Resistance Challenges to MB Services in Developing Nations

In my blog post today, I will be looking at an article by Lakshmi Mohan and Devendra Potnis that examines and discusses the implication of mobile banking for the rural poor populations of India. My decision to critique this article in particular is based only two factors: 1) An interest in banking applications in their functionality, usage, and perceived trustfulness; 2) To learn more about the topics as I have previously read and heard some information on the circumstances of the rural poor of India and their difficulties faced when handling financial transactions in these environments.

Within this article, the authors present a study examining and comparing three companies that are currently in this market of India. The authors at the conclusion of this study provide a detailed response to the findings and discuss their impact. Their argument is primarily in favor of the systems operated by these companies in serving the poor, and they encourage other IT professionals to enter the mobile banking markets of, not just India, but other developing nations as a space for significant financial gains in its latent market opportunities (Mohan, 2015).

In a study in 2016 by Onneile Juliet Ntseme, Alicia Nametsagang, and Joshua Ebere Chukwuere, the authors found similar evidence to Mohan and Potnis concerning the positive impact ease of use and convenience has on the actual usage of mobile banking opportunities and applications (Ntseme, 2016, p. 363). In the paper by Mohan and Potnis, the authors argued that “door-step banking helps customers by serving them at a convenient location and time…” (Mohan, 2015). In order for mobile banking to serve a purpose in the community presented by Mohan and Potnis, the applications/opportunities must be usable formed in such a way that prospective and ongoing customers of these services may utilize them accessibly. Regarding this factor, IT companies that enter this market have the opportunity to develop the applications for use by “door-step” finance agents—this is in-part the “latent market opportunity” Mohan and Potnis are referring too (Mohan, 2015).

In a published work by Rodrigo F. Malaquies and Yujong Hwang in 2016, the authors completed a study in a similarly developing nation to India—Brazil—to examine the relationship between trust and the adoption of mobile banking technologies. Their findings discovered that both banks and customers are negatively affected by low levels of disclosure about the security on mobile banking (Malaquies, 2016). In other words, the adoption rates among potential customers may be heavily impacted by their perceptions regarding the security of the technology; where younger people—who made a greater effort to inform themselves on the technology—readily trust these new technologies, information disparities results in population groups who did not trust mobile banking options (Malaquies, 2016). In some respects, these findings complement the evidence of Mohan and Potnis as they argue in their work that, because the majority of customers in this area are poor and illiterate to semi-literate in India, companies must “recruit, train, and retain a sales force of human agents” (Mohan, 2015). These customers are extraordinarily dependent on the proficiency of the finance agents, and thus, companies must build a trusting relationship between their agents and the customers. Lastly, by recruiting local agents as Mohan and Potnis indicate, loyalty to different mobile banking companies could develop out of the formed trustful relationship (Mohan, 2015, p. 2175).

On the flipside of the trust argument, companies who are unable to build trust with their clientele will no doubt suffer financially as a consequence. The study by Malaquias and Hwang, as previously mentioned, discovered the negative impact limited information disclosure by banking companies can inhibit the adoption rates among prospective customers (Malaquias, 2016). Tech companies that enter this space must ensure information regarding their technology is well-disclosed and accessible to their potential customers to develop credibility and trust within the populations of developing nations. In a study of Taiwan by ChauShen Chen in 2013, the author argues that the communication methods of banks should be “compatible” with mobile banking styles of potential users (Chen, 2013). In other words, the mobile banking companies and organizations should disclose information in a manner its potential and current customers can access and easily understand. In regard to the piece by Mohan and Potnis, one could argue that the presence of “door-step” finance agents is a positive variable for this factor as these agents may readily and directly disseminate information to the customers. However, this could result in the agent becoming less efficient—focusing more on information dissemination than actual service.

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References

Aijaz A. Shaikh, Heikki Karjaluoto, (2015). Mobile banking adoption: A literature review, Telematics and Informatics, Volume 32, Issue 1, 2015, Pages 129-142.

Chen, C. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality,23(5), 410-436.

Malaquias, & Hwang. (2016). An empirical study on trust in mobile banking: A developing country perspective. Computers in Human Behavior, 54, 453-461.

Mohan, Lakshmi, Potnis, Devendra, (2015). Mobile Banking for the Unbanked Poor without Mobile Phones: Comparing Three Innovative Mobile Banking Services in India. System Sciences (HICSS), 2015 48^th Hawaii International Conference on System Sciences, 2168-2176.

Onneile Juliet Ntseme, Alicia Nametsagang, & Joshua Ebere Chukwuere. (2016). Risks and benefits from using mobile banking in an emerging country. Risk Governance & Control: Financial Markets & Institutions,6(4), 355-363.

Privacy and User Agreements of Social Media Platforms

For tonight’s blog entry, I elected to look at an article by Amy B. Wang on the relationship between user agreements and privacy rights, specifically for teenage users. As someone who holds online privacy in high regard, this article felt natural to examine further. Additionally, this is an issue that has mostly been swept under the rugs over the past decade as user agreements have grown ever more complex and dense to understand for the average end user.

The argument Wang makes in her piece is that platforms should write their user agreements in language that is easily accessible to all age groups utilizing the platform substantially. As teenagers make up a large portion of Instagram’s users, they clearly meet the latter requirement (“NORC…,” 2017). While this discussion primarily centers around Instagram, the author would likely argue these circumstances are arguably similar to other social media applications in high use by teenagers.

Unfortunately, the reality is that use of social media often implies the giving up of many privacy rights, some of which are unknown to the end users. A previously entrenched belief that has begun to ebb away over the past five years is the idea that teenagers do not care about their privacy when it comes to social media (Boyd, 2014). Indeed, at the end of Wang’s article, a few teenagers mention that they will stop using Instagram simply from realizing how much of their privacy they were giving up—whether they followed through on these remarks is another matter. However, Wang’s argument implies that the complexity of the user agreement inhibits teens from seriously considering their privacy on social media platforms. In other words, the platform itself is responsible for ensuring users can understand user agreements in a more accessible manner, most simply through writing their user agreements comprehendible to the average American teenager.

The privacy concerns of modern social media platforms are significant and require careful reading of user agreements to fully comprehend what you are giving up as a user of the service. In the case of Instagram, while users are considered to hold ownership of the picture they post, Instagram is given privileges to hosting and sharing the images beyond your original scope without your direct notice ((Thompson, 2015). Additionally, removal of one’s account does not necessarily mean the complete removal of any images the user has previously posted; if someone reuploads the image via different account, Instagram does not actively remove the content (Thompson, 2015).

Over the years, millennials have been ingrained with the practice of ‘agreeing’ instinctively with the user and license agreements either out of impatience or frustration with the complexity of the agreement’s writing. Privacy lawyer Jenny Afia, whom Any B. Wang speaks with, says herself regarding user agreements, “‘…of course, no one reads them. I mean, most adults don’t read them’” (qtd. by Wang, 2017). A study done by Jonathan Obar of York University and Anne Oeldorf-Hirsch of the University of Connecticut did a study in early 2017. Using a sample of 543, the authors confirmed themselves similar findings: “nobody reads online contracts, license agreements, terms of service, privacy policies and other agreements (Berreby, 2017). In other words, if people even tried to read these user agreements, the users would derive more understanding than reading nothing at all. Blaming the platform for peoples’ decision to not read user agreements seems a major shift in responsibility from being personal to an entity.

Personally, while I agree that user agreements should be written in a more succinct form, users are ultimately responsible for what they agree to. Click “I agree” on a website’s agreement of service is virtually no different to signing your signature on a physical document of importance—only that clicking a button on a computer screen requires less ‘effort.’

 

References

Berreby, David (2017, March 3). Click to agree with what? No one reads terms of service, studies confirm. Retrieved 3/6/2018 from https://www.theguardian.com/technology/2017/mar/03/terms-of-service-online-contracts-fine-print.

NORC at the University of Chicago. (2017, April 21). New survey: Snapchat and Instagram are most popular social media platforms among American teens: Black teens are the most active on social media and messaging apps. ScienceDaily. Retrieved March 6, 2018 from www.sciencedaily.com/releases/2017/04/170421113306.htm.

Thompson, Cadie (2015, May 20). What you really sign up for when you use social media. Retrieved 3/6/ 2018 from https://www.cnbc.com/2015/05/20/what-you-really-sign-up-for-when-you-use-social-media.html.

Wang, Amy B. (2017, January 8). A lawyer rewrote Instagram’s terms of use ‘in plain English’ so kids would know their privacy rights. Retrieved 3/6/2018 from https://www.washingtonpost.com/news/parenting/wp/2017/01/08/a-lawyer-rewrote-instagrams-terms-of-use-in-plain-english-so-kids-would-know-their-privacy-rights/?utm_term=.8c89c1105ed5.

Machine Learning in the Workplace

For my entry today, I chose a fascinating article by Kaveh Waddell of the Atlantic that discusses machine learning and algorithmic approaches to employee evaluation in the workplace. Similar to my decision last semester to choose Big Data applications in the management of organizations in my INSC 560 course (Management of Information Organizations), my choice of article reflects an interest how technology is revolutionizing human resources in the corporate workplace.

Kaveh Waddell’s argument with this article is that machine learning applications, specifically in the blossoming field of sentiment analysis, will have a growing presence in the professional workplace, particularly of larger companies and organizations. His discussion within the work is well-balanced, offering both a positive and negative spin on the technology, but ultimately in a positive-neutral light.

Sentiment analysis—the primary focus of this text—is merely a subfield of the much larger technology industry developing around machine learning (aka Big Data) applications. As Waddell points out, the subfield of sentiment analysis developed from marketing-analytics of market research (2016). These days, however, the technology surrounding sentiment analysis is becoming increasingly turned inward towards examining the behavioral habits, emotions, and communication of an organization’s employees (Waddell, 2017). This has clear privacy concerns that are, unfortunately, not addressed virtually at all in Waddell’s article and must be discussed in further detail here.

In a chapter titled “From Data Privacy to Location Privacy” from the text, Machine Learning in Cyber Trust, chapter authors Ting Wang and Ling Liu present an in-depth analysis of the privacy concerns growing with machine learning and the many approaches and research that has taken place to address these concerns.

The data collected in machine learning applications in relation to privacy concerns are generally classified into three categories: identity attributes, quasi-identity attributes, and sensitive attributes (Wang, 2009, p. 217). “Identity attributes” refers to information that directly identifies an individual (e.g. social security number, voter-registration, etc.); “quasi-identity attributes” is information that when culminated may result in discovering sensitive information or directly identifying an individual (e.g. zip code, address, place of employment, etc.); “sensitive attributes” is typically information an individual wish to keep private for personal reasons (e.g. health concerns, criminal records, etc.) (Wang, 2009, p. 220). To prevent privacy breaches, theoretical models typically take one of two approaches: a) developing a model that aims to limit data measuring based on a set of criteria that prevent the information from ever being aggregated; b) developing a data manipulation / transformation method that can filter sensitive information of a data set before its publication (i.e. the data is collected but then filtered) (Wang, 2009, p. 218). Thus, to prevent sensitive information from being uncovered by organizations or individuals with malicious intent, the organizations must develop a model that protects the privacy of the individual within the legal requirements of the law and Constitution.

Just as there are privacy concerns, the benefits of machine learning algorithms cannot be understated. As Waddell’s article implies with case examples, policy changes that can take upwards of many months may take place, instead, in real-time (Waddell, 2016). Additionally, employee grievances that might not normally appear during routine evaluations may become evident through analytics that monitors the employee’s behaviors (Waddell, 2016).

In the traditional sense, machine learning “aids knowledge workers and customers alike” (Petrocelli, 2017). The means that it aids vary according by application, however, a common theme is the reduction of errors. As Petrocelli points out in his article, errors made by customers can throw them into longer queue lines for a simple issue, and employees can produce drastic errors that put the business at risk (2017). By distilling massive quantities of information into a more meaningful and manageable form that may be used in decision-making by managers (Petrocelli, 2017).

There are clear pros and cons to both sides of the argument regarding the use of machine learning technologies towards employees. Privacy concerns will always need addressing if such technologies are to become common in the workplace, otherwise, company policies will clash in the court system for years to come.

 

References

Waddell, Kaveh (2016, September 29). The Algorithms that Tell Bosses How Employees Are Feeling. The Atlantic. Retrieved 2/26/2018 from https://www.theatlantic.com/technology/archive/2016/09/the-algorithms-that-tell-bosses-how-employees-feel/502064/.

Wang, Ting, Liu, Ling (2009). From Data Privacy to Location Privacy. In Yu, P., Tsai, Jeffrey J. P. (Eds.), Machine Learning in Cyber Trust: Security, Privacy, and Reliability 217-246. Boston, MA: Springer-Verlag US.

Petrocelli, Tom (2017, October 2). When Machine Learning Benefits Employees and Customers Alike. Retrieved 2/26/2018 from https://www.cmswire.com/customer-experience/when-machine-learning-benefits-employees-and-customers-alike/.

Perscription Drug Prices in India compared with the U.S.

Today my article will revolve around an ongoing issue here in the United States in the form of overpriced medications. Although I consider myself knowledgeable in this area to an extent from personal health experience, this article still provided fruitful information for those who find themselves in this dilemma.

Rema Nagarajan’s purpose with this piece is primarily to make readers aware of the importance India’s pharmaceutical industry is in the world, particularly in relation to the crisis in the U.S. The author argues that the industry is able to produce “affordable, high-quality medicines” because of India’s laws, which prevent the occurrence of “patent monopolies” as it the situation in the U.S. (Nagarajan, 2016). What differs India’s patent laws regarding medicine from western countries, particularly the U.S., is the stringent requirements on what is considered innovative. Thus, as patents are much more difficult to obtain by pharmaceutical companies, there is a much larger degree of competition to bring down the prices for consumers.

The author, Rema Nagarajan, is particularly unnerved by the fact that international pharmaceutical companies are beginning to apply pressure to India’s courts to be more lenient towards these laws (Nagarajan, 2017). Plainly, the international corporations want to protect their markets from the cheaply made generic medications from countries such as Brazil and India (Nagarajan, 2017).

In my own personal experience in this area, I have sought out alternative treatment options in the past for Crohn’s Disease as the previous treatment undergone costs about $8,000 every eight weeks. This same medication, however, costs far less overseas as generic biosimilars of the drug are beginning to pop up around East Asian countries (“Biosimilar Players…,” 2017). My hope is that the U.S. will eventually move towards a form of national healthcare support as in other Western nations (e.g. Canada, U.K., etc.).

In a study performed by Dana O. Sarnak, David Squires, Greg Kuzmak, and Shawn Bishop, in October 2017, the author came to the conclusion that the primary reason for U.S. consumers facing high drug prices is due to “particularly high out-of-pocket costs,” which are a result of the U.S.’s “large uninsured population” and a burdensome “cost-sharing requirements for those with coverage” (Sarnak, Dana O., 2017). It is important to note, however, that over the past three decades the U.S. government has stepped in in certain sectors to boost the financial coverage of prescription drugs; examples include, the “Children’s Health Insurance Program” (CHIP), “Medicaid,” and “Medicare” (Sarnak, Dana O., 2017). Additionally, the U.S. actually has one of the world’s largest generic drugs market, which makes up make about “84% of the total pharmaceutical market” in terms of utilization (Sarnak, Dana O., 2017).

At this time, Rema Nagarajan’s work bringing the importance of India’s generic drug market to the forefront of this issue is beneficial to consumers around the world. Until legislation is passed in the U.S. to similarly discourage patent use in pharmaceuticals, the issue of high drug prices will likely remain in the U.S. for many more years.

 

References

“Biosimilar Players seek Specialty & Niche Space in Japan (2017, April 12). Retrieved 2/25/2018 from https://www.biosimilardevelopment.com/doc/japan-biosimilar-active-players-in-quest-of-a-specialty-niche-bs-space-0001.

Nagarajan, Rema, (2016, February 7). The Pill that Costs $9,000 in [the] US sells for $70 in India. Retrieved 2/25/2018 from https://timesofindia.indiatimes.com/home/sunday-times/deep-focus/The-pill-that-costs-9000-in-US-sells-for-70-in-India/articleshow/50883406.cms.

Sarnak, Dana O., Squires, David, Kuzmak, Greg, Bishop, Shawn, (2017, October 5). Paying for Prescription Drugs around the World: Why is the U.S. an Outlier? The Commonwealth Fund, October 2017.

 

A.I. in the Modern University Classroom

“Imagine how great universities could be without all those human teachers” by Allison Schrager and Amy X. Wang

For my post this evening, while there were many great articles to choose from, I decided to look at an article found on Quarz.com by Allison Schrager and Amy X. Wang that discusses the implication of artificial intelligence (AI) in the university setting, specifically operating in the role of a teacher and assistant.

The main argument both authors present is the belief that AI could one day substantially reduce the budgetary costs at universities and college by replacing much of the institutions’ human staff with AI, capable of completing the same measure of work (or better). I agree with the argument to an extent. While I believe AI can and should become more applicable to the university setting, the idea of machines completely replacing professors, especially those of the liberal arts, seems somewhat absurd at this stage in the development of AI.

In presenting supporting evidence, the article by the authors does provide some convincing evidence worth noting. First, it has become apparent that college degrees have become the “standard entry point to jobs,” forcing modern high school students to often pursue college simply for filling a box on an application or resume (Schrager & Wang, 2017). Additionally, despite that student populations are higher than ever before in the U.S., over half of university students complete their four-year degrees in four-years or less nowadays (Schrager & Wang, 2017). As students drop their admission to institutions for a variety of circumstances or reasons, AI offers insight into the data analytics of student performance—able to spot which students may soon drop out of their program (Schrager & Wang, 2017).

The second element presented in the article in favor of their argument is in the human-machine interaction between the AI teacher’s assistant “Jill Watson” and the students of the class. The realization that—for a period—students were unable to notice who they were speaking to was not human, paints an incredible example in the rate of advancement AI has undergone in the past decade (Schrager & Wang, 2017).

As a final testament to the benefits AI may place in higher education is the ability for AI to reach more people beyond the traditional classroom. Ashok Goel mentions that “about half” of the world’s population does not “have access to [a] good education” (Schrager & Wang, 2017). Although AI may not reach the capabilities of a real professor, the opportunity for people around the world to have access to a decent education is worthwhile.

There are two major arguments critics hold that contest the authors’ thesis. First, higher education is simply not prepared for, nor fully understands the impact AI may have on the higher education setting. Dan Shewan’s article, “Robots will destroy our jobs…,” in The Guardian presents the more ominous effects of automation, even if not strictly within the higher education space. Approximately “two-thirds of Americans believe that robots will inevitably perform most of the work currently done by human being [in] the next 50 years” (Shewan, 2017).  While robotics has started primarily in the low-skill job market, as machine-learning continues the develop the abilities of modern AI, medium-skill tasks performed by secretaries, janitorial staff, student assistance, and tutors will gradually occur, leaving hundreds-of-thousands of people with reduced opportunities to find employment without acquiring new training, skills, and possibly, more higher education learning (i.e. earning another bachelor’s degree).

The second major argument against overuse of AI, especially regarding the idea of replacing professors, is the factor of destroying “inspiration” in the classroom and university setting (Schrager & Wang, 2017). Further, evidence of a breakthrough of using AI in the classroom is “limited;” the tech has not been adopted in enough university settings to make conclusions of its effectiveness or actual role in the classroom of higher education (Schrager & Wang, 2017).

 

References

Schrager, Allison, & Wang, Amy X. (2017, September). Imagine how great universities could be without all those human teachers. Accessed at https://qz.com/1065818/ai-university/

Shewan, Dan, (2017, January). Robots will destroy our jobs – and we’re not ready for it. Accessed at https://www.theguardian.com/technology/2017/jan/11/robots-jobs-employees-artificial-intelligence.

Blog Post #1 – Social Media ROI

For my blog entry today, I chose the article published by Oracle titled, “Measuring Social Media ROI in the Enterprise: Myths and Facts.” My decision is based primarily on two factors. First, I am less familiar with the corporate context_­­­ (e.g. the acronym ROI was completely unknown to me). Second, I am somewhat interested in the financial benefits social media can reap for companies.

The argument presented in this article is, simply, that the benefits outweigh the costs regarding transitioning to utilizing social media applications from a previously underused state. Many of these benefits have practical financial returns as well (e.g. attracting new customers and employees, improving customer relations). Additionally, failure to begin utilizing social media effectively in business practices, namely marketing, may result in falling behind competitors.

The argument made by Oracle is based primarily on their in-house data metrics and experience in this growing area of corporate marketing. One benefit of adopting a social media strategy is a massive array of data metrics available from most social media applications/sites (e.g. Facebook, Twitter, YouTube, etc.) p. 3). As stated within the piece, “data coupled with strong analytics capabilities save time and money by helping the organization make better decisions…” (p. 3). It is common to make the best decisions possible based on the available information; therefore, social media offer many data sets that can give insight.

In her article, “Social Media: Measuring the ROI,” Lisa Montenegro of DMX, a “premier Google Partner Agency located in Toronto, Canada” (p. 1), discusses the importance for businesses to take social media marketing seriously—before they get left behind. As she indicates in her piece, the ROI of social media lies based on how much marketing budget is available, and how much of that budget should be reallocated to social media marketing and customer-support (p. 1). A practical benefit regarding budgetary concerns is on social media’s ability to be tested and analyze on a small scale before moving to larger staff involvement. Nuria Lloret Romero’s published an article in 2011 examining the nature of social media ROI in relation to libraries. The author concludes with a major point: social media offers a highly accessible and relatively easy method for libraries to increase the “visibility of the institution and improve its service and its users’ experiences” (p. 151).

There are, however, counter-arguments to be made against large-scale resource allocation to social media applications. In “Busting the Myth on Social Media ROI,” Gretchen Fox’s argument supporting social media applications ironically presents several of the major problems in the context. First, many in the business and corporate community, even in marketing, remain unexperienced in interacting with customers outside formal contexts (e.g. support emails, physical store, office phone calls, etc.); on the flipside, customers have only started feeling comfortable interacting with (and complaining too) staff of business and organizations on social media sites (Fox, 2016, p. 1). The consequence of these circumstances requires companies to front costs associated with training staff to use these new modes of communication without full awareness of its potential for the company. Additionally, “it must be customized for each business” (Fox, 2016, p. 1). This fact results in companies having less information to work from in making decisions regarding the transition, as the process performed by other businesses may have little relevance to each other.

Further, some businesses may not find much application to social media utilization versus others. For example, businesses that based most of their profits on selling directly to other businesses (e.g. industrial supplies, commercial supplies, etc.) are rarely going to use communication methods outside formal channels (namely, email and phone). Building a social media presence will likely have little effect on the company, given those who use its service typically discover the company through connections and referenced individuals. Thus, the organization must accept a degree of risk if choosing to allocate resources to building a social media utilization aspect to the company.

 

References

Fox, Gretchen (2016, April). Busting the Myth on Social Media ROI. Forbes.com. https://www.forbes.com/sites/gretchenfox/2016/04/20/busting-the-myth-on-social-media-roi/2/#6ed3b1573b8c

Lloret Romero, N. (2011). ROI. Measuring the social media return on investment in a library. The Bottom Line, 24(2), 145-151.

Measuring Social Media ROI in the Enterprise: Myths and Facts (2014, March). Oracle. http://oracle-downloads.com/social-roi.pdf

Montenegro, Lisa (2018, January). Social Media: Measuring the ROI. Forbes.com. https://www.forbes.com/sites/forbesagencycouncil/2018/01/30/social-media-measuring-the-roi/#4982523e6d9f

February 5 2018 Post #1

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