At any academic institution, one of the most highly valued outcomes is knowledge. In business schools across the country, faculty, staff and students produce insights that can change how business is done, inspire evidence-based management and shape the face of industries.
Part of being a global citizen, however, is ensuring that these discoveries are shared. Research without dissemination does not solve real-world problems. Bringing knowledge to the hands of practitioners is critical for the translation of insights into action.
At the Fox School, we are committed to bridging the gap between academia and industry—that’s why, in 2014, we launched the Executive Doctorate in Business Administration (DBA) program to teach the tools of applied theory and research to senior executives. In this three-year, part-time program, industry leaders come together to learn a new way of thinking to solve tomorrow’s business problems.
Why the DBA Matters
In business, many organizations encourage their employees to innovate. However, the Fox DBA allows executives the freedom to experiment with evidence. By introducing students to new tools for understanding organizational systems and preparing them to address challenges with facts and data, the program offers senior managers the opportunity to become thought leaders.
“I was in the military for over 20 years. I was looking for growth and new challenges,” says Dennis Martin, DBA ’18. “I wanted a more practitioner-focused doctorate rather than just a theory-based program.”
The structured program unites academically rigorous research with practice-focused business questions. Then Fox DBA alumni like Dennis bring their insights—both the knowledge generated from the program and the tools for new ways of thinking—back to work.
Leading the Charge
“The Fox School is proud to be a leader in the DBA space,” says Steve Casper, managing director of the DBA program and professor of finance at the Fox School. “Our research focus, combined with the faculty mentors, really make our program stand out.”
Our DBA scholarly practitioners were on display at the Engaged Management Scholarship (EMS) Conference, which the Fox School hosted last September. The annual international conference, which is for doctoral students, alumni, faculty and managers involved in applied research and evidence-based management on a global scale, brought over 200 people from 100 organizations to discuss the importance of bringing research into the real world.
Presented by the Executive Doctorate in Business Administration Council (EDBAC), an organization representing more than 50 member schools in ten countries, EMS unites the academic and the practical into one three-day conference.
“By hosting EMS, we demonstrated to the business community that the Fox School cares about bringing research to the real world,” says Casper. “We were very proud to host EMS and show off our university, as well as the city of Philadelphia.”
Applying Research to Business
At EMS, the Fox School strengthened its community of thoughtful and knowledgeable practitioners. Faculty engaged in networking across countries, programs and disciplinary fields. Students stretched the applications of their research beyond their own ideas and sought feedback from their peers. Program managers learned from each other and identified best practices for running DBA programs around the world.
“One of the more prominent questions during the conference was, ‘How do we come up with interesting problems that are researchable but also have applied business value?’” says David Schuff, professor of management information systems at the Fox School.
One example of these practical questions: How do female members of a company’s board of directors perform differently than companies with all-male boards? Ofra Bazel-Shoham, a graduate of the Fox DBA program in 2017 and assistant professor of finance at the Fox School, received the 2018 Best Paper Award in Applied Business Research, sponsored by Business Horizons, an academic journal from Indiana University, for her research that answers that question. Bazel-Shoham found that, while there was a negative correlation between the number of women on boards and the number of investments in R&D, women were more likely to focus on monitoring performance, which ends up incentivizing risky, but data-driven decisions. “As female leaders put more emphasis on monitoring,” says Bazel-Shoham, “gender-diverse boards were able to quantify and measure their decisions better than all-male boards.”
As the Fox School recommits to its position as a leader in changing global business, the DBA program can energize the bridge between research and industry. “At EMS, we built up energy and excitement of impactful and applied research,” says Susan Mudambi, academic director of the Fox DBA program and associate professor of marketing and supply chain management. “It shows how the Fox DBA is an important part of education in today’s world.”
This story was originally published in Fox Focus, the Fox School’s alumni magazine.
Each year, consumers create 16.3 zettabytes of information—enough to fill over 127 billion iPhones. Sorting through all this information is like trying to find a needle in a haystack the size of California.
Within these treasure troves of data are valuable insights waiting to be discovered. Data scientists use statistics, math, and information technology to sort through enormous datasets with millions of variables, looking for patterns. Yet combing through this information takes immense power, not to mention computer memory. So do they sort through it all?
That’s where people like Zhigen Zhao, associate professor of Statistical Science at the Fox School, come in.
Zhao and his statistician colleagues invent new ways to use statistics, overcome computation limitations, and see patterns through the noise. Their discoveries range from a newly patented methodology that enables users to analyze millions of data points in seconds to a new threshold for pinpointing statistical significance.
Deciphering Genetic Codes
Humans have 20,000 genes in our DNA. Much like data, decoding how each gene interacts with another can provide valuable insight, in this case into a person’s health. With over 190 million possible pairs, that’s a lot of variables to test.
“Years ago, 10,000 was considered a big data set, but not anymore,” says Zhao. When using standard algorithms like distance correlation, statisticians run into issues with computation speed, and the old algorithms can’t keep up with the large datasets available today.
Zhao and his colleagues devised a methodology that can analyze all of these variables in seconds. “Our method only takes two-tenths of a second to finish this kind of calculation,” says Zhao. His computer would crash when using older algorithm to analyze a dataset of that size.
“People’s health can depend on a specific combination of their genes,” says Zhao. This revolutionary methodology, which was recently approved for a patent, can identify certain combinations of genes that may help doctors understand medical issues ranging from heart disease and Alzheimer’s to obesity and alcoholism.
Discovering Differences in Education
With millions of pieces of information, statisticians and data scientists often grapple with the problem of false discoveries—inferring a pattern that is not truly significant. Statisticians try to account for these false discoveries, but this may lead to a less complete picture of the data.
Zhao and his colleagues created a new algorithm to reduce the number of false discoveries while keeping more pertinent patterns than other methods. Zhao’s team applied this algorithm to school districts in California, analyzing standardized test scores of students from over 4,000 elementary schools.
The researchers compared pass rates from two groups of students, the socioeconomically advantaged and the socioeconomically disadvantaged. Normally, the advantaged students will have higher scores than their disadvantaged counterparts. However, Zhao used his algorithm to identify schools that have unusually small or unusually large differences between the two populations—where the disadvantaged students were either significantly underperforming or overperforming in statewide math tests.
Their new algorithm found more schools whose populations have significant differences in test scores, providing a more complete understanding of the dataset. “The main idea for this method is to incorporate school district information to get a new threshold,” says Zhao. “The standard method, which doesn’t include this information, can be either overly conservative or overly liberal.” This kind of refined analysis can help district and state policymakers to reallocate resources to support underperforming schools or to imitate overperforming schools.
From education to healthcare and everything in between, Zhao and his fellow statisticians sort through enormous datasets, finding new ways to compute that better our everyday lives.
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
Victor H. Gutierrez-Velez never expected his work to lead him to the topic of public health. His expertise lies in remote sensing science, analyzing data such as satellite images. “Every day, numerous satellite images are taken,” says Gutierrez-Velez. And the information drawn from these images has both academic and commercial applications.
For example, satellite images can help prescribe management, fertilization, irrigation, and other activities in precision agriculture, according to Gutierrez-Velez. They can help the insurance industry assess risks related to flooding or other natural disasters, or to verify crop insurance complains. Satellite imagery can allow energy companies to pinpoint the ideal location for solar panels. And this kind of data, it turns out, can even come in handy when it comes to fighting certain diseases.
To that end, partnering with colleagues with expertise in biology and public health, Gutierrez-Velez, assistant professor in the College of Liberal Arts, has recently been drawn to an unlikely research subject: mosquitoes. Specifically, the tiger mosquito (scientific name: aedes albopictus). What’s so interesting about this tiny, blood-sucking bug?
“It’s worrisome. They can spread the Zika virus and other dangerous diseases,” says Gutierrez-Velez.
In 2016 when the Zika pandemic caught his interest, mosquitoes dominated the headlines. Once thought to be limited to tropical and subtropical regions, the tiger mosquito had expanded its territory into most continents. Climate change plays a role, but these mosquitoes are also particularly aggressive. They’re among the 100 most invasive species in the world. In the 1980s, they were first spotted in the U.S. in Texas. Today, they reach as far north as Connecticut. Their presence in Pennsylvania remains an ongoing public health concern.
For his project, a recipient of the Office for the Vice President of Research‘s Targeted Grant Program and supported by the Data Science Institute housed at the Fox School, Gutierrez-Velez decided to look at multiple datasets, including climate data, information gathered from sampling for the presence of the tiger mosquito, land cover data, and census information. Gutierrez-Velez believes that with these and other datasets as inputs, machine learning and advanced algorithms can be used to predict the locations of tiger mosquito populations in advance of the season.
One of the most interesting possible findings of this research is that the tiger mosquito is less of a rural dweller than previously thought. “What we’re finding contradicts conventional wisdom about where these mosquitoes live. They are becoming domesticated animals. They prefer to be where lots of humans are living closely together—in cities. Because they love our blood,” says Gutierrez-Velez.
Scientific curiosity led Gutierrez-Velez to census data, which is not necessarily an obvious source of information to predict the presence or absence of a small flying bug. “If they feed on humans, human behavior should have something to do with it,” he says. And it does seem like including this data makes for a more accurate prediction about where the mosquitoes will go next.
Gutierrez-Velez’s ultimate goal for the project is to perfect a reliable working model that can be used to predict the upcoming mosquito season. Knowing that a particularly bad mosquito season is about to start will give officials the opportunity to plan in advance.
For example, the most affected areas can be targeted for treatment before the problem becomes unmanageable. Residents could be strongly cautioned in advance of the season to deal with housing-related conditions, such as places that collect standing water, which act as mosquito breeding areas. In the event that mosquitoes are spreading Zika or another virus, these protections could even save lives.
“There’s a lot we can do if we have a model that can say, ‘Hey, it’s going to be a bad year for mosquitoes, get ready,’” says Gutierrez-Velez.
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
The Class of 2019 from the Fox School is full of high achievers. Jonathan Huynh is one of them, but he took a path unique to undergraduate students—a deep dive into research.
“I have always been purpose-driven,” says Huynh. “The education I’ve received at Fox has constantly challenged me to delve deeper into concepts and ideas and look at them from an out-of-the-box perspective.”
Huynh is a part of the first graduating class of undergraduate students with a Statistical Science and Data Analytics (SSDA) major at the Fox School. Looking back at his journey, “I actually started out in actuarial science, but ultimately got drawn to [this program],” says Huynh. “I liked the idea of being able to gain quantitative and analytics skills to translate large data sets into meaningful solutions.”
Dr. Pallavi Chitturi, deputy chair of the Statistical Science Department, says that the new undergraduate major in Statistical Science and Data Analytics is highly rigorous. “Students get a solid foundation in statistical methods, programming languages and statistical computing, which is why the major is gaining popularity among students and employers alike,” says Chitturi.
It is rare to see an undergraduate student so deeply involved in high-quality research. “My first exposure to research was through Dr. Robert Pred, the academic director of the Fox Business Honors program,” says Huynh. ”My friend and I did an independent research project over the summer of 2017 and Dr. Pred helped set us up with an advisor to oversee our research.”
Since then, Huynh became involved with data science research projects at Temple’s College of Public Health. One of the ongoing projects that Huynh is working on compares the difference between the cost to patients and their outcomes at the Temple University Hospital versus other hospitals in the area. “Dr. Michael Halpern, an associate professor in the College of Public Health, reached out to us as they were looking for a research assistant with strong statistical skills and Jonathan was a perfect fit,” says Chitturi, who presented Huynh with this opportunity. “Dr. Halpern was so impressed by the quality of his work that he later hired another student from the SSDA major.”
Huynh largely attributes his early success to the well-designed and differentiated curriculum at the Fox School. “I’ve seen how beneficial it is to have technical skills backed by an understanding of business for courses in data science,” Huynh says. “It allows you to be at the intersection of knowing how to comfortably work with data to drive business decisions–a skill that recruiters highly value.”
Huynh plans on pursuing a master’s degree in the near future and is looking at specialized fields of data science such as Artificial Intelligence and Machine Learning. When asked if a doctorate in data science is in the cards, Huynh says he believes education is a matter of continuous learning. “Even if I don’t pursue formal education [like a PhD], I will always be self-learning… and keeping up-to-date with the latest technologies,” says Huynh.
Huynh‘s ultimate goal is to run a startup that uses data science to solve important problems that have the potential to improve the world. “I have always had entrepreneurial aspirations,” says Huynh. “The connections that I have made at Fox and the experiential learning opportunities I’ve had here have really shaped my perspectives and prepared me to achieve these goals.” He has already started on this journey, having participated in 2019 Be Your Own Boss Bowl ® pitch competition and winning the “Crowd Favorite” award.
As of now, Huynh is all set to begin his foray into the corporate world as a Business Technology Analyst at Deloitte in the Government and Public Sector practice. Huynh says, “The thing I am most excited about is to be able to make a real impact in the everyday lives of people, as I will be working on projects with government agencies to make the lives of people easier.”
Learn more about programs in the Department of Statistical Science.
Change doesn’t happen overnight, especially in education.
For years, academics and business executives alike have questioned whether the insights from business school research conducted are getting into the hands of those who need it. The debate about “rigor versus relevance” is age-old. While the answer may seem simple, the process of getting there is complex.
The Fox School of Business is committed to pushing this conversation forward. On Friday, March 29, the Fox School’s Translational Research Center (TRC) hosted the 2019 Impact Summit, bringing together deans, faculty and students from across disciplines and parts of the world to determine how schools can move the needle of impact in tangible ways.
The attendees sought to answer the question: How can business school leadership change the way research is conceived, produced and implemented to prioritize impact?
These are five lessons business school leaders can apply:
1. Start at the top. “It takes time to re-engineer a school at a systems level,” said Tarun Khanna, a professor at the Harvard Business School. However, a top-down perspective is key to encouraging institutional change.
Jerry Davis, associate dean at the Ross School of Business, highlighted the University of Michigan’s experiments with the promotion process. By making research impact a more significant part of an associate professor’s evaluation, he advised, deans can use promotion structures to affect change in the way their faculty conduct research. Getting top business schools across the country to agree on a new evaluation structure would be even more influential.
2. Instill impact’s importance early. The attendees also discussed tackling the issue of impact from the opposite side—starting with junior faculty and doctoral students. Elizabeth Cowley, deputy dean of the University of Syndey, said that in Australia, “faculty are encouraged to build a narrative of the long-term impact [they] have had on some sector of society.” Attendees agreed, remarking on the importance of letting junior faculty members define for themselves how they would want to make an impact and develop a strategy based on that objective. With doctoral students, the starting point should be their research questions—advisors should ask if it is grounded in a real-life phenomenon and has relevance in the business world.
3. Systematically engage with business. “Business leaders tend to look at our schools primarily as labor markets for sourcing the MBAs and business graduates,” said Joanne Li, dean of the business school at Florida International University. “We need to help them recognize us as knowledge markets as well. We are able to produce expert knowledge vital for their business growth and survival.”
Brent Beardsley, the chief strategy officer at Vanguard, talked about the value of an advisory board made up of executives, entrepreneurs and academics. “That mix is really rich,” he said. “This is a lab outside of the walls of Vanguard’s large institution that can get out in front of market trends and themes.”
Participants championed the creation of a brokerage platform between companies and universities that could connect those who have real problems to those working on practical solutions. Simple activities like business sabbaticals for faculty, corporate engagement in research projects and programs like Fox Management Consulting can help faculty to better define their research questions.
4. Use teaching as a tool. One speaker suggests a change in vocabulary to underscore the importance of teaching. “We shouldn’t be referring to a ‘teaching load,’ said Gautam Ahuja of SC Johnson Graduate School of Management at Cornell University. “It’s not a load, it’s a tool.” Academic leadership can encourage faculty to step into the shoes of learners, focus on practical insights in the classrooms and foster intellectual questions with relevance. Stronger connections to industry, through practitioner conferences, relationships with practice faculty and co-teaching with executives can also benefit classroom outcomes.
5. Be a community hub. Business schools will also benefit from a stronger community connection. “We should be known by the community where they can come to get ideas,” said Will Mitchell, a professor at the University of Toronto’s Rotman School of Management. Attendees brainstormed ways to make research more accessible but noted that faculty will need different reward structures and training to bring that to fruition. Ideas like three-minute presentations or one-page summaries of academic papers can help get ideas out of academia and into the real world.
Ronald Anderson, interim dean of the Fox School, remarked at the end of the day that a lot was learned. “Disruption is going to have to be part of the process,” he said. “Technology and innovation are changing higher education, and research is going to have to address that.”
The event, a follow-up to the 2018 Editors’ Summit, is part of a series of initiatives by the TRC to change how both academics and practitioners view business research. Other activities have included the TRC’s Seminar Series, which invites executives to share their viewpoints on faculty research presentations, and case writing workshops, which encourage faculty members to learn and perfect their skills in writing and submitting teaching cases for publication.
Learn more about the Fox School’s Translational Research Center.
More than 15 million adults struggle with alcohol addiction. In fact, according to the CDC, one in ten deaths of working-age adults in America is linked to alcohol. That’s one reason data on alcohol use has been chosen by researchers for study from the enormous data set from the U.S. Department of Veterans Affairs’ ambitious Million Veteran Program (MVP). The VA intends as the project’s name states, to gather data on an astonishing one million service members.
Kuang-Yao Lee, assistant professor of statistical science at the Fox School, sees a world of potential new knowledge in this vast cache of data. This is particularly true of alcohol use because the data from the MVP is longitudinal, which means the same measurements are tracked over time. Alongside the support from the VA, Lee’s project received funding through from Office for the Vice President of Research at Temple University.
Volunteers in the MVP each submit blood samples as well as health surveys, amassing a dataset that comprises both genetic data and behavioral patterns. Beginning in 2016 when he was a researcher at Yale University, Lee and his colleagues have been using this information-rich resource to search for the specific combination of genes that correspond to alcohol and other substance use.
“Previous studies have suggested [these genes exist], but mostly were only limited to small scales or restricted conditions,” says Lee. “We want to use statistical models to find out if this is really a valid assumption. Our results so far suggest a very strong association.”
While ample electronic health records and genetic data have long been available to researchers, only recently has the efficient computing power become available to slice and dice the information into accurate, usable new insights and discoveries. More sophisticated algorithms combined with larger-than-ever computer storage capacity, as well as parallel computation techniques, allow today’s researchers to make meaning from a huge amount of complex data.
How huge? “Depending on the facility, the whole genome sequencing [for one person] can produce hundreds of millions of variants,” says Lee. Questionnaires allow researchers to gather large amounts of information about each subject every time they are administered. Multiply that by one million veterans. “We’re talking about not just billions, but millions of millions of points of data,” he says.
Data with this level of complexity can lead to findings that are more nuanced and reliable than in the past. Previously statistics sometimes led to oversimplified and other not-quite-right conclusions. We’ve all heard the old axiom, “There are three kinds of lies: lies, damned lies, and statistics.” But as so-called big data increases in scope and complexity and the tools used to analyze this data become more sophisticated, statistics are becoming more honest than ever before. From projects such as the Million Veteran Program and other similarly vast datasets, new genetic truths may ultimately emerge.
There are many possible real-world applications for this research. For one thing, determining which specific genes are linked with alcohol and other substance abuse could lead to new and better medicines and treatments for the very veterans who have volunteered their most sensitive personal information for this work. A dialed-in genetic profile that indicates a vulnerability for substance abuse could be used to screen kids and even adults while there is still time for effective early interventions that can keep them on a healthy path. Given the current public health crisis around opioids, alcohol, and other substance use, a breakthrough of this kind could have far-reaching benefits.
Lee says that the knowledge gleaned from the Million Veteran Program about substance abuse may lead to similar projects that could help solve other vexing behavioral, health, and genetic puzzles. He also notes that the innovative statistical models and tools he’s used in this research could be applied in myriad ways to other complex datasets.
For example, online shopping platforms can easily observe huge amounts of individual consumers and, at the same time, collect data across large numbers of variables. “One of the core problems in business analytics is to use statistical models to study the inter-dependency between observed variables, for example, the dependency between decision making and consumer behavior,” Lee says.
“There are a surprising number of similarities between genomics and online shopping.”
This story was originally published in On the Verge, the Fox School’s flagship research magazine. For more stories, visit www.fox.temple.edu/ontheverge.
Everything around us seems to be getting smarter by the day—like smart refrigerators, driverless cars and robotic assistants. The “Internet of Things” (IoT), which is the internet-enabled network of everyday devices, has become prevalent in our lives, both inside and outside of the workplace. But with the rapid developments in recent technologies like Artificial Intelligence (AI), will these intelligent systems make human workforce redundant?
In other words: do we run the risk of being replaced by machines?
Paul Pavlou, Milton F. Stauffer Professor at the Fox School, argues that instead of replacing us, AI and humans will work side-by-side to address some of the bigger problems that neither can solve alone. Popularly referred to as “Augmented Intelligence,” this concept focuses on the assistive role of AI to improve human intelligence, rather than computers fully taking over our jobs.
Man vs. Machine
While computers have the ability to collect, aggregate and analyze an enormous amount of data, humans surpass machines when dealing with ambiguity, vagueness and incomplete information. Augmented Intelligence recognizes these complementary strengths and problem-solving capabilities of man and machine. “This collaborative interaction between human beings and computers arises when IoT collects the data and AI tools perform calculations based on criteria determined by humans,” says Pavlou, who is also the co-director of Temple’s university-wide Data Science Institute.
For example, GIANT Food Stores has introduced “Marty,” a robotic assistant, to the 172 stores in Philadelphia and the surrounding region. The robot roams the store, seeking to identify and eliminate spills from foods, products or liquids. Other examples can be found in the retail industry, where location-based technology devices and eye-tracking devices can help optimize the placement of merchandise. Meanwhile, salespeople equipped with mobile devices can leverage personalized information in real-time to sell products customized to individual shoppers.
A More Human IoT
In the future of work, managers can embrace both the fully-automated and Augmented Intelligence solutions. This choice depends on factors such as the nature of the task, expected performance and the costs and risks of autonomous IoT solutions that would operate without any human interventions. For example, automated manufacturing, predictive maintenance and security IoT solutions may—cautiously—be fully automated. But in industries like healthcare, cybersecurity and financial technology, human oversight will still be crucial.
For the time being, appropriate IoT designs should maintain a reasonable level of human control and oversight, says Pavlou. “This will give us adequate time to get comfortable with delegating control to machines.” In the distant future, machines alone might dominate decision-making in most applications. However, Pavlou says, “It will be a fairly long time until this happens. Until then, major intellectual advances will be made by humans and computers working together.”
Home-sharing has revolutionized the lodging market. Today, digital platforms such as Airbnb and HomeAway are popular choices over conventional hotel stays. With the industry expanding exponentially over the past decade, home-sharing lodging is expected to reach $107 billion—or 10% of total accommodation bookings in the country—by 2025.
So what makes Airbnbs so popular? Three researchers from the Department of Tourism & Hospitality Management at Temple University’s School of Sport, Tourism and Hospitality Management sought to answer that question.
In a study recently published in Tourism Management, Assistant Professor Yang Yang, PhD student Karen Tan and Professor Xiang (Robert) Li used a dataset from a nationwide household tourism survey to better understand this growing segment of American travelers.
“First, we looked into what segment of consumers choose Airbnbs over conventional hotel stays,” Yang says. The researchers studied five broad categories of user-motivations: tripographics (including the purpose of the trip, nights of stay, expenditure, children companions, and group size), past travel experiences, tech savviness, socio-demographics (such as age and education) and destination characteristics (like home-sharing supply and crime rate).
“Airbnbs are selected by travelers with particular needs,” Yang notes. “Tourists who are younger, more tech-savvy and traveling with a large group size were the leading users.” Some of the other characteristics common across most users included travel for leisure purposes, itineraries planned in advance, interest in local cultural activities and the presence of personal vehicles during the trip.
The rate of crime in the destination was an important determinant in the choice of stay as well. “Travelers are less likely to stay in Airbnbs when there are crime-related security concerns,” Yang says. “Hosts and platforms should consider ways to mitigate tourists’ fear of crime, such as the introduction of home safety features, methods of crime prevention or even by offering insurance coverage.”
Yang highlights that their study challenges the popular stereotype that travelers choose Airbnbs mainly because they are cost-effective. “We did not find any significant effects of household income and price differences between hotels and Airbnbs on tourists’ choices,” Yang says. Based on this insight, he thinks that any price wars between hotels and Airbnbs would not be beneficial for either group.
The researchers also investigated the effect on the guests’ experiences when staying in Airbnbs versus a hotel. “Trip satisfaction did not differ between the two groups,” says Yang, “but the perceived value of the trip was significantly higher in the home-sharing group.”
That additional sense of value experienced by the users reflected the extra benefits that they received in Airbnbs that were not met in a traditional hotel setting. Yang says, “Facilities such as household amenities, extra space, experience authenticity and host-guest interactions were some of the key reasons.”
Karen Tan, a PhD student in the department and a co-author of the paper, believes that Airbnbs do not necessarily jeopardize the business of hotels. “Home-sharing may very well appeal to a segment of the population that previously didn’t travel as much,” she says. “Peer-to-peer accommodation could just be making the lodging pie larger.”
Much of the optimism underlying the projected growth of home-sharing lodging arguably lies in its untapped potential. “As the market for Airbnb grows,” says Yang, “hotels should not compete on lower prices, but rather focus on aspects that deliver greater value to guests.”
Learn more about Fox School Research.
According to the Food and Agriculture Organization, by 2050 the world’s population will have an estimated 9.1 billion people, and food production will need to expand by 70 percent in order to match the increased rate of consumption. The future of food security is in the hands of consumers and producers and what they can do to create sustainable food systems to account for the predicted growth.
On a smaller scale, agriculture in Pennsylvania and the Northeast region is facing some changes to its operations. Design thinking might not be top of mind for agriculture, but approaching solutions through these practices yields some fresh insights for a healthy food system.
Marilyn Anthony, director of business development for Fox Management Consulting, and the Vice President and Agricultural Lending Manager of Ephrata National Bank William Kitsch teamed up to lead an interactive workshop for the Northeast Sustainable Agriculture Working Group’s (NESAWG) annual “It Takes a Region Conference” held in Philadelphia October 26 and October 27th, 2018.
Anthony’s and Kitsch’s workshop, “Here’s the Data: Let’s Design the Solutions,” used principles of design thinking to encourage participants to create consumer and user-oriented solutions to obstacles facing farmers and producers. “What surprised me was that everyone found a topic that they are passionate about and wanted to work on,” Anthony said. “We asked our workshop audience to think from the perspective of a user, someone who could benefit from or who could participate in Pennsylvania’s strategic recommendations and to think about how they could connect.”
Anthony and Kitsch presented the results of a research study, led by Temple University’s Fox Management Consulting group, a cohort of OMBA students, and the Philadelphia-based economic consulting firm E-consult Solutions, exploring 10 sectors of agriculture in Pennsylvania. The Pennsylvania Department of Agriculture (PDA) and Team Pennsylvania funded the research project, forming the basis for PDA’s strategic recommendations. The resulting six strategic initiatives focused on improving the branding and marketing, infrastructure of processing and manufacturing, business climate, workforce development and educational opportunities, and diversity of products within food systems in order to create more opportunities for Pennsylvania growers and producers.
Kelly Kundratic, the Manager of Agriculture Policy and Programs for Team Pennsylvania, took an active role in the workshop. “Learning the design thinking process and really stepping back, thinking from a place of empathy, looking at these goals, that’s something that I use now as much as I can,” Kundratic explains. “It can be time consuming, but really reframes how I’ll approach helping government and industry move together to act upon these six strategic initiatives. Trying to be empathetic and use the design thinking model will help me be able to do my job more effectively.”
Emphasizing the core take-away from the workshop, Anthony explains, “what was very valuable and useful was getting people to think about who, other than themselves, might be in that space and to begin to generate some ideas for how they could make an impact.”
Workshop participants brought their experience and perspectives from Vermont, Maryland, New Jersey, New York and Pennsylvania. Many participants actively work to create more accessible and equitable food system as educators, nonprofit advocates, and funders.
Founded in 1992, NESAWG is a network of more than 500 organizations across Connecticut, Delaware, Massachusetts, Maine, Maryland, New Hampshire, New Jersey, New York, Pennsylvania, Rhode Island, Vermont, West Virginia, and Washington D.C. It works with
organizations and individuals involved in every sector of sustainable agriculture from farming and ecology to architecture and social services to garner awareness and support for the creation of just, sustainable food systems.
Are you interested in learning about sustainability topics? Check out “BlockChain Technology for Sustainable Procurement” in the Fox Video Vault.
Consumers today are heavily dependent on online reviews to make informed choices about what to buy. In fact, studies show that as many as 90 percent of consumers read online reviews before making financial decisions, and nearly 70 percent trust these opinions.
Given their importance, how do you tell if the reviews are from genuine customers?
Subodha Kumar, director of the Center for Data Analytics and professor of Marketing and Supply Chain Management at the Fox School, developed an approach to detect fake reviewers on online digital platforms. In his paper published in the Journal of Management Information Systems, Kumar proposes an algorithm that analyzes the behavior of reviewers on a set of key features to help differentiate between the real and the fake.
“A user who reads a negative review of a restaurant is likely to trust the message, even though it was written by a stranger,” Kumar says. “One convincing review can often persuade consumers to shift their brand loyalty or drive several extra miles to try a new sandwich shop.”
This gives firms a strong incentive to influence their online review ratings. “Business owners inject their public ratings with a positive bias,” says Kumar. “They use fake accounts or paid reviewers to either promote their offering or strategically denounce competitors’ products.”
In studying a dataset from Yelp, a popular restaurant review platform, Kumar observed a striking difference in the way spammers interact on online platforms. “Even though individual reviews by a spammer may look genuine, collectively we can capture anomalies in the review patterns,” Kumar says, “In fact, they are remarkably skewed.”
By analyzing this pattern of behaviors, Kumar’s approach to detecting review manipulation can not only improve the experience of consumers across industries but also increase the credibility of reviewing platforms like Yelp.
Kumar considers six distinct features of every review in the data set:
- Review gap: Spammers are usually not longtime members of a site, unlike genuine reviewers who use their accounts from time to time to post reviews. Thus, if reviews are posted over a relatively long timeframe, it suggests normal activity. But when all reviews are posted within a short burst, it indicates suspicious behavior.
- Review count: Paid users generally generate more reviews than unpaid users. In other cases to avoid being detected or blacklisted, a spammer could post very few reviews from one account and create a new account.
- Rating entropy: Spammers mostly post extreme reviews since their goal is either to artificially improve a particular company’s rating or to bring a bad reputation to its competitors. This results in high entropy—or drastic randomness—in fake users’ ratings.
- Rating deviation: Spammers are likely to deviate from the general rating consensus. If genuine users fairly outnumber spammers, it is easy to detect instances where a user’s rating deviates greatly from the average ratings from other users.
- Timing of review: One strategy spammers may use is to post extremely early after a restaurant’s opening in order to maximize the impact of their review. Early reviews can greatly impact a consumers’ sentiment on a product and, in turn, impact sales.
- User tenure: Fake reviewers tend to have short-lived accounts characterized by a relatively large number of reviews and handles, usernames or aliases designed to avoid detection.
After considering these variables individually, the algorithm then looks into the way the variables interact with each other. It employs techniques like supervised machine learning and accounts for the overall review behavior of a user to provide a robust and accurate analysis.
Kumar’s methodology can also be deployed to post the information of the spammers in real-time. Digital platforms like Yelp could develop a spam score using these key features for each reviewer and share it with business owners and consumers, who can subsequently be tagged or filtered.
“The issue of opinion spamming in online reviews is not going away and detecting the perpetrators is not easy,” says Kumar. But developments in approaches like these, he says, “offer great insights to businesses, allowing them to create more effective marketing strategies based on the sheer volume of genuine, user-contributed consumer reviews.”
A board of directors plays a crucial role in determining the success of any organization and is largely responsible for major strategic decisions. However, females in these top management roles are often underrepresented. Without women on boards, companies are losing out—not only on talented leaders, but also on different perspectives of business. This raises the question: in what ways do companies with women on the board perform differently than companies with all-male boards?
Prior research suggests there are gender differences in risk-taking decisions, with many researchers supporting that women are more sensitive to risk than men. However, Ofra Bazel-Shoham, research assistant professor in the Department of Finance at the Fox School, reconsiders the implications of this conclusion.
Bazel-Shoham argues that female leaders change the way business is being done in her paper, “The Effect of Board Gender Diversity on R&D.” She looked at boards’ decisions regarding high-risk, high-reward investment decisions, as well as their professional behavior, to understand the differences in outcomes that gender-diverse boards produce. The research recently won the Best Paper Award at the 2018 Engaged Management Scholarship Conference, hosted by Temple University this September. The award was sponsored by Business Horizons, an academic journal from Indiana University.
As a proxy for analyzing risk-taking decisions, Bazel-Shoham used choices around research and development (R&D), often a potentially risky yet highly rewarding investment. “It requires upfront resources and has a very low probability of success,” she says.
Bazel-Shoham, who is also the academic director of Fox School’s new part-time MBA Program in Conshohocken, collected data from CEOs and board members in 44 countries and over a period of 16 years. The gender disparity was already obvious, as she notes in her sample only 2% of all CEOs and 9% of all board members were female.
The study found that while the direct correlation between the number of women on boards and the number of investments in R&D was negative, women were more likely to focus on monitoring performance, which ends up incentivizing risky but data-driven decisions. Bazel-Shoham says, “As female leaders put more emphasis on monitoring, gender-diverse boards were able to quantify and measure their decisions better than all-male boards.”
Bazel-Shoham elucidates this argument by analyzing the behavior of female directors who are most often outnumbered by their male counterparts. Her interviews with female leaders suggest that being in a minority puts more pressure on women to not make mistakes and make data-driven decisions.
She elaborates, “We realized that female directors felt they were ‘under a magnifying glass’ most of the time and were judged more stringently than their male colleagues.” This made them make more conservative decisions, which usually translated into making lesser high-risk R&D investments. However, teams that quantified their results better supported performance-based compensation where incentives are measurable and dependent on the actual outcome rather than on vaguely defined promises.
Organizations often use performance-based incentives to motivate managers to make riskier but potentially profitable long-term investing decisions. Bazel-Shoham says, “We observed that such remuneration systems encourage CEOs and senior management to engage in more R&D activities.” With women involved, boards more often supported this form of compensation, in affect encouraging managers to make more of these investments. Bazel-Shoham found that these actions successfully mitigated women’s effect of being more risk-averse.
Besides indirectly increasing R&D spending, Bazel-Shoham notes having even one woman on the board of directors significantly influences how the board behaves, the decisions it makes and their resulting outcomes. To illustrate this, she quotes an experience of a male CEO of a large educational organization. “The women directors read all the materials ahead of time, have specific questions and are more professional than the others,” he says. “They have changed the organizational culture of the board. The men, in turn, have started to prepare themselves better as well.”
Underrepresentation of women on boards of directors continues to be a pressing issue to shareholders and society at large. However, organizations are slowly understanding the strategic importance of leveraging a more diverse top management team. With rapidly changing market dynamics, leveraging the power of gender diversity is beneficial for the long-term success of businesses.
Remember the last time you donated warm clothes to a homeless shelter and felt good about yourself? Or that time your friends helped you get through a difficult life problem after which you couldn’t help but feel extreme gratitude towards them?
A lot of traditional research has been done on why people help and how they feel after helping. You Jin Kim, assistant professor of Human Resource Management at the Fox School, goes beyond just that by exploring the role of the recipient of the help. Her research emphasizes how demonstrating gratitude, as well as the helper’s feelings of pride, interact to encourage repeated helping.
In her paper, “A Dyadic Model of Motives, Pride, Gratitude, and Helping,” which was accepted for publication by the Journal of Organizational Behaviour, Kim demonstrates that the motives of the helper interact to predict pride via initial helping whereas recipient attributions of helper motives predict recipient gratitude in response to being helped. This interaction of emotions (i.e., pride and gratitude) influences any subsequent helping by the helper, making them both active members of the social exchange.
Kim points out that the helper’s motives drive their initial actions. She highlights two positive motives: “autonomous motives,” where individuals help because they value doing so, and “other-oriented motives,” where individuals help because of their concern for others. These motives often lead to voluntary helping that is intended to benefit others.
These motives affect the perception of the recipient and the level of appreciation they feel. “Recipients seek information about helpers and helping contexts because they seek to understand why others help them,” Kim reasons. For example, an employee might choose to cover a shift for a sick worker because he or she truly cares about the coworker’s welfare, leading to the recipient attribute this action to the helper’s selfless (what Kim classifies as autonomous or other-oriented) motives. In such interactions, the recipient feels more gratitude toward the helper.
Kim also considers that the motives may not always be altruistic. She elaborates, “They could be doing it because of impression management, career enhancement motives, and not truly directed towards benefitting others.” For example, a helper could choose to teach a peer a new skill with the goal of transferring an undesirable task to this peer. Such interactions fail to evoke the feeling of pride or gratitude in either party.
Kim highlights cases where, although the helping motive was genuine and the helpers experienced authentic pride, they did not engage in repeated helping unless recipients expressed their gratitude. “Unlike economic exchanges, social exchange returns are not specified in advance, and so reciprocity is not guaranteed,” says Kim. “A simple ‘thank you’ makes a lot of difference.” Thus expressing gratitude is very crucial in encouraging the helper to continue helping others in the future, making the recipient an important influencer of the interaction.
The results of these studies have practical implication for managers. “Managers need to understand why helping is being provided and create a work environment where employees do not feel pressured to help and that helping is voluntary,” says Kim. “It should not be related to any type of organizational decision, such as a promotion or vacation days.”
Importantly, gratitude also has positive implications for recipients. Kim says, “Managers also need to emphasize the benefits of showing gratitude and encourage recipients to communicate their gratitude when receiving help has been positive.” Such reciprocative interactions create a positive environment at a workplace, subsequently improving the efficiency and lowering the turnover intentions of all employees.
Learn more about Fox School Research.
Most public officials want to stay in office—and insurance regulators are no different. In the days, weeks, and months leading up to the elections, many assume that public officials would be proactive, striving to implement policies that improve their credibility and increase their chances of reelection. However, recent studies by Martin Grace, Harry Cochran Professor of Risk, Insurance, and Healthcare Management at the Fox School and Tyler Leverty of the University of Wisconsin-Madison, say that this is not the case for insurance regulators.
The financial health of the insurance companies is closely monitored by the state insurance departments to provide protection to the policyholders. When a company faces a financial crisis, the regulators intervene and help them regain their footing. In situations where the company is irreversibly dying, they are declared insolvent, or bankrupt.
To keep these stages in check, insurance regulators conduct regular financial examinations, especially for companies facing financial crisis. In their paper, ”Do Elections Delay Regulatory Action?” which was accepted by the Journal of Financial Economics, Grace found that these interventions on failing companies fall by up to 78% in the year leading up to an election. These delays result in an increased cost of failure for both policyholders and taxpayers.
The reason for this seems to be rooted in the political incentives for the insurance regulators. Insurance commissioners are elected by popular vote in some states or appointed by the governor in the others. To have a positive opinion around their candidateship, insurance commissioners avoid making formal regulatory orders or making declarations of insolvency for insurance companies up to a year before the elections. “As this could raise questions on their competency and could be seen as a black mark when they run for higher office,” says Grace, “it is easier for insurance regulators to delay companies’ bankruptcies. So they strategically postpone any official resolution until after election day.”
And, Grace says, “The more competitive the race is, the more bad news might matter.” While appointed commissioners tend to delay interventions only before tightly contested elections where the appointing governor is running for office, elected regulators delay interventions before all elections.
To conduct this study, the researchers collected data from approximately 3,200 firms and 300 separate elections in 50 states over 21 years (1989-2011). With varying election dates and state-regulated insurance policies, Grace says, “these heterogeneities gave us a very rich data to study a given insurer at different intervals of time, across different states, and at various stages of the electoral cycle.”
With so much data and possible causations, it took the researchers about eight years to publish the paper. During various presentations of the research, Grace recollects offering a dollar to anyone who could come up with a plausible explanation to the observation that they hadn’t heard of before. ”We covered it all,” Grace says. “But if someone came up with a new idea, I would give them a dollar.” However, given their extensive data set and time, Grace and his co-author were confident in their findings that elections were the main cause of these delays.
Grace emphasizes that these delays are important because they cost taxpayers more money. When an insurance company goes bankrupt and they run out of cash to pay off their debts, the balance is covered by the government from the pool of state taxes collected from policyholders of the healthy insurers. For example, he reasons, “Let’s say we have a $100 left in the failed insurer. If we closed the insurer immediately, the value would remain $100.” However, if the insurer is closed in 6 months, there would be more costs associated, like paying employees and managers of the failed insurer. “That means all taxpayers will have to pick up the balance.” Grace’s research found that delays increased the cost to taxpayers by up to $0.48 dollars for every dollar of failed insurers assets at the time of insolvency.
Research shows that prompt governance reduces the delays caused due to elections. “This was seen to be especially true in the case of appointed regulators,” says Grace. Current laws mandate regulators to report and take timely corrective actions at prescribed levels of declining capital of the insurers, limiting the regulators’ ability to delay.
The effect of delays in regulating insurance companies has a discreet yet profound effect on the cost of insurance to society. Timely settlement of claims, especially when the insurance company is in a financial crisis, helps decrease the cost of failure to both the policyholders and taxpayers.
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The “agency theory of the firm,” a way of looking at social interactions in business, says that managers are agents of shareholders. As such, managers must generally make decisions that maximize shareholder profits. Since the Citizens United case in 2010, those decisions have included the right to make unlimited independent political expenditures, under the right to freedom of expression.
So what are the ethical implications of companies making contributions for or against a political candidate? Daniel Isaacs, assistant professor of Legal Studies and academic director in the Fox School, weighs on this question in his article, “When Government Contractors May or May Not Spend Money on Political Speech,” which has been accepted for publication in the Journal of Business Ethics.
“There are some situations where it will be in the economic interests of businesses to forgo making independent political expenditures,” says Isaacs. By aligning profit motives with ethical conduct, Isaacs aims to remove barriers to ethical behavior.
Sometimes, however, profits and ethics do not align. In these cases, Isaacs argues that managers may not use the agency theory of the firm as a means to escape their ethical obligations.
For example, says Isaacs, imagine a private prison that is experiencing a reduced number of prisoners due to declining crime rate in the state. The prison has the right to make independent political expenditures on behalf of a candidate that favors laws that would require courts to impose longer prison sentences for all crimes. The outcome of these expenditures and the succeeding election would increase profits for the private prison by ensuring a steady stream of prisoners who will spend more time in jail.
But what happens if maximizing profits for shareholders by making these independent political expenditures leads to profit and unethical outcomes, like longer prison sentences? Does the agency theory allow managers to ignore the ethical situation and simply make money? No, says Isaacs, “because the agency theory relies on the concept that principals must do that which agents dictate.” If that is the case, though, managers cannot act beyond the authority of their principals.
“This relationship between the managers and the shareholders does not dilute the managers’ moral obligation,” Isaacs says. “The agency theory does not grant them an ethical free pass.”
Isaacs says that the shareholders lack the power to authorize managers to make profits in a way that they wouldn’t do themselves. “And managers cannot escape their ethical obligations by claiming that they were just following orders,” he says.
Companies should consider whether it is in their best interests to make independent political expenditures, as forgoing in some cases might make them more appealing. For example, if a company voluntarily waives its right to make independent political expenditures, Isaacs argues that it can use that to its competitive advantage. “One of the risks that at least one private prison identified in its disclosure statement was that the public may change its perception of private prisons,” says Isaacs. “If the public becomes hostile to the concept of private prisons, governments may stop entering into contracts with the corporations—something that a reasonable investor would want to know.”
With the boundaries of profitability, law and ethical obligations blurring in the real world of business, Isaacs’ research works to identify ways in which the market can support ethical decision making. He finds an unexpected friend in agency theory, arguing that the way people justify profit maximization, also serves to demonstrate the limits of shareholder power to engage in or authorize others to undertake such behavior.
“Shareholders and managers, as human beings, have a moral obligation, and desiring profits does not justify all actions of achieving them,” he concludes.
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Peace has finally been brokered in a long-standing argument between two schools of thought in statistical science.
Research from Deep Mukhopadhyay, professor of statistical science, and Douglas Fletcher, a PhD student, was accepted for publication in Scientific Reports, a journal by Nature Research. Their research marks a significant step towards bridging the “gap” between two different schools of thought in statistical data modeling that has plagued statisticians for over 250 years.
“There are two branches of statistics: Bayesian and Frequentist,” says Mukhopadhyay. “There is a deep-seeded division, conceptually and operationally, between them.” The fundamental difference is the way they process and analyze the data. Bayesian statistics incorporates external domain-knowledge into data analysis via so-called “prior” distribution.
“Frequentists view ‘prior’ as a weakness that can hamper scientific objectivity and can corrupt the final statistical inference,” says Mukhopadhyay. “I could come up with ten different kinds of ‘prior’ if I asked ten different experts. Bayesians, however, view it as a strength to include relevant domain-knowledge into the data analysis.” This has been a disagreement in statistics over the last 250 years.
So, which camp is right? “In fact, both are absolutely right,” says Mukhopadhyay. In their paper, they argued that a better question to ask is, how can we develop a mechanism that incorporates relevant expert-knowledge without sacrificing the scientific objectivity?
The answer, Mukhopadhyay says, can ultimately help design artificial intelligence capable of simultaneously learning from both data and expert knowledge—a holy grail problem of 21st Century statistics and AI.
“The science of data analysis must include domain experts’ prior scientific knowledge in a systematic and principled manner,” Mukhopadhyay says. Their paper presents Statistical rules to judiciously blend data with domain-knowledge, developing a dependable and defensible workflow.
“That is where our breakthrough lies,” says Mukhopadhyay. “It creates a much more refined ‘prior,’ which incorporates the scientist’s knowledge and respects the data, so it’s a compromise between your domain expertise and what the data is telling me.”
Answering that question—when and how much to believe prior knowledge—offers dozens of real-world applications for Mukhopadhyay’s work. For example, healthcare companies can use apply this to new drugs by leveraging doctors’ expertise without being accused of cherry picking data for the sake of a speedy or unusually successful clinical trial.
Mukhopadhyay thanks Brad Efron of Stanford University, for inspiring him to investigate this problem. “It took me one and a half years to come up with the right question,” says Mukhopadhyay. “I believe Bayes and Frequentist could be a winning combination that is more effective than either of the two separately in this data science era.”
*This article corrects an earlier version by specifying that the research was published in Scientific Reports, a journal by Nature Research.