The Emerging Dominance of Users in the IT System Flashcards
user-specialist communication gap
IT specialists and users of information systems often have great difficulty in working together. Traditionally, IT specialists have held a dominant position within organisations. This is, however, being challenged as users are becoming increasingly comfortable with IT and are being exposed to a wide array of powerful, consumer-oriented technologies. These developments will undoubtedly impact the role of end-users in the context of business applications.
Recommender Systems
Are tools for interact- ing with large and complex information spaces. They provide a personalised view of such spaces, prioritising items likely to be of interest to the user.
Principles of recommender systems
A. Personalisation: The recommendations it produces are meant to optimise the experience of one user, not to represent group consensus for all.
B. It is intended to help the user select among discrete options
Knowledge resources of recommender systems
- Social knowledge about the user base in general
- Individual knowledge about the particular user for whom recommendations are sough
- Content knowledge about the items being recommended, ranging from simple feature lists to more complex ontological knowledge and means-ends knowledge that enable the system to reason about how an item can meet a user’s needs.
Two Techniques of recommender systems
- Collaborative recommendation: if Alex and Bob have the same utility for items 1 through k,
then the chances are good that they will have the same utility for item k + 1. Usually, these utilities are based on ratings that users have supplied for items with which they are already familiar. - Advantage: simplicity
- Problems:
I. New items cannot be recommended without relying on some additional knowledge source. Extrapolation depends on having some values from which to project
II. Cold start: is the problem associated with new users, for which the algorithm has no data - Content-based recommendation: linked with supervised machine learning —> view the
problem as one of learning a set of user- specific classifiers where the classes are “useful to user X” and “not useful to user X.”
Issues:
I. Quality —> The objects to be recommended need to be described so that meaning- ful learning of user preferences can occur.
II. knowledge acquisition, maintenance, and validation
Problems with recommender systems
- Capture novelty —> A recommendation technique that optimises for high accuracy over the entire data set therefore contains an implicit bias toward well-known item
- Static —> need to capture the dynamic nature if recommendations as field of items is always expanding; a user’s tastes are evolving; new users are coming to the system
- Interaction between the utility functions of the store owner (which wants to increase profit) and the user, which necessarily look quite different
Types of recommender system algorithms
A. User-user algorithm: computes the “distance” between pairs of users based on how much they agree on items they have both rated. Drawbacks:
* Many pairs of users have only a few ratings in common or none at all
* The distance change rapidly (algorithms has to do the calculations on the spot)
B. Item-Item algorithm: calculates the distance between each pair of books or movies or what have you according to how closely users who have rated them agree. Drawback:
* Inconsistency of rating: users often do not rate the same item the same way if offered the chance to rate it again
—> both of the two algorithms are too rigid: they can spot people who prefer the same item but then miss potential pairs who prefer very similar items
C. Dimensionality reduction: more computationally intensive —> use a mathematical technique called singular value decomposition, which involve factoring the original giant matrix, of all of the taste of users reading a certain category, into two “taste matrices” plus a third matrix that, when multi- plied by either of the other two, re-creates the original matrix.
* The main drawback to this approach is that the time it takes to factor the matrix grows quickly with the number of customers and products, and the process needs to be repeated frequently
How to judge algorithms
- Look at the difference between its predictions and the actual ratings users give
- The extent to which recommendations match actual purchases —> however, it erroneously rewards the recommender for items users managed to find on their own
- Given the shortcoming of the above, research is still searching
Key business objectives for a reputation system
- Build trust: Encouraging “good” behaviours and discouraging “bad” ones
- Promote quality: enhance the system by helping to recognise and feature high-quality contributors. —> This provides an incentive to contributors to try harder (so that they can be recognised) while helping users easily identify quality content.
- Facilitate member matching: this can help users assess how similar or compatible they are with each other so that they can decide how much to trust some- one’s posting or whether to initiate collaboration.
- Sustain loyalty: Once users have built a reputation on a site, they will be reluctant to defect to a competitor since they would then have to build their reputation from scratch
—> different social web platforms, however, assign different priorities to each of these objectives Ex. eBay’s primary objective is building sufficient trust so that buyers can feel comfortable enough to send their money to sellers they have never met
What information should be included in the user’s reputation profile?
- Which actions are most relevant to the reputation system’s users? ex. , if the key objective of the system is to help users decide whether a seller is honest, then keeping track of the seller’s percentage of completed transactions is a very relevant indicator
- Which user behaviours are desirable? The mere act of publicly keeping track of someone’s actions can encourage or discourage their incidence
* If a site wants to encourage the volume of contributions, it might consider keeping track on reputation profiles of the number of reviews posted or the number of comments posted
* If a site wants to encourage the quality of contributions it might want to hide information about contribution volume and keep track of how other people rated a particular contribution instead. - For which behaviours is it possible to obtain reliable information? It is important to choose metrics that are reliable and difficult to manipulate —> the choice is between internally generated (first-hand) information and (second- hand) feedback provided by others
* Relying on internally-generated information about user actions is generally preferable as it tends to be more reliable. However, such informations are not always available or it is very costly
* When relying on second-hand feedbacks provided by others, firms must put in place mechanisms to limit manipulation and gaming
How should reputation information be aggregated and displayed?
- Raw activity statistics: ex. number of reviews posted, number of transactions completed.
* It is the most neutral method of summarising someone’s reputation, allowing users to make their own conclusions.
* The burden of interpreting these quantities falls on the shoulders of the user, who must be familiar enough with the environment to draw the proper conclusions. - Scores and distinctions: ex. star rating, numerical scores (eBay), achievement badges (eBay power seller, Amazon Top Reviewer).
Easy to read, they immediately communicate whether a performance is good or bad —> the best for assessing quality - Leaderboards and user ranking: ex. the list of top Amazon reviewers; Epinions’ author popularity ranking
They imply judgement and indicate a user’s stand- ing relative to everyone else. The direct comparison among users installs competition and may be disruptive
—> the choice of such system can determine:
I. The extent to which the reputation systems allow users to make judgements
II. The extent to which the reputation system can create competition among users
3 Stages how managers should foster and sustain member engagement
- Stage 1: understand consumer needs and motivations
Consumers participate in virtual communities to meet social and physiological need (intrinsic motivation), as follows:
—> different community members will try to fulfil different needs at different times. Thus, managers should target their efforts appropriately according to the different needs of community members to accelerate and amplify engagement with their firms’ community. - Stage 2: promote participation
Focuses on the role of a sponsor in promoting participation in a virtual community, as an extrinsic motivation for consumer participation.
A. Encourage members to contribute high-quality content
B. Cultivate connections among members
C. Create enjoyable experiences for members - Stage 3: motivate cooperation
In this stage firms can extrinsically motivate consumers to meet their needs and, at the same time, intertwine these needs with their desire to create value for themselves and for the community sponsor.
—> the research suggests that members are motivated to cooperate with a sponsoring firm when they believe that the sponsor has attempted to embed and empower them through the community
There are 3 efforts that a re effective in motivating cooperation through a sponsor’s efforts that embed and empower virtual community members
A. Mobilise member leaders
B. Inside ideas from members
C. Pool a panel of members
Assessing the value of engagement in virtual communities:
There are 3 sources of value that emerge through member engagement in virtual communities
A. Participatory value: benefit associated with a sponsor’s efforts to promote participation in a virtual community —> can be asses by tracking metrics on an interaction profile
B. Relational value: e benefit associated with efforts to motivate cooperation from virtual community members, which leads to sustained levels of member engagement. —> members foster trust, satisfaction, and relational benefits to firms, such as consumer willingness to collaborate in developing new products and services, spread positive word of mouth about a sponsoring firm, and give opinion-based feedback to firm
C. Financial value: sales of products, content, advertisement placements
