Profs. Ayushi Tandon, Trinity Business School, and Swanand J. Deodhar, IIM Ahmedabad, together with researchers Abhas Tandon and Abhinav Tripathi from Words in Sentences, Bangalore, dig deep into online user behaviours to discover what makes them rapidly consume content or save it for later. The findings spell practical innovations for platform designers

The Digital Compass and the Ladder of Engagement: What makes us click or save? by: CoBS Editor Antonin Delobre. Related research: Effects of social information signals on user engagement: evidence from randomized field experiments, Swanand J. Deodhar, Ayushi Tandon, Abhas Tandon, and Abhinav Tripathi, Behaviour & Information Technology, 2023.

In the vast, sprawling wilderness of our digital platforms, we are all, in a sense, lost. Every time we open a mobile app, navigate an e-learning portal, or scroll through a streaming service, we are confronted with a “wicked problem” of choice: what is worth our time?

With thousands of videos, articles, and words vying for our attention, the human brain faces a staggering cognitive load. To survive this information saturation, we look for beacons. We look for the “commonly viewed,” the “frequently liked,” or the “expert recommended”.

These are not just labels; they are social information signals, the digital compasses that guide us to click, save, or skip. But do these signals influence every action equally? Does a “Peer” – another user – recommendation carry the same weight as an “Expert” one when we move from mere browsing to active organizing?

An empirical  study by Prof. Ayushi Tandon of Trinity Business School, and her fellow researchers Swanand J. Deodhar (Indian Institute of Management, Ahmedabad, India) Abhas Tandon and Abhinav Tripathi (Industry experts, game developers), reveals that our response to these signals is far from uniform. It is a complex, hierarchical dance where the source of the signal and our own familiarity with the platform determine exactly how, and if, we engage.

User Behaviour: Climbing the engagement ladder

For years, the world of Information Systems (IS) research has been obsessed with a single peak: Content Generation (CG). Researchers have studied why people write reviews, post photos, or moderate discussions. But as they point out, the majority of our digital life happens on the lower rungs of what they call the “ladder of engagement”.

 This study shifts our focus to two critical, yet understudied, behaviours:

1.  Content Consumption (CC): The “baseline” act of engagement, such as clicking to watch a video or, in the context of the researchers’ mobile e-learning experiment, clicking a word to see its usage in a sentence.

2.  Content Organisation (CO): A higher-order, more complex action where a user “curates” their experience, such as adding a video to a “watch later” list or a word to a personalised vocabulary list.

Understanding the distinction between these two is vital. While CC is often a fleeting, low-cost action, CO represents a deeper investment of time and a reflection of personal preferences. The researchers set out to discover if the “social signals” that drive us to consume are powerful enough to drive us to organise what we want to watch.

The peer vs. the expert

To test the power of these digital beacons, the team deployed a series of randomised field experiments on a mobile app called “Words in Sentences” (WiS). They split users into two distinct camps: those receiving signals from Peers (other users) and those receiving signals from Experts.

The “Peer” signal was a simple label: “Commonly viewed by others”. The “Expert” signal was equally direct: “Expert Recommended”. The question was simple: would these labels make a user more likely to click a word (Consumption) or add it to their personal list (Organisation)?

The results were a striking study in asymmetry. The researchers found that both Peer and Expert signals acted as powerful catalysts for Content Consumption. The Peer signal was particularly potent, increasing the probability of a user clicking a word by approximately 29 percentage points. The Expert signal also provided a significant, though slightly more modest, boost of over 14%.

Why does this happen? In an information-rich environment, we suffer from “bounded rationality”. We don’t have the time to evaluate every option deeply. Instead, we use “decision heuristics” – mental shortcuts – that allow us to mimic the “correct” course of action based on what others have done. These signals reduce our cognitive burden, acting as an instrumental cue that guides us through the fog of choice.

The silence of the signal

However, when it came to Content Organisation, the digital compass suddenly stopped spinning. Neither the Peer signal nor the Expert signal had any significant effect on whether a user added a word to their personal list.

This “curious asymmetry” suggests that CO is qualitatively different from CC. While consumption might be driven by social mimicry, organisation is an inward-facing act. When you create a personalised list, you are not looking for social proof – you are seeking personal value, identity expression, and long-term utility. You don’t save a video to your “favourites” just because an expert liked it; you save it because it resonates with your specific needs.

To test further, the researchers even tried a “double-barrelled” approach, showing users both Peer and Expert signals simultaneously, thinking that perhaps a plurality of signals would provide the necessary push for organisation. It didn’t work. The effect on consumption remained positive, but the organisation actions stayed stubbornly unresponsive to general social signals.

Does this mean social signals are useless for organisation? Not quite. In a follow-up experiment, the researchers uncovered the “missing link”: Signal Parity. They changed the Peer signal from “Frequently viewed by others” (a CC-based signal) to “Frequently added by others” (a CO-based signal). The results were transformative.

When the signal finally matched the desired action, the probability of the user adding the word to their list nearly doubled. This finding reveals a fundamental truth about digital influence: for “higher-order” engagement actions, the signal must be specific. We don’t just want to know what others are looking at – we want to know what they are valuing enough to keep. This reinforces the concept of a “hierarchy” of engagement where the further up the ladder you go, the more selective and specific the social influence must be.

User Behaviour: When familiarity breeds independence

There is one more crucial variable in this digital symphony: Time. The researchers discovered a clear “decay” in the effectiveness of social signals as users spent more time with the content – a concept they call Content Exposure. As we scroll through more content and become familiar with an app, we experience “reduced uncertainty”. We start to understand the quality of the content for ourselves, and as our personal perception sharpens, our reliance on external heuristics fades.

The signal that was once a vital compass bearing for a newcomer becomes mere “background noise” for a veteran. Our engagement actions become more “inward” and less reliant on the digital crowd. Interestingly, they also found that “stronger” signals (like knowing what others have organised) can actually resist this decay, keeping the user engaged for longer. But the general rule remains: the social signal is a tool for the uninitiated, and its power is a function of our own ignorance.

Reflections for the future of design

The implications of this research for app designers, marketers, and e-learning platforms are profound and practical. In an era where mobile screen space is a precious, limited resource, every pixel must earn its keep. Here are some takeaways:

1.  Chirurgical Interface Design: Designers should not saturate their interfaces with social signals for every feature. If the goal is to drive discovery and consumption (CC), social signals are the “king” of the user interface. However, if the goal is to encourage deep curation (CO), general “view counts” are a waste of space. You must show users what their peers are curating, not just what they are seeing.

2.  Dynamic Disclosure: Since the power of the signal decays as the user becomes familiar with the app, information structures should not be static. Apps could benefit from “dynamic social information,” where social cues are prominent at the start of a session or for new users, then gracefully fade away as the user becomes more self-assured, freeing up space for more personalised tools.

3.  Data-Driven Requirement Engineering: Developers should use usage logs to identify the specific asymmetries in their own apps. By understanding which signals drive which behaviours, they can move toward “data-driven” design that respects the hierarchy of user engagement.

Civilizing the digital choice

Ultimately, the work of Prof. Tandon, Swanand J. Deodhar, and co-authors teaches us that social information is not a “magic bullet” that boosts engagement across the board. It is a delicate, context-dependent heuristic. It is most effective when we are uncertain, when the signal matches the action, and when we are at the earliest stages of our digital journey.

By recognising the ladder of engagement, and the different fuels required to climb its rungs, we can build digital environments that don’t just “nudge” us to click, but respect the personal, curatorial nature of our deeper digital lives. The social compass is a powerful tool, but its true value lies in knowing when the user is ready to put it down and walk their own path.

Useful links:

• Link up with Profs. Ayushi Tandon and Swanand J. Deodhar on LinkedIn
• Related article: ICT in Social Connections: Good servant, bad master
• Discover Trinity Business School
• Apply for the Trinity MBA.

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