intangible assets (INT), the ratio of intangible assets to total assets; R&D expenditures (R&D), measured as annual R&D expenses over total

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

assets; loss-carry-over (LOSS), measured as net loss-carry-overs over total assets; and auditor quality (AUD), an indicator variable, equal to 1 if a firm is audited by the Big 4, and 0 otherwise. In addition, we include industry dummy variables to control for potential industry fixed effects.

three measures for the pre- and post-TCJA sample periods shows that the difference in permanent BTD between the two periods is significant at the 10% level (-0.0901 for pre- vs. -0.0904 for post-), and the mean differences in total BTD (-0.0898 for pre- vs. -0.0892 for post-) and UTB (0.00545 for pre- vs. 0.00533 for post-) are not statistically significant.

We predict that SIZE is positively associated with tax avoidance, implying that larger firms engage in more tax avoidance, which is relatively consistent with Kim and Zhang (2016) 53 to the extent that large firms are more powerful and capable of negotiating more favorable policies, including tax treatments. ROA is positively associated with tax avoidance, which is consistent with both Dyreng et al. (2008) 54 and Lennox et al. (2013) 55, suggesting that profitable firms are more likely to avoid taxes. LEV is positively associated with UTB, which is consistent with the argument that interest expenses are tax deductible. 56, 57, 58, 59, 60 BIG4 is negatively associated with unrecognized tax benefits, suggesting that firms audited by the Big 4 engage in less tax avoidance. This is consistent with findings from both Klassen et al. (2016) and Kanagaretnam et al. (2016). 61, 62 4. TESTING RESULTS 4.1. Univariate statistics The descriptive statistics of major variables are provided in Table 2. It shows that, on average, the unrecognized tax benefit (UTB) is 0.005, the permanent book-tax difference (PBTD) is -0.102, and the total book-tax difference (TBTD) is -0.097. Table 2 also demonstrates that, on average, firms’ total assets are $14,614 million; profitability (ROA) is -0.075, implying that, on average, firms incurred a loss in the sample time periods. The average leverage (LEV) is 0.178 and the median value shows that over 50% of the sample observations have less than 3.2% of debts. The average and median values of inventory (INV) are 0.06 and 0.002, respectively. The average capital intensity (PPE) is 0.174, which is higher than the median value of 0.046. Loss-carry-over (LOSS) has a significant variation and the average value is 0.684. The median value of foreign income (FOR), R&D investment (R&D), intangible assets (INT), and goodwill (GW) is zero, implying that more than 50% of the sample observations have no foreign income, no R&D expenditures, no intangible assets, and no goodwill assets. Table 2 also shows that, over three quarters (78%) of sample firms are audited by the Big 4 and over one third of firm-years are from the years after 2017 (i.e., after TCJA). The un-tabulated univariate test for differences in means across the

We also include the time trend analysis of permanent BTD, total BTD and UTB from 2015 to 2019 in Figure 1. All three dependent variables were increasing from 2015 to 2017 and decreasing from 2017 to 2019, supporting our prediction of lower tax avoidance after TCJA. Table 3 presents the Pearson correlation matrix. It reports the correlations between major variables. It shows that book-tax differences, including both permanent and total book-tax differences, are negatively correlated with TCJA, while unrecognized tax benefits are positively correlated with TCJA. However, the results are not significant. It also shows that permanent book-tax differences and total book-tax differences are highly correlated with each other. Surprisingly, unrecognized tax benefits are negatively correlated with both book-tax difference measures. 4.2. Primary results Table 4 documents the regression analysis of the impact of TCJA on overall tax avoidance measured by unrecognized tax benefits, and tax base avoidance measured by permanent book-tax differences and total book-tax differences, specified by Hypotheses 1 and 2. We removed all loss firms in Table 4 when we ran the regression model. It reports that, consistent with Hypotheses 1 and 2, the coefficients on TCJA are negative and statistically significant for all three tax avoidance measures, suggesting that firms engage in less tax avoidance activities after the implementation of TCJA. In addition, Table 4 demonstrates that the coefficient on INV is significant and negative, showing that firms with inventory intensity avoid less taxes. The coefficients on FOR, GW, and R&D are negative for both book-tax difference measures but positive for UTB, which provides an inclusive result about the effect of foreign income, goodwill, and R&D on tax avoidance. Finally, INT is negatively associated with total TBTD and UTB, implying that firms with more intangible assets are less likely to avoid taxes. 4.3. Robustness Tests ETRs as Alternative Measures

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

Tax avoidance activities cannot be directly observed and we use a number of proxies to measure these activities including total and permanent book-tax difference and current unrecognized tax benefits. Following prior literature, we also include effective tax rate (ETR) as a proxy for tax avoidance in this robustness test. We measure tax avoidance as the difference between the statutory tax rate and annual GAAP or cash ETR. The lower the annual ETR relative to the statutory tax rate, the higher the level of tax avoidance engagement. The un-tabulated results are not qualitatively different from our main results, suggesting that firms avoid less tax after the implementation of TCJA.

benefits. Moreover, this study further isolates the effect of TCJA on tax base avoidance and found that larger, more profitable and/or leveraged firms were less likely to engage in tax base avoidance activities after TCJA.

Transition Year as Additional Indictor Variable Similar to Dyreng et al. (2020), we added Transition Year as another indicator variable, equal to 1 for 2017, and 0 otherwise, and rerun the main test. The un-tabulated results show that Transition Year is not significant for PBTD, TBTD, and UTB, suggesting that this additional indicator variable might not capture the substantial effect of TCJA on tax avoidance. Interaction Terms of TCJA with Main Control Variables In addition to adopting TCJA as a standalone indicator variable, we also add the interaction terms of TCJA with main control variables such as SIZE, ROA and LEV to reflect the impact of TCJA on these key variables. The un-tabulated results show that TCJA * ROA is significantly negative for TBTD, TCJA * SIZE and TCJA * LEV are significantly negative for PBTD, and TCJA * LEV is significantly positive for UTB, suggesting that larger, more profitable and/or leveraged firms are less likely to engage in tax base avoidance activities after TCJA. Using Current Year Addition in UTB as a Dependent Variable In the main analysis, we use current year UTB as our dependent variable. In order to investigate the effect of TCJA on incremental UTB, we also utilize current year addition in UTB (ΔUTB) as a dependent variable in our robustness test. The un-tabulated results show that the TCJA did not significantly affect ΔUTB, suggesting that the TCJA did not trigger a timely change in firms’ tax positions. 5. CONCLUSION This study’s aim was to examine whether firms changed their tax avoidance practices after the Tax Cuts and Jobs Act (TCJA) was enacted. Using listed companies for the time period around the implementation of TCJA, this study found that firms engaged in less tax avoidance activities after TCJA, as evidenced by (1) a lower permanent book-tax difference; (2) a lower total book-tax difference; and (3) lower levels of current unrecognized tax

This study has two implications for policy makers, researchers and practitioners: (1) it provides evidence on the impact of a given tax policy on tax avoidance and how firms behave differently (micro level) in connection with the overall economic conditions (macro level). Policy makers might benefit from this study and gain some insights into how to encourage firms to behave in certain way leading to the achievement of policy goals; (2) it also draws researchers’ and practitioners’ attention to tax base avoidance for the case where the statutory tax rate has been substantially reduced. Dyreng et al. (2020) document that the TCJA affects mostly domestic activities while foreign income remains unchanged. By shifting the focus away from tax rate avoidance (e.g., multinational firms), future tax avoidance research is encouraged to investigate more tax base avoidance activities such as non-conforming tax avoidance (i.e., booktax difference) and conforming tax avoidance (i.e., reducing both book income and taxable income). Appendix A: Variable definitions PBTD Permanent book-tax difference, measured as pre-tax earnings net of estimated taxable income. Estimated taxable income is calculated as total tax expenses over statutory tax rate TBTD Total book-tax difference, measured as pre-tax earnings net of estimated taxable income. Estimated taxable income is calculated as current tax expenses over statutory tax rate UTB Unrecognized tax benefits at the end the year, deflated by total assets TCJA

An indicator variable, equal to 1 for 2018 and 2019, and 0 otherwise

SIZE

Firm size, measured as the log of total assets

ROA

Profitability, measured as net earnings (losses) over total assets

LEV

Leverage, measured as the sum of short-term and long-term debts over total assets

JIN AND ZENG

INV

Inventory intensity, the ratio inventory to total assets

PPE

Capital intensity, the ratio of net fixed assets over total assets

LOSS

Loss-carry-over over total assets

FOR

Pre-tax foreign income over total assets

Goodwill over total assets

R&D

R&D expenditures over total assets

INT

Intangible assets over total assets

BIG4

An indicator variable, equal to 1 if a firm is audited by the Big4, and 0 otherwise

DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

FIGURE 1.

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

Table 1. Sample Selection

Table 2. Descriptive Statistics

Observations from COMPUSTAT 2015–2019

31,441

Less Observations with less than $1 million total assets (1,148) Outliers with 1% of ROA, LOSS, and LEV (900) Full Sample Agriculture Mining Utility Construction Manufacture Wholesale Retail Transportation Information Financial Real Estate Tech Service Admin Service Education Health Entertainment Board & Lodge Others

29,393 56 1,234 1,075 339 8,741 649 815 605 2,248 9,008 2,196 645 452 93 370 166 397 304

VARIABLES

MEAN

MEDIAN

STDEV

1ST QUAR

3RD QUAR

UTB

0.005

0.025

0.002

PBTD

-0.102

-0.001

0.323

-0.041

0.006

TBTD

-0.097

-0.001

0.313

-0.052

0.014

CTP

3.577

5.312

4.051

7.053

SIZE (million $)

14,614

1,068

116,716

209.1

4,403

ROA

-0.075

0.004

0.312

-0.028

0.037

LEV

0.178

0.032

0.237

0.322

INV

0.060

0.002

0.118

0.068

PPE

0.174

0.046

0.250

0.011

0.225

LOSS

0.684

2.172

0.219

FOR

0.001

0.062

INT

0.136

0.024

0.208

0.198

0.081

0.008

0.137

0.106

R&D

0.054

0.156

0.019

DICHOTOMOUS VARIABLES

TCJA

18,263 (62.5%)

11,030 (37.5%)

AUD

6,464 (22%)

22,929 (78%)

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DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

Table 3. Pearson Correlation Matrix

Table 4. Primary Testing Results for Hypotheses 1 and 2

UTB

PBTD

TBTD

PBTD

-0.139

TBTD

-0.132

0.963

TCJA

0.002

-0.002

SIZE

0.080***

0.472

0.484

TCJA

SIZE

LEV

ROA

INV

INT

R&D

PPE

0.043***

-0.021

-0.027

0.026***

0.082***

ROA

0.115***

0.971

0.960

0.007

0.458

0.010*

0.054***

0.070***

0.076***

0.008

0.119***

0.246

0.093***

INV

0.027***

0.017***

0.000

0.014**

0.148***

0.096***

0.040***

0.004

INT

0.048***

0.001

0.010*

0.006

0.050***

0.286

0.022***

0.878

0.205***

-0.667

-0.655

0.003

0.372

0.042***

0.654

0.060***

0.055***

0.039***

PPE

0.038***

0.027***

0.024***

0.008

0.052***

0.249

0.044***

0.076***

0.038***

0.094***

0.118***

LOSS

0.218

-0.574

-0.573

0.012**

0.440

0.005

0.569

0.066***

0.024***

0.019***

0.498

0.039***

FOR

0.009

0.229

0.208***

0.011*

0.132***

0.041***

0.241

0.087***

0.058***

0.052***

0.149***

0.009

-0.028

-0.031

Model: UTB it = α 0+α 1 TCJA it + δY it + Industry dummies + ε it BTD it = α 0+α 1 TCJA it + δY it + Industry dummies + ε it Variables

PBTD

TBTD

UTB

Intercept

-0.043***

-0.033***

-0.011***

(-5.39)

(-5.13)

(-5.40)

TCJA

-0.008***

-0.006***

-0.001*

(-7.03)

(-6.78)

(-1.78)

SIZE

0.005***

0.003***

0.001***

(6.80)

(5.28)

(7.78)

ROA

0.944***

0.92***

0.007***

(>10)

(10.22)

LEV

-0.001

-0.003

0.004***

(-0.50)

(-1.26)

(5.23)

INV

-0.052***

0.02***

-0.003**

(-8.32)

(-4.00)

(-2.02) -0.007***

0.005

R&D

0.040***

FOR

0.048***

LEV

BIG4

LOSS

0.005

0.150***

0.112***

0.018***

0.105***

0.016***

0.102***

0.043***

0.094***

PPE

0.112***

0.003

0.029***

LOSS

*** significant at 0.01 level; ** significant at 0.05 level, * significant at 0.1 level

FOR

INT

R&D

BIG4

-0.001

0.003

(-0.30)

(0.87)

(-7.23)

-0.000

0.002***

(-0.33)

(-0.87)

(>10)

-0.080***

-0.038***

0.007***

(-7.57)

(-4.75)

(2.92)

0.007

-0.01**

-0.009***

(1.12)

(-2.14)

(-6.15)

-0.020**

-0.013*

0.011***

(-2.17)

(-1.81)

(4.98)

-0.027***

-0.07***

0.021***

(-5.09)

(>10)

-0.001

0.001*

(-0.62)

(-1.16)

(1.94)

Industry

Yes

obs

24,566

26,569

29,393

adj R-sq

0.93

0.95

0.08

*** significant at 0.01 level; ** significant at 0.05 level, * significant at 0.1 level based on two-tailed t-test

JIN AND ZENG

DID FIRMS CHANGE THEIR TAX AVOIDANCE PRACTICES AFTER THE TAX CUTS AND JOBS ACT OF 2017?

Notes:

30. Hanlon, M. and Heitzman, S. (2010) A review of tax research, Journal of Accounting and Economics, 50(2-3), pp. 127-178. 31. Chen, S., Chen, X., Cheng, Q. and Shevlin, T. (2010) Are family firms more tax aggressiveness than non-family firms? Journal of Financial Economics, 95(1), pp. 41-61. 32. Rego, S. O. and Wilson, R. (2012) Equity risk incentives and corporate tax aggressiveness, Journal of Accounting Research, 50(3), pp. 775-810. 33. Badertscher, B., Katz, S. P. and Rego, S. O. (2013) The separation of ownership and control and tax avoidance, Journal of Accounting and Economics, 56(2-3), pp. 228-250. 34. Lisowsky, P., Robinson, L. A., and Schmidt, A. P. 2013. Do publicly disclosed tax reserves tell us about privately disclosed tax shelter activity? Journal of Accounting Research 51(3), 583–629. 35. Gupta, S., Mills, L. F., & Towery, E. M. 2014. The effect of mandatory financial statement disclosures of tax uncertainty on tax reporting and collections: The case of FIN 48 and multistate tax avoidance. The Journal of the American Taxation Association 36(2), 203–229. 36. Dyreng, Scott D., Michelle Hanlon, Edward L. Maydew. 2019. When does tax avoidance result in tax uncertainty? The Accounting Review 94(2), 179-203. 37. Dryeng, S.D., Geartner, F. B., Hoopes, J. L., and Vernon, M. E. 2020. The effect of U.S. tax reform on the tax burdens of U.S. domestic and multinational corporations. Available at SSRN: https://ssrn.com/abstract=3620102 or http://dx.doi.org/10.2139/ssrn.3620102. 38. Henry, E. and Sansing, R. (2020). Corporate Tax Preferences Before and After the Tax Cuts and Jobs Act of 2017. National Tax Journal, December 2020, 73 (4), 1065–1086 39. Clausing, K. A. (2020). Profit Shifting Before and After the Tax Cuts and Jobs Act. National Tax Journal, December 2020, 73 (4), 1233–1266. 40. Ibid. 41. Drake, K. D., Hamilton, R., and Lusch, S. J. (2020). Are declining effective tax rates indicative of tax avoidance? Insight from effective tax rate reconciliations. Journal of Accounting and Economics 70. 42. Schwab, C. M., Stomberg, B., and Xia, J. (2022). What Determines Effective Tax Rates? The Relative Influence of Tax and Other Factors. Contemporary Accounting Research Vol. 39 No. 1 (Spring 2022) pp. 459–497. 43. Edwards, A., Kubata, A., and Shevlin, T. (2021). The Decreasing Trend in U.S. Cash Effective Tax Rates: The Role of Growth in Pre-Tax Income. The Accounting Review 96(5): 231–261. 44. Comprix, J., Graham, R. C. and Moore, J. A. 2011. Empirical evidence on the impact of book-tax difference on divergence of opinion among investors. Journal of American Taxation Association 33(1), 51-78. 45. Hanlon, M. and Heitzman, S. (2010) A review of tax research, Journal of Accounting and Economics, 50(2-3), pp. 127-178. 46. Mills, L.F. 1998. Book-tax differences and Internal Revenue Service adjustments. Journal of Accounting Research 36(2), 343-356. 47. Desai, M. 2003. The divergence between book income and tax income. NBER/Tax Policy and the Economy 17, 169-206. 48. Frank, M.M., Lynch, L. and Rego, S.O. 2009. Tax reporting aggressiveness and its relation to aggressive financial reporting. The Accounting Review 84(2), 467-496. 49. Lisowsky, P. 2010. Seeking shelter: Empirically modeling tax shelters using financial statement information. The Accounting Review 85(5), 1693–1720. 50. Wilson, R. J. 2009. An examination of corporate tax shelter participants. The Accounting Review 84(3), 969-999. 51. Hanlon, M. and Heitzman, S. (2010) A review of tax research, Journal of Accounting and Economics, 50(2-3), pp. 127-178. 52. Wilde, J. H. and R.J. Wilson, 2018. Perspectives on Corporate Tax Avoidance: Observations from the Past Decade. The Journal of the American Taxation Association 40(2), 63-81. 53. Kim, F. and Zhang, L. (2016) Corporate political connections and tax aggressiveness, Contemporary Accounting Research, 33(1), pp. 78-114. 54. Dyreng, S. D., Hanlon, M. and Maydew, E. L. (2008) Long-run corporate tax avoidance, Accounting Review, 83(1), pp. 61-82. 55. Lennox, C., Lisowsky, P. and Pittman, J. (2013) Tax aggressiveness and accounting fraud, Journal of Accounting Research, 51(4), pp. 739-779. 56. Stickney, C. P. and McGee, V. E. (1982) Effective corporate tax rates, the effect of size, capital intensity, leverage, and other factors, Journal of Accounting and Public Policy, 1(1), pp. 125-152. 57. Porcano, T. M. (1986) Corporate tax rates: Progressive, proportional or regressive, Journal of the American Taxation Association, 7(1), pp. 17-31. 58. Zeng, T. 2019. Country-level Governance, Accounting Standards, and Tax Avoidance: A Cross-country Study, Asian Review of Accounting, 27(3), 401-424. 59. Chen, S., Chen, X., Cheng, Q. and Shevlin, T. (2010) Are family firms more tax aggressiveness than non-family firms? Journal of Financial Economics, 95(1), pp. 41-61. 60. Higgins, D., Omer, T. C. and Phillips, J. D. (2015) The influence of a firm’s business strategy on its tax aggressiveness, Contemporary Accounting Research, 32(2), pp. 674-702. 61. Klassen, K., Lisowsky, P. and Mescall, D. (2016) The role of auditors, non-auditors, and internal tax departments in corporate tax aggressiveness, The Accounting Review, 91(1), pp. 179-205. 62. Kanagaretnam, K., Lee, J., Lim, C. Y., & Lobo, G. J. 2016. Relation between auditor quality and tax aggressiveness: Implications of cross-country institutional differences. AUDITING: A Journal of Practice & Theory, 35(4), 105–135.

1. Auerbach, A.J. 2018. Measuring the effects of corporate tax cuts. Journal of Economic Perspectives 32(4), 97120. 2. Wagner, A. F., Zeckhauser, R. J., Ziegler, A. 2018. Unequal rewards to firms: Stock market responses to the Trump election and the 2017 corporate tax reform. American Economic Association Papers and Proceedings 108, 590-596 3. Kalcheva, I., Plečnik, J. M., Tran, H., Turkiela, J. 2019. (Un)intended Consequences? The Impact of the 2017 Tax Cuts and Jobs Act on Shareholder Wealth. Journal of Banking & Finance 118. 4. Bennett, B., Thakor, A. V. and Wang, Z. 2019. Stock Repurchases and the 2017 Tax Cuts and Jobs Act. Working paper, Available at SSRN: https://ssrn.com/abstract=3443656 or http://dx.doi.org/10.2139/ssrn.3443656 5. Edwards, A. and Hutchens, M. 2020. Taxes and IPO Pricing: Evidence from U.S. tax reform. Working paper. 6. Gaertner, F. B., Hoopes, J. L., Williams, B. 2020. Making Only America Great? Non-US Market Reactions to US Tax Reform. Management Science 66, 687-697. 7. Dryeng, S.D., Geartner, F. B., Hoopes, J. L., and Vernon, M. E. 2020. The effect of U.S. tax reform on the tax burdens of U.S. domestic and multinational corporations. Available at SSRN: https://ssrn.com/abstract=3620102 or http://dx.doi.org/10.2139/ssrn.3620102. 8. Wilde, J. H. and R.J. Wilson, 2018. Perspectives on Corporate Tax Avoidance: Observations from the Past Decade. The Journal of the American Taxation Association 40(2), 63-81. 9. Dryeng, S.D., Geartner, F. B., Hoopes, J. L., and Vernon, M. E. 2020. The effect of U.S. tax reform on the tax burdens of U.S. domestic and multinational corporations. Available at SSRN: https://ssrn.com/abstract=3620102 or http://dx.doi.org/10.2139/ssrn.3620102. 10. Wilde, J. H. and R.J. Wilson, 2018. Perspectives on Corporate Tax Avoidance: Observations from the Past Decade. The Journal of the American Taxation Association 40(2), 63-81. 11. Lamoenius, N., Shevlin, T., and Stenzel, A. (2021). Measuring corporate tax rate and tax base avoidance of U.S. Domestic and U.S. multinational firms. Journal of Accounting and Economics 72. 12. Dyreng, Scott D., Michelle Hanlon, Edward L. Maydew, and Jacob R. Thornock. 2017. Changes in Corporate Effective Tax Rates over the Past 25 Years. Journal of Financial Economics 124(3), 441-463. 13. Gaertner, F. B., Laplante, S. K., and Lynch, D. P. 2016. Trends in the sources of permanent and temporary booktax differences during the Schedule M-3 era. National Tax Journal 69(4), 785–808. 14. Wilde, J. H. and R.J. Wilson, 2018. Perspectives on Corporate Tax Avoidance: Observations from the Past Decade. The Journal of the American Taxation Association 40(2), 63-81. 15. Bruhne, A.L. and M. Jacob. 2019. Corporate tax avoidance and the real effects of taxation: A review. Available at SSRN: https://ssrn.com/abstract=3495496 or http://dx.doi.org/10.2139/ssrn.3495496 16. Atwood, T., Drake, M., Myers, J., & Myers, L. (2012). Home country tax system characteristics and corporate tax avoidance: International evidence. The Accounting Review, 87(6), 1831–1860. 17. Kanagaretnam, K., Lee, J., Lim, C. Y., & Lobo, G. J. 2016. Relation between auditor quality and tax aggressiveness: Implications of cross-country institutional differences. AUDITING: A Journal of Practice & Theory, 35(4), 105–135. 18. Kerr, J. N. 2019. Transparency, information shock, and tax avoidance. Contemporary Accounting Review, 36(2), 1146-1183. 19. Source: Corporate income tax revenues and forecast in the United States from 2000 to 2030 (in billion U.S. dollars) available at https://www.statista.com/statistics/217509/revenues-from-corporate-income-tax-and-forecast-inthe-us/ 20. Klassen, K. and Laplante, S.K. 2012. The effect of foreign investment and financial reporting incentives on crossjurisdictional income shifting. Contemporary Accounting Research 29(3), 928-955. 21. De Simone, L., Mills, L.F. and Stomberg, B. 2019. Using IRS date to identify income shifting to foreign affiliates. Review of Accounting Studies 24(2), 694-730. 22. Beer, S., Mooij, R. and Liu, L. 2018. International corporate avoidance: A review of the channels, effect sizes, and blind spots. IMF working paper, WP/18/168. 23. Atwood, T., Drake, M., Myers, J., & Myers, L. (2012). Home country tax system characteristics and corporate tax avoidance: International evidence. The Accounting Review, 87(6), 1831–1860. 24. Kanagaretnam, K., Lee, J., Lim, C. Y., & Lobo, G. J. 2016. Relation between auditor quality and tax aggressiveness: Implications of cross-country institutional differences. AUDITING: A Journal of Practice & Theory, 35(4), 105–135. 25. Zeng, T. 2019. Country-level Governance, Accounting Standards, and Tax Avoidance: A Cross-country Study, Asian Review of Accounting, 27(3), 401-424. 26. Bruhne, A.L. and M. Jacob. 2019. Corporate tax avoidance and the real effects of taxation: A review. Available at SSRN: https://ssrn.com/abstract=3495496 or http://dx.doi.org/10.2139/ssrn.3495496 27. Lamoenius, N., Shevlin, T., and Stenzel, A. (2021). Measuring corporate tax rate and tax base avoidance of U.S. Domestic and U.S. multinational firms. Journal of Accounting and Economics 72. 28. Hanlon, M. and Heitzman, S. (2010) A review of tax research, Journal of Accounting and Economics, 50(2-3), pp. 127-178. 29. Desai, M. and Dharmapala, D. (2006) Corporate tax avoidance and high-powered incentives, Journal of Financial Economics, 79(1), pp. 145-179.

Erasing Loneliness Among Black Executives Through Self-Leadership and Super-Leadership by Samuel Akin, Ph.D. Of the chief executives reported by the U.S. Bureau of Labor Statistics (2023), only 6% are Black. 1 The lived experience of the Black C-suite executives is discomfort within the workplace, ultimately producing Black Silence (workplace disengagement). As the leader is, so is the follower: Black employees have a 12 times better perception of their organization’s integrity when their leader is Black. There is an opportunity for organizations to increase Black C-suite executives’ representation through Black executives erasing loneliness through self-leadership and super-leadership.

Samuel Akin, Ph.D. is an instructor at Howard University School of Business Accounting Department with Ph.D. in Organizational Leadership. LONELINESS among Black executives affects the executive and the organization alike. Although there are few Black executives, an incredible impact is still possible. Much of the remedies offered through research and think-pieces address the systemic problem of hiring practices. However, this article sought out the academic literature that forwarded self-leadership and super-leadership as the solution to the problem. The recommendations provided suggest that Black executives (1) shift the paradigm of their position to that of a change agent who is blazing a trail for many to follow. Adopting this mindset also embraces loneliness in the short term to build a better tomorrow. (2) Creating a pipeline for other Black executives through mentoring differently. INTRODUCTION There is a lack of Black representation in the workforce. At the same time, there is hope for increased employment through diversity, equity, and inclusion (DEI) efforts by various companies (Fortune 500, mediumsize, and small businesses). Companies are taking their commitment to corporate social responsibility (CSR) more seriously and finding ways

AKIN

ERASING LONELINESS AMONG BLACK EXECUTIVES THROUGH SELF-LEADERSHIP AND SUPER-LEADERSHIP

to create a more equitable job market for Black employees. Although the progress is slow, it is undeniable that equity in the workplace is a significant focus of many companies. However, there seems to be little attention to executives’ lack of Black representation. According to the U.S. Bureau of Labor Statistics (2023), of the 20,199 chief executives, only 1,192 (5.9%) are Black. 2 The lack of representation within the leadership of companies is a problem.

self-determination, and purpose. 9, 10 In other words, self-leadership is motivating oneself to achieve a particular goal. Self-leadership theory offers strategies to increase work performance or personal effectiveness. There are three categories of self-leadership strategies: behavioral, rewards, and cognition. 11, 12 The behavioral strategy utilizes selfintrospection, self-goal setting, and self-reward to identify inefficiencies and barriers to leading effectively. 13 By slowing down and taking inventory of behaviors, the leader will be able to identify undesirable behavior and positive behavior. As the leader avoids negative behavior, self-condemnation being one, the leader reinforces the positive behavior. Neck and Houghton 14 noted external stimuli such as positive quotes, motivational pictures, and reflection journal entries as tools to encourage positive behavior.

A Gallup poll in 2023 revealed that only 22% of employees (all races) would strongly agree that they trust their organization’s leadership. 3 Additionally, the poll revealed that only one out of 12 employees would engage with the organization despite distrust. While those are the perceptions of employees, Cortes 4 reported that 21% of employees observed no Black representation in leadership. The repercussions of the underrepresentation of Black executives are that Black employees view their organizations as (1) lacking equity among employees and (2) lacking convictions on integrity and ethics. 5 Moreover, Lloyd unearthed that Black employees have a 12 times better perception of their organization’s integrity when their leader is Black. If surveys and interviews expose the distrust among Black employees, what about the lack of social networking opportunities among Black c-suite executives? Harvard Business Review 6 noted that Black executives are inauthentic to themselves at work to better assimilate into the company’s culture. The need to present differently than their authentic self stems from the fear of being judged or otherized. Black executives would employ tactics such as code-switching and hiding their personal lives from other executives to appear more relatable. Such behaviors could lead to Black Silence (workplace disengagement), which could lead to loneliness and isolation. 7 Gulati’s research on Black C-suite executives revealed that their disconnection from others has created imposter syndrome, leading to a lack of confidence, anger, stress, and fear—ultimately diminishing their work performance. 8 In other words, Black executives’ could maximize their work performance if their work environment had more diversity and representation on the C-suite level. It is evident through research that the lack of Black executives is costly, as pointed out above. Although there are few Black executives, an incredible impact is still possible. Change can happen with what we have rather than wishing for more. ACADEMIC INSIGHT THAT ADDRESSES THE PROBLEM Self-leadership is the ability to influence oneself to develop competence,

Rewards are strategies of self-leadership that provide an enjoyable experience for the leaders as they work on behavioral strategies. Part of what makes self-leadership possible is self-motivation, which comes through rewards. Adapting and editing one’s behavior is both challenging and unpleasurable. Establishing a reward system encourages the continuation of self-leadership until the results are evident. Neck and Houghton 15 provided two ways to reward oneself: (1) strategically and constantly implementing rewards based on certain parts of the task to the point where the task itself becomes enjoyable, and (2) reframing negative perspectives and refocusing on positive expectations. Subsequently, positive feelings will be associated with the task at hand. The cognitive strategies focus on the leader’s thought life. Negative thoughts are demotivators that discourage leaders from making progress in their specific endeavor. The cognitive strategy of self-leadership slows down leaders to the point where they scrutinize every thought and categorize them as helpful or unhelpful. Neck and Houghton 16 pointed out that identifying unhelpful thoughts is not good enough, but the leader must replace them with positive, uplifting thoughts. Another cognitive strategy provided by self-leadership is the utilization of selftalk to influence oneself. Alongside self-talk is mental imagery. Mental imagery is the process of visualizing oneself completing a task or being successful in an endeavor. Those who utilize mental imagery are more likely to succeed at that very task (Neck & Houghton, 2006). As leaders envision progress, their behavior aligns with the vision. Self-leadership also promises increased self-efficacy. 17 Self-efficacy is someone’s belief in their skills to acquire the necessary knowledge or action to perform on a specific level. 18 As leaders grow in self-efficacy, so do their resilience, creativity, and problem-solving - ultimately increasing the likelihood of

AKIN

ERASING LONELINESS AMONG BLACK EXECUTIVES THROUGH SELF-LEADERSHIP AND SUPER-LEADERSHIP

attaining the goal.

sharing their authentic selves with others, it only robs the organization of the ability to hire another version of a Black executive. Another way of conceptualizing the former point is that something about the established Black executives’, outside of their skills, positioned them to become an executive. Hiding their true selves only limits the organization from promoting or hiring a younger Black employee that may have similar qualities to that of the Black executives. Choosing to share authentically will train the Human Resource director, which trains their recruiter to watch out for executive qualities like the already established Black executives.

There is also Super-leadership which is the ability to lead oneself and replicate the same self-leadership process/strategies within another. 19 Consequentially speaking, if the leaders lead themselves effectively, it will influence those under them to be self-learners. Super-leadership aims to develop employees to a point where they can develop independently and without the leader’s help. There are seven strategies under the superleadership theory: (1) being a self-leader, (2) becoming an exemplar of self-leadership by modeling before employees, (3) encouraging employees to establish self-goals, (4) providing rewards and constructive feedback that encourages followers to continue in self-leadership, (5) engage in destruction and reconstruction of thoughts of employees and empower self-efficacy, (6) delegate work and empower decision making of employees, and (7) create a culture for self-leadership among employees. The goal of employing super-leadership is to encourage employees to self-lead, an investment that yields long-term benefits. Employing superleadership puts the leader in a mentorship role, which forms a deeper and meaningful relationship between employee and leader. The problem is that Black executives are lonely because of the lack of representation- which fuels a more significant problem of Black employees’ perception of their organizations as untrustworthy or unethical. However, that is a systemic problem that one article cannot solve. This article, however, recommends to Black executives a new perspective and strategies to act upon. RECOMMENDATIONS Black executives are trailblazers and must start to see themselves as such. They are a symbol of underrepresentation of Black employees. Being a trailblazer comes with the burden of being first. Black C-suite executives are first at the table, first in making decisions and influencing the deals. Providing leadership in organizations that have global reach, or leadership over diverse groups of people is not common in Black and African Americans’ community. Many Black executives are passing through territory that, as a people group, have scarcely engaged in. This article recommends owning the burden and responsibility of being a trailblazer, knowing that just continuing makes a significant difference. The quicker acceptance happens, the faster progress will occur in erasing loneliness. More to the point, the uniqueness of being a Black executive adds color (pun intended) to the workplace. While Black executives refrain from

Shifting one’s mindset is the first part of the battle; there must be a change in approach. This article is recommending spending time developing personal self-leadership skills that lead to greater self-efficacy. Accepting that being a Black executive means being a change agent and having the patience to build towards a better tomorrow, will require selfleadership. Black executives must develop specific behavioral, rewards, and cognition strategies. As one develops specific strategies, ask the following questions: (1) What behavioral traits can be changed to attract collaboration rather than division? (2) What rewards will generate motivation to continue the process of self-leadership? (3) Of the many thoughts, which are helpful and unhelpful? Going through these questions provides a consciousness that slows down the leader enough to take inventory of the perception of their work and the impact on their organization. The last recommendation is creating a pipeline for other Black executives to emerge. Mentorship provides dual benefits between the mentor and mentee. For the mentee, it provides a safe place to grow and develop in their career. For mentors, it creates a community that mitigates loneliness. While mentorship already occurs within companies, think about mentoring differently. • The first way of mentoring differently is utilizing superleadership - developing mentees to become self-learners. Typical mentorship in the workplace is either out of obligations per the organization’s system or for the mentor’s self-importance. Focus on developing the mentee to become independent of the mentor. Giving the mentees the tools of self-leadership creates selfefficacy to aspire to become an executive. • The second way of mentoring differently is the selection process.

AKIN

ERASING LONELINESS AMONG BLACK EXECUTIVES THROUGH SELF-LEADERSHIP AND SUPER-LEADERSHIP

The reality is that some mentors choose their mentees based on their potential. Having selective and preferential treatment will limit the number of Black executives that could emerge. This pipeline initiative creates meaningful work and a community of Black executives, thus erasing loneliness. Loneliness is a problem among Black executives. Much of the remedies offered through research and think-pieces address the systemic problem of hiring practices. This article sought to give Black executives more control in improving their lived experience by providing practical recommendations.

Notes: 1. U.S. Bureau of Labor Statistics. (2023, June). CPS Home. U.S. Bureau of Labor Statistics. https://www.bls.gov/ cps/ 2. Ibid. 3. Robison, V. R. & J. (2023, July 21). Trust is in decline: Here’s how to rebuild it. Gallup.com. https://www.gallup. com/workplace/393401/trust-decline-rebuild.aspx 4. Cortes, G. (2023, June 20). Black executives see progress since George Floyd, but much work still to be done: CNBC survey. CNBC. https://www.cnbc.com/2023/06/19/black-leaders-rate-corporate-americas-dei-progress-sincegeorge-floyd.html 5. Lloyd, C. (2022, July 21). The culture costs of no black leaders. Gallup.com. https://news.gallup.com/opinion/ gallup/329588/culture-costs-no-black-leaders.aspx 6. Harvard Business Review. (2021, November 30). What do black executives really want? https://hbr.org/2021/11/ what-do-black-executives-really-want 7. Lloyd, C. (2022, July 21). The culture costs of no black leaders. Gallup.com. https://news.gallup.com/opinion/ gallup/329588/culture-costs-no-black-leaders.aspx 8. Gulati, R. (2022, April 4). What black executives really want: New research insights - executive education harvard business school. HBS Executive Education. https://www.exed.hbs.edu/blog/post/what-black-executivesreally-want 9. Neck, C.P., Manz, C.C. and Houghton, J.D. (2020), Self-Leadership: The Definitive Guide to Personal Excellence (2e), SAGE Publications, Thousand Oaks, CA. 10. Knotts, K. G., & Houghton, J. D. (2021). You can’t make me! The role of self-leadership in enhancing organizational commitment and work engagement. [Self-leadership, commitment and engagement] Leadership & Organization Development Journal, 42(5), 748-762. https://doi.org/10.1108/LODJ-10-2020-0436 11. Neck, C. P., & Houghton, J. D. (2006). Two decades of self-leadership theory and research. Journal of Managerial Psychology, 21(4), 270–295. https://doi.org/10.1108/02683940610663097 12. Knotts, K. G., & Houghton, J. D. (2021). You can’t make me! The role of self-leadership in enhancing organizational commitment and work engagement. [Self-leadership, commitment and engagement] Leadership & Organization Development Journal, 42(5), 748-762. https://doi.org/10.1108/LODJ-10-2020-0436 13. Ibid. 14. Neck, C. P., & Houghton, J. D. (2006). Two decades of self-leadership theory and research. Journal of Managerial Psychology, 21(4), 270–295. https://doi.org/10.1108/02683940610663097 15. Ibid. 16. Ibid. 17. Knotts, K. G., & Houghton, J. D. (2021). You can’t make me! The role of self-leadership in enhancing organizational commitment and work engagement. [Self-leadership, commitment and engagement] Leadership & Organization Development Journal, 42(5), 748-762. https://doi.org/10.1108/LODJ-10-2020-0436 18. Schunk, D. H., & DiBenedetto, M. K. (2021). Self-efficacy and human motivation. Advances in Motivation Science, 153–179. https://doi.org/10.1016/bs.adms.2020.10.001 19. Müller, G. F., Georgianna, S., Schermelleh-Engel, K., Roth, A. C., Schreiber, W. A., Sauerland, M., Muessigmann, M. J., & Jilg, F. (2013). Super-leadership and work enjoyment: Direct and moderated influences. Psychological Reports, 113(3), 804–821. https://doi.org/10.2466/01.14.pr0.113x32z0

The ROI of Rapport: 4 Steps to Build Your Confidence and Career Trajectory without Telling Everyone All of Your Personal Business by Shamis Pitts, MBA, CPC, PCC, SHRM-SCP Interpersonal relationships are central to how work gets done, how we feel about the work as we are doing it, and whether or not our ideal outcomes are achieved. In an era where we are told to show up and be our authentic selves, it may be challenging to figure out what, when and how to share who we are. And saying nothing will not serve us in the long run. This article shares an approach to confidently building rapport at work in a way that is authentic for you to build career success.

Shamis Pitts is the founder and owner of Pitts Leadership Consulting LLC, a company specializing in organizational health and employee wellbeing. WHEN WE ENTER THE WORLD of work, so much of the conversation is about our effectiveness at developing skill and technical expertise in a particular subject matter. As we build and grow our careers, our ability to be adept at leading other people becomes the greater accelerator or blocker to advancement. That’s why building capability in how to create, grow and sustain interpersonal relationships is important early in your career so you have more runway to learn and grow. That said, if you are mid-career and interpersonal relationship development has not been an area of focus for you or if you have shied away from it due to past negative experiences, let’s focus on the opportunity to move forward. Getting to know people within your organization, and enabling them to get to know you, will positively impact your career trajectory. Let’s explore how to intentionally create rapport in the workplace.

THE ROI OF RAPPORT: 4 STEPS TO BUILD YOUR CONFIDENCE AND CAREER TRAJECTORY WITHOUT TELLING EVERYONE ALL OF YOUR PERSONAL BUSINESS

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What is Rapport? Rapport represents a mash-up of the components of interpersonal emotional intelligence - social skills and empathy. According to the Merriam-Webster dictionary, rapport is defined as “a friendly, harmonious relationship; especially: a relationship characterized by agreement, mutual understanding, or empathy that makes communication possible or easy.” Rapport is, by definition, positive. It also requires two people. It is important to acknowledge that the degree of agreement and mutuality upon which a relationship exists is subjective because it involves two unique individuals who have their own worldviews.

When viewed holistically, the aforementioned benefits contribute to employee engagement, which I am defining as psychological meaningfulness, psychological safety and psychological availability 2. ● Availability anchors on whether you have clarity of and access to job expectations, resources and support from your manager and colleagues to get work done. ● Safety speaks to whether you feel free to be your authentic self without fear or worry. ● Availability refers to whether you have the capacity to engage, which gets to the heart of physical and mental wellbeing.

BENEFITS AND CHALLENGES

When each component is present, you are more likely to stay at an organization and do your best work. And we know that can be easier said than done, especially with respect to psychological safety.

What are the benefits of rapport in the workplace? The benefits of rapport in the workplace have been studied for decades. In 2011, Reich and Hershcovis analyzed previous academic research and compiled the benefits of both individual and organizational interpersonal relationships. Those findings 1 are as follows:

INDIVIDUAL BENEFITS ORGANIZATIONAL BENEFITS

Formal Relationships

Informal Relationships

Reduced stress levels

Reduced turnover

Decreased turnover

Increased feelings of belonging, inclusion, social significance, togetherness

Enhanced teamwork, communication, cooperation

Decreased in-group conflict

Increased salary, promotion, career mobility, recognition, rewards, opportunity to establish a base of power

Increased job satisfaction, job involvement, job performance, team cohesion, organizational commitment

Increased employee compliance, motivation, job satisfaction, group cohesion

Greater likelihood for others to follow the positive example demonstrated by leaders

Increased cardiovascular activity, immune system functioning, hormone patterns

Increased organizational commitment due to positive team performance (efficacy and efficiency)

What are the challenges to establishing rapport for Black/African Americans in the workplace? In a workplace study of Black/African American employees, data show that feeling rules, defined as “the norms and guidelines that govern emotion work or emotion management,” within corporate culture are not applied equally across racial groups. Standards for displaying likeability and congeniality (always and often) and when it is appropriate to feel irritation and anger (never) reinforce racial stereotyping and promote bias. Not surprisingly, Black workers being held to a different emotional standard than their White colleagues is taxing, emotional labor. 3 Establishing rapport requires psychological safety, and if your past or current experiences have included micro and macroaggressions, then it is understandable that you may feel uncomfortable or unwilling to invest in those conversations. It is important to acknowledge the past to help you understand how you want to move forward. Four Steps to Overcome Rapport Challenges Overall, developing rapport with another human requires being intentional - a thoughtful investment of your time and energy - in order to reap all of the benefits and avoid and/or mitigate the challenges. Going through the process outlined below will help you gain clarity and confidence around your commitment, which will be important to keep you focused in the days and weeks ahead. 1) Identify the Key Relationship, Ideal Outcome and Success Metric Determine the person with whom you want to establish or deepen rapport. We will call this person your “rapport target.” Is it your direct manager? Your manager’s manager? A member of a cross-functional

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team? Then ask yourself: • Why is this relationship important to me? • What would having rapport with this person enable me to accomplish? • If I were able to create or increase rapport with this person, how would I know that I was successful? This is starting with the end in mind. It is easier to make moves when you know where you are headed and why it matters. We often get bogged down in the daily grind. Figuring this out will help you stay the course. And we know that work demands, and requirements shift, information changes, and goals are redefined. Return to this exercise to clarify and reprioritize as needed. If you have no existing relationship, proceed to step 3. 2) Evaluate Your Existing Rapport and Analyze the Underlying Information Determine what your level of rapport is with your rapport target. Pick a number on a scale of 1 to 10, 10 being the highest. To norm the rating scale, 5 means that you are neutral or have no relationship, therefore the relationship is neither positive nor negative. A rating of 1 represents a negative relationship. Once you have your number, analyze what made you choose that number. If you have an existing relationship, look for data to support your selection. Think about actual experiences you had with the person, then get curious about what transpired. Look for patterns. For example: • When did those experiences occur? Early in the relationship? Recently? • How frequently do they occur? • What is the context when those experiences occur? o What are the discussion topics? o How comfortable are you with those topics? o What is a feeling word to describe your emotions during those conversations? o Who else is in the room? • What happens when you are in one-on-one conversations versus a group? • How do you characterize those interactions, positive, neutral or negative? o If you only have identified negative interactions, what positive or neutral interactions might you be missing?

THE ROI OF RAPPORT: 4 STEPS TO BUILD YOUR CONFIDENCE AND CAREER TRAJECTORY WITHOUT TELLING EVERYONE ALL OF YOUR PERSONAL BUSINESS

o What are your underlying assumptions that support those thoughts? • How does your relationship with your rapport target differ from your relationship with others having a similar level of positional authority? o Is your level of rapport similar, lower, or higher? o To what do you attribute those similarities or differences? The aforementioned questions are coaching questions designed to delve into the recesses of your brain. They are not exhaustive, only designed to start you down the path of self-exploration and clarity. You can complete this exercise on your own - write down thoughts on pen and paper or type it out - or talk it out with a trusted source. Your approach will depend on your learning style and how you like to structure your time. Being able to self-coach is a wonderful capability to develop and flex that gets easier with time. 3) Create and Implement Your Plan There are two parts to planning: • First, review your ideal outcomes and success metrics. Based on where you want to go, decide what actions would be required to move forward and/or close the gap. • Second, it is imperative that you decide what you are willing to share about yourself. We are always establishing our credibility with other people. However, I argue that folks do not actively think about what they are willing to share beyond the basics. When you prepare for a conversation, you are more confident. Make a list of what you are comfortable sharing and what you are not comfortable sharing. Then think about what lives in the gap between the two. Identify a few things in the gap. Meaning, to create rapport, you must go beyond the comfort zone with your rapport target and you can do that without telling them all of your personal business. I know at this point, you are likely very familiar with the phrase “know, like and trust.” Sharing job history alone isn’t sufficient to create rapport. People want to know YOU. As you build trust and feel greater safety with your rapport target, you may choose to share more about yourself. 4) Apply and Iterate Now you are prepared to apply your plan. Schedule your conversation. Before the conversation, practice saying out loud what you intend to share about yourself. You want to be confident in your delivery, therefore, you are putting yourself in a stronger position when you

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practice a few times. After the conversation, give yourself time to reflect on how you felt about the exchange. What worked? What didn’t work? If needed, talk through your thoughts with a trusted person to get an outside perspective. If this is a new way of being for you, be gracious with yourself. This is a learning experience. Rapport in Action To bring what I have shared from the theoretical into the practical, I would like to spotlight the insights of Kenya Howard, Risk Manager, Principal Associate. I had the opportunity to work with Kenya when I served as an executive coach for Capital One’s Catalyst, a talent development program designed to support the growth and retention of Black, Indigenous and People of Color mid-career associates. The following interview has been edited for brevity and clarity. What does having rapport enable you to do? How do you measure your success? Rapport enables me to connect the dots a lot quicker to get information and resolve problems. I measure my success in the quality of the results from having rapport with people. Over the last two years, building rapport and getting better at doing it has allowed me to have more visibility. I won’t say [it’s] the only reason, but [it’s] part of the reason why I’ve been able to have the reputation that I have. People can trust that when Kenya’s involved, it’s going to be done right…I deliver great work and results, and people see that, and now they come back and say, ‘She’s the one. She’s reliable.’ And it all goes back to how I interact with people. If I want people to be open and available to answer questions and help me, I have to return the same. Understanding how others operate, always keeping that communication door open for them to circle back with me and leverage me when they need me, and for me to leverage them when I need them. Quantifying it is all in the ‘circle back’. If they keep coming back because they can trust me because I’ve built that rapport, then that circle of trust just gets bigger and bigger. Then, the visibility, the acknowledgement, the rewards, the promotions, the spotlights, the recognition, all of that comes [because] I’ve put a lot of effort and energy into managing relationships, building connections, and establishing rapport. It’s part of the bigger picture. How have your views about the importance of building rapport changed over time? Building rapport is not just doing a meet and greet. It’s actually caring

THE ROI OF RAPPORT: 4 STEPS TO BUILD YOUR CONFIDENCE AND CAREER TRAJECTORY WITHOUT TELLING EVERYONE ALL OF YOUR PERSONAL BUSINESS

about what that person does. Learning about what someone does and why the work is important. You have to be more intentional about those networking connections. I wasn’t confident in myself when I would have rapport conversations. My initial mindset was, ‘I need you to do something for me.’ I would be timid when I go into the interactions, so I would get straight to the point. I wasn’t even trying to address you as a human being. I wouldn’t pick up on the social cues, like, this might not be the appropriate time to have this conversation. I was blind to all of that because it was only, ‘Solve my problem. Here’s my issue.’ Now, it’s a conversation. It’s more of a partnership versus me relying on somebody else to ‘do the thing’. I’ve had to change that perspective to, ‘Let me learn about this person.’ That mind shift was major because as an African-American woman in corporate, it’s always a struggle trying to get that visibility and make a name for myself. Understanding what it is that I am lacking or where I need to grow and develop will help me get to where I want to go. Now, my mindset is, ‘Let me tell you who I am…how I can help you solve some problems or make some different connections.’ I’m getting to know them as a person so I know how they think, what they like, what they don’t like, how they like to be approached, whether they’re even the right person for me to even ask the question or determining, ‘You’re not my problem solver, but I can bounce things off of you.’ Networking has become making true connections with people. I’ve been able to say, ‘I thought I was going to make a connection with you on something work-related and we actually connected on something personal.’ How do you decide what you were going to share about yourself? I have to see the person. I talk with my hands and body [and] I pick up on those cues from other people. [Conversations] virtually, off video, I really can’t tell how engaged that person is. I always start with my base [information] and feel how this person is reacting and responding to me. How open and engaged are they being? Are they being a little bit more closed off and answering me with ‘yes’ or ‘no’. Based on how they respond to my questions, I adjust my questions so that if I want more, I ask more open-ended questions to get them to give me more details. If we have a connection, we stay in contact with each other. Over the last year or two, it has served me very well. I’ve had many opportunities where people will come back and say, ‘Hey, I may not know, but Kenya

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might know.’ Or I’ve got a request and I phone a friend, and we lean on each other to get to where we need to go to find the right information. What helped you build the confidence to really lean into rapport conversations? What have you learned along the way? In our interactions, you’ve really helped me think through how I have conversations and tailoring how I deliver things and how I ask questions. What questions am I asking? Should I even be asking questions? Or is this an opportunity for me to sit back and listen and acknowledge? The other thing that I learned is that everything doesn’t always need my immediate response. I’ve been able to identify those opportunities to sit back but be verbal about the time I need to process. [If] I need a few moments, ask, ‘Can I circle back?’ [Otherwise], they might get a raw, unfiltered, response. I want to be transparent and honest with my audience. I have to have the rapport with people that I would want them to have with me.

THE ROI OF RAPPORT: 4 STEPS TO BUILD YOUR CONFIDENCE AND CAREER TRAJECTORY WITHOUT TELLING EVERYONE ALL OF YOUR PERSONAL BUSINESS

Notes: 1. Reich, T. and S. Hershcovis. (2011). Interpersonal relationships at work. In S. Zedeck, H. Aguinis, W. Cascio, M. Gelfand, K. Leung, S. Parker & J. Zhou (Eds.). Handbook of industrial and organizational psychology (Vol. 3) (pp.223-248). American Psychological Association. 2. Nishi, L. (2018). ILDR1001: Improving Engagement [Online lecture]. In Diversity and Inclusion. Cornell University. 3. Wingfield, A. H. (2010). Are Some Emotions Marked “Whites Only”? Racialized Feeling Rules in Professional Workplaces. Social Problems, Vol. 57, No. 2 (May 2010), pp. 251-268.

Rapport isn’t a one-time effort. It is always on. At times I’ve been like, man, I’ve lost touch with this person. I need to reach out. Life could have taken [us] into different directions, good, bad, or indifferent. I’m [not] reaching out because I need anything. I’m just reaching out to make sure you’re still in my network, keep[ing] tabs on those relationships. Who benefits most from building rapport? Everyone benefits from it. Everyone that I interact with, whether it’s the person that I’m having the conversation with, or if it’s my manager looking for the results, or the business customer that’s impacted. If I don’t [build rapport], then there’re gaps, holes and miscommunication, and nobody knows who to trust. When people know that they can rely on you for accuracy, for information or just overall guidance, or just [know] that you will be transparent and honest, everybody wins. CONCLUSION Rapport is the glue that holds organizations together. It enables you to have the conversations - the easy ones and the tough ones - that are going to keep you and your organization advancing and adapting in this forever changing world. Continue to nurture your relationships. Rapport is active - it can grow and contract with each interaction.

ABOUT THE CONTRIBUTORS Samuel Akin, Ph.D. is an instructor at Howard University School of Business Accounting Department who obtained his Ph.D. in Organizational Leadership. His research interest are leadership, self-leadership, and virtual leadership. Yan Jin, Ph.D. is an assistant professor at Hampton University. Shamis Pitts, MBA, CPC, PCC, SHRM-SCP is the founder and owner of Pitts Leadership Consulting LLC, a company specializing in organizational health and employee wellbeing. PLC’s mission is to strengthen the employee/employer social contract to support a thriving and engaged workforce. That commitment comes to life by delivering consulting, advising, training and facilitation, and executive and leadership coaching services within the domains of leadership development, employee engagement and inclusive culture building. Robert P. Singh, Ph.D. is anassociate professor and the Dr. Abraham Pishevar Endowed Chair of Finance and Entrepreneurship in the School of Business at Howard University. Dr. Singh’s research primarily focuses on issues facing Black and minority entrepreneurs, as well as macroeconomic issues in the U.S. economy. Tao Zeng, Ph.D. is an associate professor at Wilfrid Laurier University.

A Research Journal for Black Professionals The Executive Leadership Council

The Executive Leadership Council (ELC) is the preeminent membership organization committed to increasing the number of black executives in C-suites, on corporate boards, and in global enterprises. Its mission is to increase the number of successful black executives, domestically and internationally, by adding value to their development, leadership, and philanthropic endeavors, thereby strengthening their companies, organizations, and communities across the life cycle of their careers. Its purpose is to open channels of opportunity for the development of black executives to positively impact business and its communities.

ISBN 979-8-218-32706-4

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