NZ: Please tell us a little about yourself. What is it you do? How and when did you become engaged in the battle against Information Overload?
MB: I am a Professor of Information Management at the Stuart School of Business, Illinois Institute of Technology. I first became aware of Information Overload during a seminar on Mathematical Psychology I attended during the early 1970’s at the University of Illinois. It was where I first met the concepts of human information processing and cognitive load, which I’ve been studying ever since. Throughout my professional career, I have had a continuing interest in behavioral issues related to judgment and decision-making; and as we all know, IO impacts decision quality, task productivity, and human stress. In 2008 I noticed the newly launched Information Overload Research Group, and connected to it; I am at present a director and treasurer of IORG.
NZ: We hear many different views of Information Overload from different researchers. What is your vision of IO space?
MB: I’ve realized in the late 70s that most everyone was defining information as it relates to decisions, and I felt this was too narrow, that how you present the information to be useful to people matters. I’ve been teaching this for 40 years!
I define IO as a reduced ability to recognize/receive/classify or interpret data elements that reduces our capacity to process stimuli and continue executing cognitive processes to complete a task.
Consistent with psychologists, I separate information processing into Attention Reception and Cognitive Workload. IO is an operative factor for each of these phases:
IO may cause our Selective Perception filters to overlook an important information signal. Too much background (audio or visual noise) may distort or hide foreground information; competing demands of background issues (e.g., family health concerns) can dilute our focus and concentration.
IO impacts the degree of mental effort required to perform a task and its resulting decision quality. There are many factors to consider in this context, including among others Information Attributes (volume, relevance, clarity, urgency, integrity,…) and Task Attributes (due date, competing tasks, expected result,…).
In my view IO is not necessarily the same across people, there are individual differences. The impact depends on personal attributes – for example stress, alertness, ability to focus/concentrate, physical and mental health status, personality type,… Our degree of happiness, our alertness, all affect how we respond to IO and absorb information.
NZ: As a veteran in this research area, looking at the changes in the IO problem since it began in the 1990s, what trends do you observe?
MB: On the whole, our evolving work mode in industry has created dysfunction due to an overload of communications demands within the team. I see an expanding variety of information sources, increasing speed of communication that motivates more frequent attention to status updates in more information channels, and increased volume of inter-personal communication in collaborative teams (due to the introduction, in the 1990s, of business processes that span functional silos). I also see the emerging acceptance of intelligent assistants that assume responsibility for executing highly structured tasks.
NZ: Looking at the academic environment you work in, is the IO problem affecting the ability of professors to teach, and of students to learn? If yes, how can academics deal with this?
MB: I find that students today prefer self-paced learning and digital content (including videotaped lectures) to the traditional formal frontal lecture. Yet, some degree of social interaction is lost if students do not attend classes. Perhaps a hybrid of both online learning and classroom discussions and experiential explorations could offer an improved learning experience.
Having a variety of intellectual capabilities in the classroom, the ability to absorb information varies from student to student. So having all students learning in the same process may not be good – we should allow students to read and learn outside the class and participate in discussion boards. Quizzes should involve immediate feedback. It is not “One size fits all” – we need “Mass customization” of teaching, as opposed to the traditional lecture followed by a uniform quiz. The challenge of academia is to figure out what info can be delivered individually. Faculty should figure which info nuggets are required and which are optional for the curious to consume at will. I recall a colleague sharing that a faculty member at the University of Chicago would use programmed learning material with embedded quizzes for students. After completing one or two quizzes successfully, the student earned the right for some one on one time with the faculty member – an early variant of the “ flipped classroom” concept. As to whether to give Wi-Fi access in class – I am of two minds here; it has its pros and cons.
Looking at my own work – I lack the time to do all the study and research I would like to do… so, I am indeed overloaded.
NZ: What role do you think the latest advances in CS, such as AI and Big Data analysis, will play in addressing the problem? What are the pros and cons of such methods in this context?
MB: The key contribution of Artificial Intelligence will be to present to people information in a manner that most suits them. AI will need to take into account personal preferences, create a profile of the user, and take into account what the user is seeking when they search for information. I expect we’ll see use of AI filters to compose messages before they are sent to others, in addition to classifying and prioritizing incoming message content.
Big data holds much promise, yet the veracity of the data is a crucial issue that is often overlooked. Perhaps we’ll need to reduce this threat by assessing big data integrity before combining it with structured transaction data. The volume and speed of some IOT or machine data requires more intelligent reporting threshold/aggregation criteria to communicate the essence from these real-time inflows (e.g., aircraft engine status to airlines and engine vendors). I also think that the deployment of wearable technology will increase our demands for information monitoring.
NZ: Do you see a difference in the manifestations and solutions of IO in the younger millennial generation – your students – versus older folks?
MB: The millennials are more curious and inquisitive. They search (Google) for information on demand as needed rather than reading (as their elders would) a published structured document – whether a user manual or a formal newspaper (print or digital). This can reduce IO if the “hits” are not overwhelming and noisy… otherwise it can be a double-edged sword.
NZ: Can you share with our readers one or two “best practices” you use that they can adopt to become happier and more productive?
MB: I continue to struggle with IO primarily due to my multi-disciplinary curiosity – I attempt to follow developments in a variety of fields, whether big data analytics, consumer psychology, cognitive and social psychology, behavioral decision theory, human-computer interaction, etc. Decades ago, I could walk through the print journals and browse the table of contents. Now the number of digital journals has exploded. I decompose this online scanning to a rotating smaller set of journals. My tradeoff is that I eventually view all of them, but at a delayed time frame.
For my approximately 250 inbox messages a day (excluding Spam that I must review a few times a week for important content), I look for a pressing action item within the first two sentences. If found, I move it to a “to do” folder to read entirely. If urgent attention is required, I either act or leave it in the inbox for action later that day or the following day. Any other messages I ignore.