Psychographic and Cognitive Human Factor Modeling in Decision

Support Systems for Building Personalized Product Ecosystems

Alberts Pumpurs

Faculty of Computer Science and Information Technology, Riga Technical University, Duagavgrivas Street 2, Riga, Latvia

Keywords: Human Factor Modeling, User Modeling, Decision Support Systems, Design and Creativity Support System.

Abstract: There are countless products build and launched every day. A growing number of possibilities for the

consumer increases competition between similar products and product developers voluntarily or involuntarily

are creating product ecosystems to stay competitive or relevant. As the products tend to form ecosystems,

then for users it less decision of which product, instead of more of which ecosystem to buy into. This poses

new challenges for product and ecosystem developers, how to comply with true user needs, and which are

worth investing in. This position paper discusses the possibility that psychographic and cognitive human

factor modeling could be the way to understand users and build personalized ecosystems using the decision

support system. Following position, the paper is a proposal for future research in developing such decision

support and conceptual model of user data and its relationship is proposed.

1 INTRODUCTION

Today's users and consumers are fortunate because

there are many solution providers ready to fix their

needs and problems. There are plenty of products that

users can acquire and invest their time to get their

desired tasks done. The issue today is not that there

would not be a particular product or digital solution

for users' problem, but the burden of making the right

choice. Because right now users are not simply

buying just one product, but the whole ecosystem that

the product is in or will be in the future. We are living

in a time where the line between digital products and

physical products is blurring. Thanks to IoT and

digitalization everyday items are connected to the

internet and our smartphone apps (Mattern,

Friedemann; Floerkemeier, Christian, 2010).

Additionally, products from the same and different

industries create product ecosystems (Dass, M., &

Kumar, S., 2014). For example, Apple, Microsoft,

and Google are known for creating their product

ecosystems in which they are interested to keep users

in and providing them with all the solutions to their

needs. Companies like Ikea and Xiaomi strive for

cross-industry partnerships to create a smart home

environment for better living. Knowing that Xiaomi

is a competitor to Apple, Microsoft, Google there is

an unlikely chance that these IT companies would

participate in IKEA and Xiaomi joined the

ecosystem.

This paper is not about the company strategy and

competition policy, but the user and the product

ecosystem developer aspect. The objective of the

position paper is to outline how currently user data is

used to create new solutions and propose a

preliminary conceptual model of how to extrapolate

knowledge about users using their cognitive and

psychographic data. It is important to evaluate what it

means for users to be a part of any kind of ecosystem,

what they gain, lose, and what kind of decisions they

are having when enrolling in a product’s ecosystem

as well as what it means to be a product development

company that creates products and ecosystems

around them. How to make better decisions and

which decision support systems or methodologies

they should use. For digital-only and products

connected to the internet, users place important to be

aware of product quality as well as what kind of

ecosystem integrations it has, with what kind of other

services it works and with whom it doesn't (Dass, M.,

& Kumar, S., 2014). Most users don't want to be

denied functionalities and features even if they are not

planning to use them. That is loss aversion bias

(Kahneman, D. & Tversky, A., 1992) that plays a

powerful role in decision making. Another aspect is

that users are paying attention to their personal data.

Who and how are using them. Users are concerned

Pumpurs, A.

Psychographic and Cognitive Human Factor Modeling in Decision Support Systems for Building Personalized Product Ecosystems.

DOI: 10.5220/0010132001450151

In Proceedings of the 4th International Conference on Computer-Human Interaction Research and Applications (CHIRA 2020), pages 145-151

ISBN: 978-989-758-480-0

145

about how companies collect, store, and use their

personal data. But for companies data analysis often

is how to improve their solutions to satisfy and retain

their users (Crocco, M. S., Segall, A., Halvorsen, A.

L., Stamm, A., & Jacobsen, R., 2020). For some

ecosystems gathering, analyzing, and sharing data is

the reason for their existence. Misusing and

miscommunicating data usage creates a conflict

between involved parties that may threaten the

existence of the ecosystem itself. There is a need for

fair exchange between users, their personal data, and

companies that develop products and ecosystems.

Where on one hand users would be happy to provide

and share their personal data and companies would

use them in a fair manner to provide the solutions for

the user needs thus retaining them on their product

and even building a product system around them.

To create better solutions companies often are

using human factor modeling principles to gain a

conceptual understanding of the user. The generated

knowledge is used to better understand user needs and

satisfy them in new product development, or product

iterative update (Fischer, Gerhard, 2001). The

objective of this paper is to propose an initial version

of a conceptual model of human factor modeling

using user psychographic and cognitive analysis to

better understand their needs. The aim of this model

is to be used as a start for the decision support systems

to assist in personalized product or product ecosystem

development, ensuring that user data is used for a

good purpose to develop products that suit their true

needs. The proposed system as well could assist

designers in the decision making process as a design

and creativity support system due to insights on

human factors.

2 BACKGROUND

Most newly created companies that are building

products fail within two years of their product launch

because of a poor problem-solution fit and negligence

of the learning process during product development

(Tripathi, N., Oivo, M., Liukkunen, K., & Markkula,

J., 2019). This shows the risks of what can happen if

important user needs are not met by the product

developers. Newly found companies that have only

one product as their main income source risks whole

existence on its success. Established companies in

case of product failure risks allocated budget and

potential setback or loss in the given product market.

There are two key aspects when creating and

releasing any product or ecosystem to the market, to

maximize its success. First is the bigger picture - why

the product is needed and what purpose it has - the

focus on the fulfillment of psychological needs to

create (Kim, J., Park, S., Hassenzahl, M., Eckoldt, K.,

2011). Second - the product’s embodiment design in

detail concerning material, usability and interface.

Thus, the two key aspects of product success are

Macro UX and Micro UX as proposed in the research

paper by Constantin von Saucken, Ioanna

Michailidou, and Udo Lindemann - “How to Design

Experiences: Macro UX versus Micro UX

Approach”.

Macro UX - the psychological needs the product

fulfills is not something that users often consciously

realize and are aware of. Thus, these are unconscious

needs. If a user would be asked, as typically done in

product development processes via focus groups,

questionnaires etc, what he wants in a new future

product, the answer will not directly show his real

psychological need. The research on a user's implicit

motives or psychological needs can lead to innovative

ideas but requires a psychological background. Since

most of the product decision-makers are not with

knowledge in psychology they would benefit from

knowledge on the user psychography. It is possible to

build products that are more suited to users by

knowing their true needs and motives, but it is only

part of product success.

Another part is the Micro UX that focuses on the

optimization of user experience (UX) in the later

embodiment design stage by anticipating the user’s

perception and processing, (Von Saucken, C.,

Michailidou, I., & Lindemann, U., 2013) in

psychology that is called cognition. A mental action

or process of acquiring knowledge and understanding

thoughts, experience, and the senses (Oxford

dictionary, 2020). Having data on users’

psychographic and cognitive thinking it is possible to

build and modify the understanding of the user by

creating a model to customize and adapt systems to

the user's specific needs. Successfully created models

can be used in decision support systems for new

innovative solution development thus achieving user

satisfaction.

The necessity for a decision support system is for

decision-makers who often rely on their personal

intuition when coming up with strategic decisions

(Jossey-Bass, H. A. Simon, 1983). This position

paper proposes an opportunity to help intuitive

decision-makers to base their decisions not solely on

their intuition, but on rational facts as well as make

their decisions user-centered. Such a system could

help make precise, personalized user-need centered

decisions, as a result - maximize the chance for

product success and drive effective resource usage.

CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications

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Figure 1: Current relationships in product development between the user, his data and product developers.

Figure 2: Conceptual model relationships between product ecosystem developers and the proposed decision support system.

To achieve that, the position paper’s aim is to propose

a model for a decision support system based on

human factor modeling principles where it would

help to make better decisions for Macro UX and

Micro UX aspects. Psychographic and cognitive

human factor modeling is proposed as a key aspect of

generating new, untapped knowledge that will be

used in the product and ecosystem development

decision-making process.

3 CURRENT APPROACH

To better evaluate and seek improvements, it is

necessary to understand how currently most of the

products are created and what are the relationships

between the user, product developer, and data.

Because of rapid digitalization and IoT common

and previously unimagined solutions more and more

are connected to the internet. As users are interacting

with products and solutions, data is generated and

brought back to users in the form of knowledge. For

example, Philips Sonicare toothbrush and its

application. Toothbrush - when used is generating

data about tooth cleaning frequency, duration, applied

pressure and time spent in cleaning teeth. This

information is sent to the backend for parsing data

into knowledge and sent back to the user via a mobile

app telling him to visit a dentist, change brush

hardness, etc. In order to build the next product

iteration, ecosystem or entirely new product, product

developers analyze user-generated interaction data,

analyze it and seek insights for improvements that

could better serve users. All that is done with an aim

to make next-generation or new products worthy and

appealing for the purchase.

3.1 Conceptual Model

The objective is to propose a conceptual model that

represents what is the relationship between users,

product, ecosystem, and the expert system within the

product creation and improvement phases. As well as

to show what type of data would be used in

psychographic and cognitive human factor modeling

to create a decision support system for personalized

product ecosystems.

At first, user-generated data would be filtered into

two key user-product relationships describing data -

cognitive and psychographic. Data identification and

structuring is an important stage because each data

type would be modeled separately through human

factor and user modeling algorithms. As a result of

modeling a conceptual model would emerge that

would describe users existing and potential

relationships with the product or ecosystem. The

gained knowledge of user preferences and taken

actions would describe their knowledge of the usage

patterns as well as their perception of the product

ecosystems. Knowledge of user psychographic

preferences and behavioral patterns are what can help

product developers to create more personalized

products and their ecosystems. Using a decision

support system product developers would be able to

Psychographic and Cognitive Human Factor Modeling in Decision Support Systems for Building Personalized Product Ecosystems

147

Figure 3: Psychographic cognitive data and decision support system outcome.

access knowledge on their users and gain answers on

product ecosystem existential questions. Questions as

- what type of user would use it, for what purpose they

would serve, what kind of psychological needs are

covered, and which are not. And answer interaction-

based questions - how what will be used, will they

understand functionality, would product functionality

within the ecosystem solve users' need for it.

For cognitive user modeling used data would be

attention, language, visual-spatial perception,

memory, and executive functioning. For

psychographic user modeling it would be personality

traits, values, interests, lifestyle, motivation opinions,

behaviour data. It would be used separately for the

user modeling algorithm since cognitive data can be

more easily collected through service usage data and

is quantitative data. The expected outcome of

cognitive and psychographic data modeling is a

decision support system.

3.2 Outputs of Decision Support

System

Market researchers have been attempting to develop

predictive models for understanding consumer

preference of newly developed products and

ecosystems they are in. Preference mapping

techniques are the most popular methods among these

prediction models to understand what product

attributes are driving preference (R. Krishnamurthy,

A.K. Srivastava, J.E. Paton, 2007). It is important to

link describing product and ecosystem attributes,

such as appearance, packaging, ease of use and others

with customer preference from perspective of how it

fits in their daily lives (psychography) to how they are

going to use it (cognitive actions). An important

aspect of human-centered system design is cognitive

compatibility, which means that the structure of the

human-machine interface of the computer should

match the cognitive styles of the users (Fuchs-

Frothnhofen, P., Hartmann, E., Brandt, D., &

Weydandt, D., 1996. Psychology has been applied to

HCI research in recent years to inform design choices

and understand differences in how in- dividuals use

technology. It enables researchers to arrive at

conclusions regarding design effectiveness, since

successful technology development requires input

from a representative set of potential users and, more

precisely, the range of differences among individuals

may influence technology. Some factors may include

age, gender, job duties, language, culture and

fundamental idiosyncratic attributes, such as

personality and motivation (Alves, T., Natálio, J.,

Henriques-Calado, J., & Gama, S., 2020.

Business must satisfy customers and their needs,

to stay competitive - decision support system purpose

is to use input data from the users to identify what

attributes they value the most. Knowing that users -

when using any product or ecosystem generate data,

the decision support system can use the given data

and parse that into the knowledge about user

preferences about particular products as well as to

better understand ecosystem usage patterns. The

purpose and given output of the decision support

system is knowledge about unstructured user

behaviour data that represents which product and

their ecosystem attributes they value the most and

how they interact with its features. Based on proven

aspects that users likability of a product impacts on

how it fits in his daily life through his personality and

usage patterns - such a decision support system task

is to model user data and provide with clear value and

behaviour description what particular users want in a

product ecosystem. Using this decision support

system product developers will be able to see data on

their users at one unified place and query database on

specific user actions and their opinions. For the

valuable knowledge output on users,’ the developers

would be able to query whether user’s behavior

matches their values – which would be considered as

a strong positive signal that can be used as

CHIRA 2020 - 4th International Conference on Computer-Human Interaction Research and Applications

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confirmation on matching with user’s preferences.

And the same if users’ behaviors do not match with

their values – that would be an indicator that what the

user says and does are two different things and further

investigation is needed. Which most of the case is

potential for additional insights and product

opportunities.

4 APPLICATION CASES

Several application cases are introduced to showcase

the potential use of the decision support system in

B2C markets. Each application case consists of key

elements used in any product development cycle -

potential or existing users, product development team

and data that is known or can be mined.

4.1 New Product Introduction

Whenever a new digital-only, internet-connected

product or whole ecosystem is about to be created and

introduced to the users, there is a degree of

uncertainty whether it is going to succeed or not. To

minimize the risk of failure, the product and

ecosystem need to be maximally attuned to user

needs. Else it has the potential to become yet another

thing that is not needed and it would cease to exist in

its early lifecycle. Often new solution development

should start with its purpose and reason for existence.

This is something that is frequently overlooked by the

stakeholders and is the reason why even well-crafted

products can fail. Product and business development

as well often are driven by stakeholder’s inspiration

and emotions. Thereof there are not enough data and

evidence and research on what to base the upcoming

product or ecosystem. User preferences, lifestyle

attributes, and expectations often are not well-read.

Thus, increasing unknown factors of overall products

or its ecosystem's ability to be personalized for a

specific target audience. Product developers would

benefit from the proposed decision support system

mostly from the psychographic aspect because it

would help them to tap into the user personality traits,

values, interests to attune desire, and a true necessity

for the product.

4.2 Iterative Product Update

When a service has already been launched, but it is

time to update its capabilities it is important that the

introduced new update still captures user needs. If the

product starts to deviate from the initially designed

purpose or becomes hard to use due to new feature

introduction - users may search for an alternative.

This is a risk for stakeholders and developers because

in new iterative product updates there are aspects that

could go wrong with the decision making. And since

stakeholders and product developers mostly rely on

their instincts when developing - the risk is even

greater. For iterative product updates it is important

to evaluate whether it is even needed. There are cases

when companies release a new iterative update with

minimal changes, mostly due to marketing reasons

and users buy it but have little or no gain in

improvement. This may leave users confused and

unhappy especially if the update requires financial

investment from them. It is important to evaluate how

and if the existing user base is perceiving product or

ecosystem personalization efforts from the previous

generations before a new and updated generation is

released. Stakeholders may discover that by

comparing behavioral and product usage data from

what they were when the product was launched and

what they have become over time. For example, how

long time is now spent on using product function

comparing to what it was in the beginning. What user

values were impacted when the product was released

and how they are changes over the product's existence

time. Existing or non-existing change in data provides

some knowledge for product developers whether they

have achieved personalization overtime or not. As

well as this should help them to make a decision on

next-generation based on knowledge. Benefit for

iterative product and ecosystem updates in a matter of

personalization comes from understanding user

psychographic data change since the previous

iteration, as well as improvements in usability

through cognitive analysis.

4.3 Integration in the Product

Ecosystem

Introducing the product to the existing ecosystem

poses a couple of questions that need to be asked by

product developers before investing in the resources.

Product development for an ecosystem is associated

with whether users need a product to be part of the

ecosystem and vice versa. And from a cognitive

aspect how users will understand the connection

between the product ecosystem and vice versa. An

interesting use case for the decision support system

would be understanding the users who are already in

the existing ecosystem before developing for it. For

example, Apple HomeKit is an existing product

ecosystem of smart home connectors, there is room

for more, but the question is how to gain a

competitive edge by being more user need-oriented.

Psychographic and Cognitive Human Factor Modeling in Decision Support Systems for Building Personalized Product Ecosystems

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To not enter the existing ecosystem with a less

personalized product, compared to already existing

competitors, stakeholders need to know existing user

needs in the ecosystem. As well as which needs

(psychographic and cognitive) are already fulfilled

and which are not. For that purpose, it is important to

analyze user cognitive and behavioral trends in a

particular ecosystem. Trends change over time shows

a rise and fall in the demand for a specific product's

purpose and its usage. And ideally. compare the data

with the existing player product offering. The

challenge is to gather data that is already known to the

competitors because they are already in the given

ecosystem. This challenge will be addressed in future

work as well.

5 FUTURE WORK AND

CONCLUSION

To achieve successfully made ecosystems and

products within them, then created ecosystems and

products need to be personalized for the user in a way

that corresponds to their actual needs. It is not enough

to make offering attractive in it form factor, it needs

to meet users' expectations in terms of functionality

and be aligned with users' personality traits.

Altogether many aspects need to be taken into the

account to create a result that succeeds for customers

and businesses at the same time. As discovered in this

paper – most of the stakeholders are relying on their

intuition when making decisions rather than making

calculated, evidence-based decisions. There are much

researches done on users' cognition and its impact on

the likability of the end product and lesser where

users psychography is taken into the account,

especially to evaluate user values and personal

beliefs. There is a lack of proposals where

psychography and cognition both and combined

could be used in human modeling for better

understanding their motives, preferences, their values

expectations as well as how product or ecosystem will

be preferred to use.

Based on this proof of concept paper that

describes the necessity for a rational decision support

system for stakeholders who are building

personalized product ecosystems – psychographic

and cognitive human factor modeling could be used.

There is potential on having a decision support

system that answers questions – why to build and

what features in what way should be added by

combining psychography and cognition of a user.

Thus answering macro UX and micro UX questions,

and in ideal case giving decision support to

stakeholders to achieve personalized products and

ecosystems for user needs.

The conceptual model will be further researched,

attuned and developed in the framework of the

research and innovation project. Additional human

factor modeling decision support system application

cases will be identified, more usable cognitive and

psychographic data will be defined and user modeling

algorithms will be introduced. When the decision

support system is built it will be tested for its

efficiency improvement over the feedback loop and

will be piloted in the actual product development

cycle.

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