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Educators all over the world are currently facing a common challenge: How do we teach in the new COVID-19 environment?
One thing we have always had on our side as educators has been time. We have time to research the topics, to research pedagogical approaches, to design courses we will teach in future way in advance. We have time to think, to deliberate, to arrive at optimal solutions. However, with COVID-19, we have suddenly been thrust outside the old world we knew. Those who taught during the Spring semester know and appreciate how mid-way through the semester, instruction was moved completely online. You wanted more time to plan and design the online experience for your students? Well, too bad. Those of us (including me) who are teaching in Fall 2020 have more time but also a lot more uncertainty. Will classes be held purely online? Will there be an in-person element? What would a blended/hybrid course look like? By some estimates, 65% of colleges plan to re-open for the fall. However, "re-opening" will look drastically different in the new world. Colleges are already discussing how to enforce social distancing norms and masks in classrooms, with fair concerns surrounding their success in practice. Given the uncertainty for Fall 2020, what can you do as an instructor to be prepared to the extent possible? I have been in online education for nearly a decade now. I have worn many hats during this time: A high school teacher, course designer and marketer for online exec ed courses, a doctoral student, and now an assistant professor of marketing. While these many roles silently prepared me for the challenges we face today, there are no definitive answers right now. So, I've been asking peers and mentors. I have been enrolling myself in online courses. I've been talking to elearning teams, videographers, and experts at Coursera. I've been participating in an intensive 3-week program offered by the U of I elearning team. What am I learning about teaching in the online space? 1) Learn and own the tech: Campus elearning teams are swamped! In the ideal world, I would walk into a studio and deliver my content. The videographer will film it. Someone on the team will work on post-production and someone else will put it all together. Voilà! My online course is up and running. The reality is these teams are swamped right now. Consult them, ask them specific questions, show them stuff and get an opinion. Other than that, learn and own the tech. Here are some more tips from Coursera on how to record home videos. I invested in inexpensive tripod and lapel mics. It also helps to record shorter videos segments in fewer cuts so post-production is easy and at the same time, the content is nicely chunked and organized for students. 2) Survey other courses: I have found it super helpful to talk to instructors who have been successful at teaching online (even in the pre COVID era) and surveying their courses. These courses can be MOOCs or online LMS for degree courses. These courses can be within your discipline or beyond. When you survey them, think like a designer. Reverse engineer it. Look for elements that you would never have thought of but that can be central to learning in a highly distracting online environment. For example, my colleague Aric Rindfleisch teaches a popular Coursera MOOC on digital marketing that offers a practice quiz at the end of each module in addition to a graded quiz. These practice quizzes can serve as a diagnostic tool for the instructor as well as a self-assessment tool for learners. Similarly, economics professor Jonathan Meer at Texas A&M University and his co-instructor in their online course have a full-fledged course orientation module #0 setup as a pre-requisite. Course LMS should be super intuitive, right? But as he points out in some of their videos, students can often miss a critical link to the module videos, central to their learning and not notice it as half the semester goes by. Jonathan summarizes his key lessons from online teaching in this video I HIGHLY RECOMMEND on how to set up an online course. Organize, prepare, plan your scripts, include a co-instructor when possible, use other forms of digital media including podcasts or videos to break the monotony. Host at least a few live zoom sessions with your students so they can see you are a real human being. 3) Include and make LIVE interactions fun: There is value in incorporating a few live sessions over and above the asynchronous videos. Whether these are online or in-person, plan these well and incentivize students to show up and participate. One of the best ideas I have received on garnering engagement during these online live sessions came from Elizabeth Luckman, Clinical Assistant Professor of Business Administration at Gies. Elizabeth opens a shared google doc that groups of students simultaneously work on in breakout rooms in zoom. In her own words: "I did a “reflection exercise” at the beginning of the live session. I had four reflection questions I wanted students to address, and I created a google spreadsheet with the same number of tabs as groups. Then I put them in breakout rooms and in those rooms, they had to type answers into the doc. So that means that even though they were in breakout rooms, I could see (and they all could see) what each of the other groups was doing. I had never done it before but it worked really well! The crowdsourcing doc that I showed is live all the time, and they can add to it at any time. My goal is that they can use that information for their final projects - and also just have this cool resource that they can download and save after the class ends. Keeping discussion going during live sessions is rough. My experience is that smaller numbers of students in live sessions, the google doc during breakout groups, and (sadly) incentivizing discussion is sort of the right mix." To better teach online, we must first learn to learn ourselves. Overall, don't fret! Accept that teaching in the new world will look different. Accept that it will require unlearning and relearning, and higher investment of time and energy. Be patient with yourself but open to learning new tools and techniques. Try to place yourself in the shoes of your students and wondrous new insights will emerge!
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 Five years. 2015-2020.
When I started my PhD five years ago, like any new incoming student experiencing the academic world for the first time, I had naive expectations about research and doctoral education. In this article, I share my own experience and challenge five myths new students often have about getting a PhD: 1) It is all about grand ideas. Grand ideas are a good start. However, execution of a few good ideas is what gets you through. When I started my PhD in Marketing, I had the grand vision of researching how the mobile-first world impacts consumers and firms in different domains, including retail (how we shop), education (how we learn), and healthcare (how we access health services). Many first year PhD students come in with a grand vision and think they must pursue it all. When defining the scope of my doctoral research, it helped to keep three things in mind:
2) The dissertation is the be-all-end-all of my PhD research and must have lasting impact on the field. There are differing perspectives and philosophies on what a dissertation is and should contain. I personally found it helpful to conceptualize my dissertation as a set of related papers that together fairly represent my broad interest areas, demonstrate my empirical skills, and introduce sufficiently new ideas or insights to an existing body of work. Often, PhD students get caught up in the "impact" trap -- where we want to create impactful research right away. Consequently, no manuscript or draft seems "good enough." The danger is a resulting research paralysis, a kind of a "writer's block" that prevents progress. Along my PhD journey, lots of people gave me good practical advice that helped get my first drafts done. Three senior academics at different times told me:
3) Unless I go to a top school or find a well-known advisor, it doesn't count. Incoming new students often place tremendous weight on top schools and popular advisors. While these factors can help, ultimately your peers, colleagues and recruiters are all looking for signals about YOU. Who are you independent of your advisor or school? What do you care about? Have you demonstrated research acumen, perseverance and initiative? Over the years, as I have seen PhD students who entered a PhD program only to find their advisor leave their job at the university or department management and budgets severely changed, this perspective may be a good reminder to take ownership of their projects despite all odds as they work to improve their circumstances. 4) Unless my advisor contributes, I cannot make progress. Let's face it. Advisors are busy. They are constantly juggling teaching, traveling, multiple projects, and sometimes, even consulting. Waiting for your advisor to do the work for you is dangerous even if they have the best of intentions. Do your part. Do as much as you can. Go out there and find the help you need -- online, offline, at the library. Most of the problems you are waiting on your advisor to solve for you may have been solved before. Take your best solution to your advisor instead of just the problem statement. You will be surprised how much faster things are able to move. 5) More number of manuscript will help me succeed. Advanced and finished projects with a fair pipeline are much better than a bunch of loosely connected works-in-progress or working papers. Prioritize prioritize prioritize. Yes, we all need some "fun" projects and starting new projects is always fun. As they age and get further along in the review process, projects tend to become drudgery for anyone. That's the time to push through. Instead of constantly starting new projects, finish the ones you start. Every research has weaknesses and the review process is geared to bring them up. Preempt them. Acknowledge them. Fix them to the extent possible. Turn those manuscripts back in. If you are going to get it rejected, might as well find out sooner than later. Finally, along the way, have fun! Make friends. Explore your campus. Reach out to people. Talk to people at conferences. Talk to people outside your field. Ask lots of questions. Help others. The journey is so much more memorable that way. Best!  Receiving the 2019 PDMA Emerging Scholar Award (1st place) w/ Nadia Danienta of UIUC The PDMA (Product Development and Management Association) Doctoral Consortium provides doctoral students an opportunity to connect with a community of innovation researchers and scholars. The 2019 Consortium was hosted by the Gies College of Business in August. This year, 24 Ph.D. students and 22 faculty attended. The event sparked several interesting conversations on research in innovation as well as perspectives for young scholars to manage their academic career and research program well.
Source: https://www.pdma.org/news/466705/PDMA-Doctoral-Consortium In several product categories, such as electronics, video games, computer hardware and software, and other hi-tech products, backward compatibility feature--the property of a current generation of hardware to allow previous generation of software or accessory to work with it--is an important strategic decision for firms introducing hardware upgrades. We empirically investigate the effect of Microsoft Xbox’s decision to make its new generation console (NGC, Xbox One) backward compatible with selected games for its previous generation console (PGC, Xbox 360) on sales of video games for both PGC and NGC. We assemble a unique dataset using data from a large proprietary game retailer and data scraped from gaming sites during 2013-2017. We analyze the effects using a difference-in-differences approach that includes the use of a synthetic control group. Our results show that when a video game console firm makes its NGC compatible with PGC games, unit sales of PGC games do not change. However, dollar sales of PGC games increase due to a price increase effect. Importantly, sales (units and dollars) of NGC games increase due to a spillover effect, driven primarily by console upgrades. The increases in dollar sales of PGC games result from greater sales of classics, sequels, and first-person shooter genres than sales of non-sequel and other genres of games. Our research offers managerial insights on the backward compatibility feature decision in product upgrades.
(Complete working paper with @Venky Shankar available on request) Over half of all shopping journeys start with the mobile channel. In particular, the presence of a branded mobile app significantly influences shopping across channels. However, a majority of app users decrease app usage or even abandon an app, in part, due to app service failure(s). Do app failures influence purchases made within the online channel? Are there any spillover effects across other channels? What factors moderate the within and across channel effects? We leverage exogenous systemwide failure shocks in a large multichannel retailer’s mobile app and related data to examine the impact of app failures on purchases in all channels using a differences-in-differences approach. We investigate heterogeneity among shoppers using a set of moderators of these effects based on insights from prior research. Our analysis reveals that although app failures have a significant overall negative effect on shoppers’ frequency, quantity, and monetary value of purchases across channels, the effects are heterogeneous across channels and shoppers. Interestingly, the overall decreases in purchases across channels are driven by purchase reductions in brick and mortar stores and not in digital channels. Furthermore, we find that shoppers with a stronger relationship with the retailer, greater digital channel use, and who experienced failures less attributable to the retailer, are less sensitive to app failures. We outline failure preventive and recovery strategies for app providers based on the insights from this study.
Download my latest working paper with Dr. Venky Shankar and Dr. Sridhar Narayanan on this link.
1. Mobile:
2. AI and robotics
3. Search and Segmentation
4. Pro-social and gift-giving behaviors
This code uses the MatchIt package for propensity score matching to demonstrate with and without replacement Nearest Neighbor matching. The additional extension it offers is to create panel data using matched sample in both instances (particularly non-trivial for with replacement matches).
There isn't an easy way to export messy R output for regressions into a usable table. You're probably okay manually doing this for 1-2 regressions, but what if you have to estimate hundred of models for different Ys (dependent variables). This code allows export of regression results produced from regressing different Ys ('outcome list') on a set of Xs into a usable CSV file. You can flexibly incorporate clustering, fixed effects, etc. as needed in the code. Happy regressing!
 Intense retail competition has led old standbys, such as Sears, to close dozens of stores. Walmart is venturing online more. And Amazon is expanding offline, opening stores and buying Whole Foods. The fight for retail dollars is fierce, and the battleground will soon migrate into the palms of customers’ hands – via apps on their smartphones. This isn’t just happening with mega-retailers. Movie chains and pet supply stores are increasingly connecting with their customers through their own branded apps. Zumiez, a specialty clothing chain with 600 stores in the U.S., has an app. Scooter’s Coffee, an Omaha-based coffee chain with 200 stores, has one too. So does New York Pizza Oven, a single pizza parlor in Vermont. Mobile apps are becoming key ways for customers and retailers to interact. Our recent analysis of data from a large U.S. retailer of video games and electronics (whose name we agreed to keep confidential) found that apps can even affect consumers’ offline buying habits. Growth in use – and spending The number of people who have the option to use mobile apps is skyrocketing. More than 70 percent of the world population will own a smartphone by 2020. And they’ll spend more than 80 percent of their on-phone time using task-specific apps. Is there no line because people are ordering ahead on their mobile phones? Letting buyers learn about products, discover deals, locate nearby stores and even place orders in advance is a huge business opportunity. At Starbucks, for example, an app allowing people to order and pay on the go – just swinging into the store for pickup – helped customers avoid standing in line and waiting: Over five years, 20 percent of its sales shifted to online transactions. Research has also begun to show that people who use mobile shopping apps buy more than they might otherwise. After individual shoppers started purchasing using eBay’s mobile app, their purchases from eBay’s website increased. Similarly, a tablet app from major Chinese e-tailer Alibaba led customers to spend about US$923.5 million more each yearwith the company than they would have without the app. Some of that increased spending is from shoppers using the app to buy impulsively – making one-off purchases of items they are interested in, or adding items to larger orders. Our research recently found a new dimension to this app-related spending boost. Over 18 months, customers who downloaded the branded app of the retailer we studied spent 30 percent more in stores than they would have without the app. We can infer this by looking at data on customers’ spending before and after the app was installed, and by comparing that to the spending of a random sample of customers who had similar demographics and shopping behavior before the app launched. We learned that most of the increase was because customers used the app to find out about products before buying them. For example, by closely analyzing the data on app use and purchases, we could see these customers started increasing purchases of lesser known video games when they started using the app. App users return products more While shoppers who use a retailer’s mobile app tend to buy more online and in stores, we find that they are also more prone to subsequently returning the products they purchased. In particular, customers who use a retailer’s app tend to return products most often when they purchased those products on discount, and within seven days of making the original purchase. Apps often make it easier to purchase items on impulse. When customers receive some of the items and are dissatisfied, they regret the decisions and return the items. Even taking into account the high rate of returns, app users spend more both online and in physical stores. But that’s when the apps work as customers expect them to. App failures –- and consequences Apps that load information slowly or crash frequently can deter not only online purchasing, but in-person spending, too. Surveys show that more than 60 percent of users expect an app to load within four seconds. And our ongoing research suggests that more than half of users will abandon an app that freezes or crashes frequently. App slowdowns can be costly. One estimate suggests that if each Amazon webpage took just one second longer to load, the company’s sales could drop as much as $1.6 billion a year. For smaller retailers, a similar drop of 2 to 3 percent would be a smaller dollar amount but still a significant blow. Our ongoing research with Stanford’s Sridhar Narayanan suggests that poor app performance reduces users’ in-store spending too. Specifically, we studied how shoppers react when an app is not accessible for five or six hours, due (users were told) to a server error. Our preliminary results suggest that in the following two weeks, those shoppers spent 3 to 4 percent less in stores than they would have otherwise. Less-frequent customers reduced their spending even more than the company’s more regular shoppers. Unnati Narang discusses her ongoing research on failures in mobile shopping apps.Interestingly, customers who experience app failures spend less in stores, but their online spending remains unchanged. A deeper analysis indicates that when a retailer’s app fails, shoppers often go to the retailer’s website to complete their intended transactions. But the negative experience from app failure discourages them from buying more in the retailer’s store. Our research illustrates some ways mobile apps can be a double-edged sword for customers and retailers alike. Shoppers can use apps to learn more about prospective purchases, be inspired on the fly and save time at the cash register. But if the software fails, they may be frustrated, discouraged and even spend less at physical stores. Retailers can see increased sales and faster transactions, but may have to handle more returns – though they’ll still make more money. The longer-term effects of mobile apps on the retail business have yet to be seen, of course, but in an ever-changing landscape, companies and customers alike will be exploring the options. (written with Dr. Venkatesh Shankar, Mays Business School; originally published on ConversationUS)  Let's face it. The hype around big data and machine learning (ML) can be intimidating for noobs. Particularly if you have little background in statistics and/or computer science, you may find this complex territory difficult to navigate. You may wonder how to go about building your toolkit in this space. Where do you even begin?
As a marketing major with no programming background until 3 years ago, I shared these concerns before I decided to chart out my own learning path. In this post, I will share quick and helpful tips that I have found useful for getting started in ML. Specifically, I will share my experience with three online course specializations that can be taken via Coursera.org. Exposure, exposure, exposure: What advice what you give to an aspiring swimmer who is afraid to get into the water? Get into the water! Surrounding myself with machine learning conversations and content helped break the ice. A few things to consider:
Structure, structure, structure Assuming you are now convinced that you want to be more than just an observer to exciting ML developments, it's time to add some structure to your learning. A convenient but effective way to do this is to first find resources that already have some structure. Online courses are my go-to tool. Just like any self-guided learning, they require discipline and dedication. I have been on both sides of the coin -- both an online learner and provider. Two useful tips are to (a) set aside time each week, particularly binge during weekends, and (b) get the Coursera app on your smartphone, at least to stay on top of video lessons. Finally, here are three sets of online courses that I found most useful and why. Together, when taken in the order below, you can get a lot out of these courses:
Of course, here I have assumed you have all the time in the world to take these courses for a great DIY learning experience. If you are facing a time crunch, I'd recommend the Machine learning specialization from University of Washington. Final tip from a grad student: You can apply for financial aid and save a few hundred dollars via individual course pages. This option is not available if you try to sign up for the entire specialization. Good luck! |
