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1

Editors – Prof. Amos DAVID & Prof. Charles UWADIA

Arts

Technology

Faith Science

Transition from Observation to Knowledge to

Intelligence

25-26 August 2016

University of Lagos, Nigeria

Editors

Prof. Amos DAVID

Prof. Charles UWADIA

327

The Web Analytics Framework: An

Integrated User-centric Evaluative Tool AKHIGBE B. I., ADERIBIGBE S. O., EJIDOKUN A. O.,

AFOLABI B. S., ADAGUNODO E. R.

*1AKHIGBE Bernard Ijesunor, 1, 2ADERIBIGBE Stephen

Ojo, 1EJIDOKUN Adekunle Olugbenga, 1AFOLABI

Babajide Samuel, 1ADAGUNODO Emmanuel Rotimi

1Information Storage & Retrieval Group Research Team,

Department of Computer Science and Engineering

Obafemi Awolowo University, Ile-Ife, Nigeria.

2Department of Computer Science,

Lagos State Polytechnic, Lagos, Nigeria

Abstract: Interoperable and Adaptive Systems (I&AS) are inevitable since

everyone seek optimum performance from systems. Additionally, pervasive

systems like the I&AS are required in the concept of the smart city. However,

there are no known framework to systematically evaluate the I&AS. Although,

there cannot be any unique framework to evaluate all kind of information

systems, particularly from the perspective of users; efforts in the foregoing

direction are sparse. Therefore, this paper seeks to present a user-centric

evaluative framework using the Web analytic approach and other synergistic

methods. The aim is to contribute an evaluative tool that is pragmatic and user-

centric in orientation. The concept of Web analytics forms the bases to use the

context, content, and process frame, the knowledge base HCI, and the big data

integrative module as the synergistic evaluative elements. Theoretically, the

constructivist view of the theory of information processing and the

unconscious thought theory provided the theoretical foundation to formulate

the framework. The originality of this contribution is in the fact that there is no

synergistic framework that is user-centric and at the same time based on the

Web analytic technique in literature. The Framework provided an integrated

user-centric evaluative tool that would serve as a guide to provide useful users’

requirements for systems’ development. Finally, this paper is descriptive (part

of its limitation), and not prescriptive. There will be need for further work to

empirically test the framework.

Keywords: Digital humanities, praxis-oriented framework, user-centricity, theory of

information processing, scholarly communication, and digital tools

The Web Analytics Framework: An Integrated User-centric Evaluative Tool

328

1. Introduction

In this age and time Interoperable and Adaptive Systems (I&AS) are

inevitable since everyone seek optimum performance from systems.

For both systems - I&AS, it is important that if they are developed for

one Department (independently), they must be easily integrated across

an organization’ processes irrespective of structures and business

concerns. This means that information must be easily interchanged or

shared between applications that are developed for different purposes

(Heubusch, 2006; ISID, 2007; Nechkoska, 2015). Several systems now

exist with interoperable and adaptive characteristics. Such systems

include decision support systems; collaborative and co-operative

systems; and other forms of systems that support the interfacing of

multiple elements and entities as obtained within the concept of the

smart city. Within the concept of the smart city, there are now systems

with specific focus on intelligent transportation management; smart

electricity management (using observatory systems); automated

building management; assisted living and e-health management and so

on (Moreno et al., 2015). However, despite the pervasiveness in the

existence of I&AS, there are no known framework to systematically

evaluate them. Although, there cannot be any unique framework to

evaluate all kind of systems, particularly from the users’ perspective;

efforts in this direction are scarce.

Interestingly, the Web analytics technique of evaluation has been

used to study online user behaviour and quickly assess how effective

users’ virtual spaces are in the commercial sector. Though, the

technique is fraught with some technical pitfalls, its results are often

considered as a working hypothesis until a more experimental approach

is employed with the technique (Fagan, 2014). Cognizance of this

caveat, this paper seeks to propose a synergistic framework. The

framework incorporates the Web analytics technique and other relevant

evaluative elements towards the provision of an integrative user-centric

evaluative tool. This is because of the belief that a synergistic

relationship of other evaluative elements with the Web analytic

framework would be useful. This usefulness will be realised in the

provision of deeper evaluative meaning than the Web analytics can

Akhigbe B. I., Aderibigbe S. O., Ejidokun A. O., Afolabi B. S., Adagunodo E. R.

329

provide alone. The motivation for this paper comes from the findings

that the Web analytics technique can combine usefully with other

methods to provide useful user-centric evaluative interpretations

(Fagan, 2014).

Nowadays, systems; whether hardware or software are designed and

implemented with the goal of user-centricity. That is users should be

able to find their way very easily with the system (Moreno et al., 2015).

This presents the need for evaluative frameworks that are pragmatic

enough to guide stakeholders in the evaluation of systems; particularly

if they are meant to be understood and used by users. It can never be

overemphasized; the fact that if systems are meant for users, such

systems should be developed based on users’ expectations. These

expectations are usually with respect to system functionalities. For this

expectation to be met the concept of User-centricity (Uc) requires

utmost attention (Akhigbe et al., 2015). This concept has its root in

user-centered design. It emphasizes among other things the user-centric

model. This model can be used to guide software engineers in such a

way that they will readjust their perspective to that of the user

(Hotchkiss, 2007; Patton, 2007).

The motivation to introduce this concept comes from the need to

formulate an interactive evaluative framework that is synergistic. This

is because a synergistic evaluative framework with the mechanism to

carefully observe and analyze users’ reactions and attitudes, will make

available satisfactory user requirement(s) to inform system

development. This paper therefore presented a pragmatic user-centric

evaluative framework based on the Web analytic technique and other

synergistic methods. The goal was to contribute an evaluative tool that

is pragmatic and user-centric in orientation. This is premised on the

desire provide an evaluative tool that will be useful to building a shared

understanding of the needs of users that is inspired from their current

and future work practices. This paper offers an evaluative framework;

which practice seeks to justify what the user-centred design

emphasizes. This emphasis is that systems should be developed to serve

the user, and not the demonstration of the use of a specific technology,

nor the skilful use of an elegant piece of programming (Gulliksen et al.,


330

2003). This paper is further structured with; section 2 dedicated to the

state-of-the-art; section 3 committed to the description of the

framework; and finally section 4 contains the papers’ conclusion.

2. State-of-the-Art

Several attempts at introducing the Web analytic technique for user-

based evaluation exists in literature (Fagan, 2014; Waisberg and

Kaushik, 2009; Phippen et al., 2004). However, the focus of its use has

been to automatically gather and provide information about online

interactions, and use its metrics to assess commercial Websites in order

to achieve business goals (Fagan, 2014). For example, Waisberg and

Kaushik (2009) presented a Web analytics process that can be followed

as a hands-on process. As a holistic approach, its use for Website

analysis is based on several sources of knowledge. This possibility

made it plausible to incorporate the Big Data Integrative Module

(BDIM) that can use the Big Data Architecture (BDA) as sources of

knowledge. Earlier on Phippen et al. (2004), had applied the Web

analytics technique for the effective evaluation of online strategies

towards e-Commerce success. For Fagan (2014), what was done

differently was to use the leverage of Web analytics data to examine

user behavior. The work highlighted the plausibility in the need to

explore the potentials of the Web analytics for user-centric evaluation.

It is interesting to note that the potentials of Web analytics as

underscored in literature (Chen et al., 2012), leaves researchers with

little or no choice to want to adapt its leverage. For example, the current

Web analytics encompass social search and mining, reputation systems,

social media analytics, and Web visualization capability. In addition,

some of the other promising research directions that is related to Web

analytics include Web-based auctions, Internet monetization, social

marketing, and Web privacy/security (Chen et al., 2012). In the health

sector, the technique of Web analytics has been used to examine online

public reports of quality. Researchers often used key performance index

like how visitors find reports and the purpose of users visit (Bardach et

al., 2015). Thus, based on user-centric conceptualizations, the Web

analytics techniques were used to improve search engine traffic, cost


331

information and Website experience for both consumers and health care

professionals (Bardach et al., 2015).

Recently, the Uc concept has been used as a key concept to guide

the formulation of the M-learning Usability and User-Experience

(MUUX-E) evaluative framework. The focus was on the evaluation of

interface usability and user experience in the mobile educational

contexts (Harpur and de Villiers, 2015). In this paper, what is done

differently from the work reviewed so far, is the synergistic

combination of the Web analytic technique with other useful evaluative

elements. These evaluative elements are methods that were included the

proposed framework to provide a broader context and sources of data

to evaluate I&AS.

3. Theoretical Underpinnings

The Uc as a concept is a major component of Human Computer

Interaction (HCI) theoretic. It emphasizes user-centric design, iterative

and ongoing evaluation, and the awareness of the context in which users

interact and use systems. This context can be examined based on how

users’ interactive goals with computing systems are satisfied on

completing specific tasks (Dix et al., 2004, and Preece et al., 2015).

This paper makes contribution. The contribution of this paper is in the

information system domain, with a focus on the evaluation of I&AS.

This paper drew on the constructivist view of the Theory of Information

Processing (TIP) (Gao et al., 2012) to formulate the proposed

framework.

The concept of Web Analytics technique (WAt), the Knowledge

base - conception of - HCI (KbHCI), and the Big Data Integrative

Module (BDIM) are the specific contributions of the framework. This

contribution is novel since the components of the framework

(particularly the KbHCI and BDIM) - though exist in literature

(Fischer, 2001; Bilal et al. 2016) - have not been leveraged nor so

suggested for user-centric evaluative practices with the WAt as

postulated in this paper. The TIP highlights the decision making ability

of users of computing artifacts. The trust of the WAt is using the

leverage of users’ behavioral patterns that exist on the Web. We believe


332

these patters irrespective of how consistent they are, cannot be enough

to make useful deductions. Users must have made very useful decisions

at one point or the other when using a computing artifact.

To materialize the proposed framework, the TIP was extended by

integrating the Unconscious Thought Theory (UTT). The rationale for

this stems from the need to stretch the TIP in order to situate its

constructivist view. This highlights the need to use the leverage of

users’ opinion for systems development so as to achieve better user-

orientation. This is in tandem with the beliefs enshrined in the user-

centered design theoretic which the KbHCI brings into the proposed

framework. Cognizance of this body of belief; the UTT was meant to

strengthen the basis to define KPIs. In this paper, the KPIs represent the

decision variables that will inspire the various metrics that would accrue

from the whole exercise of using the framework. Its introduction was

also meant to highlight the fact that the proposed framework is limited

to only the summative evaluation of computing artifacts. This is

because users who will be involve in the evaluative exercise for which

the framework will be meant should have used the computing artifacts

for task intensive work. So, with the UTT, it is noteworthy to stress that

the framework will be useful to elicitate users experiences as a result of

their use of a system. This is because the UTT suggests that

unconscious thought do help users achieve better performance in

complex tasks, than conscious thought (Dijksterhuis and van Olden,

2006).

Recent studies have shown that unconscious thought has superiority

over conscious thought in complex problem-solving situations

(Dijksterhuis et al., 2006). The nearly unlimited processing capacity of

Unconscious Thought (UT) enables users to process large amounts of

information especially in complex situations since it uses a bottom-up

principle (Dijksterhuis and Nordgren, 2006). This implies that UT

makes users form an impression based on the available information. In

this way, UT can lead to better integration of all information

(Dijksterhuis and Nordgren, 2006) and help form reasonable

expectations, which may lead to higher satisfaction after the use of

computing artifacts. The foregoing postulations highlight the goal of


333

the proposed framework. This goal is the provision of rich evaluation

findings that can be used to improve I&AS and other computing

artifacts when properly contextualized.

4. The Proposed Framework

The rationale for integrating the Context, Content, and Process

(CCP) frame and KbHCI module in the proposed framework comes

from the desire to adopt a broader evaluative approach. This approach

includes the performance of the activity of Identify, Conceptualize and

Operationalize (ICO). The ICO is a qualitative and quantitative research

methodology, where proposed decision variables are first explained in

other to find the right variables to elicit user-centric data from users.

These activities - ICO are carried out in Phase 1 of the framework (see

Figure 1). This is because I&AS and modern day Web based systems

are context oriented and can be used across multiple platforms. The

choice of the CCP, KbHCI and Big Data Integrative Module (BDIM)

as part of the framework is validated by existing reviews of both

Information system, HCI, and Big Data literature (Irani, 2008; Fischer,

2001; Bilal et al. 2016).


334

Market

Structure

Content Context

Process

Why

is the

evaluation

being done?

What

is being

eva1?

When

is the period

f eva 2?

How

is the eva

2 to Be Carried

out?

Who

effects the

eva 2 & how?

Cultural

Influences

Political

Issues

Corporate

Culture

Structure

Social

Influences

Internal Environment

External Environment

Industry

Sector

Economic

SituationTechnology

GlobalizationDigitization

Government

Policies

CCP

Define

Goal

Build

KPIs

Collect

Data

Analyze

Data

Implement

Changes

Phase1

Phase3

Phase2

KbHCI

WAt

Legend

BDA: Big Data Architecture

BDIM: Big Data Integrative Module

KbHCI: Knowledge-based HCI CCP: Context, Content, and Process frame

KPI: Key Performance Index (or measure)

WAt: Web Analytic techniqueeva1: evaluated

eva2: evaluated

Sensor Data

Images Databases LocationsEmails

Click Stream

Social Media

HTML

BDIM Using BDA

Actionable

Intelligence

Big Data

Figure 1: The proposed framework

In the reviews it was possible to identify the rich vein of work that

considers the - what, why, who, how and when - factors of evaluation.

As advocated by evaluation researchers (Lagsten, 2011; Tanner and

Mackinnon, 2015) within the constructivist paradigm; the CCP

concepts highlight the recognition of a wide range of factors that need

to be taken into account for effective evaluation (Irani, 2008). These

factors are intertwined. For instance, how an evaluation will be carried

out and when (the process) are closely informed by what is being

evaluated (the content). Interestingly, these factors are affected by the

different perceptions of the stakeholders involved. The who, and the

reason for the evaluation (the context) are also important. Even, the

internal and external environments - the contexts of the projects, and

the stakeholders are important. They inform the entire evaluation


335

process (Irani, 2008). The CCP contributes a parsimonious method to

the overall framework. It makes the entire framework useable in a wide

variety of evaluation situation(s). The complexity required to inform an

effective evaluation process that bothers on the quantitative aspect was

out using the WA methodology. This complexity is well acknowledged

in literature (Irani, 2008).

In order to situate the constructivist aspect of the framework; the

KbHCI module was incorporated. This was necessary to highlight the

essence of Uc, and make it practicable. For instance, computer usage

was traditionally modelled as a narrow explicit communication channel

as text-based terminals in a time-sharing environment. But, the advent

of more sophisticated interface techniques, such as windows, menus,

pointing devices, and touch-screens have changed things. They allow

higher levels of user-system interaction (as typified by the implicit

communication channel) as against the explicit communication channel

(Fischer, 2001). The possibilities of new design prospects based on the

implicit communication channel (which realities are already with us)

highlight the potential of exploring the Knowledge-based Architectures

for HCI (KbAfHCI).

We believe the possibility of an implicit communication channel -

back and fort from human knowledge to knowledge base (see Figure 1)

- can be so harnessed. Like Fischer (2001) had postulated, we argue that

the implicit communication channel can support communication

processes that requires the computer to be provided with considerable

body of knowledge about problem domains, about communication

(information) processes, and about the agents (users) involved. This

presents the desire to want to take advantage of the Big data (the first-

stop KbAfHCI), which has accumulated actionable intelligence over

time between users myriad of interaction (human knowledge) and

knowledge base (that contains information from various sources -

HTML, sensor data, e-mail, social media and so on). These actionable

intelligence (see Figure 1), can be harnessed through the BDIM using

the BDA for useful evaluative purposes. This framework is flexible in

that aside the use of the Wat to provide the requisite guide towards data

analysis; users of the framework can use any other quantitative guide.


336

The proviso to note is that whichever data analytic guide that is chosen,

must be compatible with the BDIM.

5. Conclusion

The evaluative framework suggested in Figure 1 is intended as

contribution to the evaluation of I&AS, and other system, which must

be properly contextualized. To say that the framework will be the final

and only mantra to contemplate in the quest for user-centric evaluative

framework will not be correct. The advent of the Internet and Web-

based systems have brought so many dynamics into the mental

conception of how computing based systems are perceived by users.

I&AS and other computing systems are to be conceptualized therefore

as dynamic systems that are subject to evolve. So, there will be the

continuous need to adjust, extend (if necessary), and add to the

framework as occasion warrants and as new contexts show up. The

framework in Figure 1 has the potential to deliver parsimonious

evaluative user-centric models, and evaluative results that can inform

the development of more engaging information systems that are I&AS.

Additionally, the conceptual information provided so far can stimulate

further debate in the domain of user-centred evaluative research.

However, the proposed framework is only conceptual. It still needs

to be tested. As part of future work, the framework in a summative

evaluative exercise will be used to evaluate computing artifacts. The

framework is also silent on issues of how to derive the criteria to be

used in the practice of system evaluation. Although, the issue of context

and content were taking care of, that of constructs and criteria are left

to the discretion of the user of the framework and what they will arive

at considering the context of use.

In conclusion it is worthy of note that this paper is basically

descriptive, and not prescriptive. The postulations provided are to be

followed with further research and empirical testing. The framework

therefore will present a useful test bed for further studies on user-centric

evaluation.


337

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Transition from Observation to Knowledge to Intelligence (TOKI) Conference is a forum that allows researchers from the fields of Competitive Intelligence, Internet of Things (IoT), Cloud Computing, Big Data and Territorial Intelligence to present their novel research findings and results. Common to all these fields are the concepts of information, information systems, knowledge, intelligence, decision-support systems, ubiquities, etc. The relevance of research findings, results obtained, systems developed and techniques adopted in these research fields for both the industries and government cannot be overemphasized.

Therefore, the Conference welcomes contributions in the following areas:

Smart Cities: With focus on Intelligent Transportation Systems, Observatory Systems, Smart Electricity Grids, building automation, assisted living and e-health management systems. Areas such as application of Geographical Information Systems, Territorial Intelligence and Sensors are also considered.

Big Data Analytics: This includes Big Data, Information Visualization, Data Analysis and related applications.

Semantic Web: Standardized formats and exchange protocols for web based data.

IoT Analytics: These center around innovative algorithms and data analysis techniques for extracting meaning and value from the Internet of Things.

Resource Management: This includes energy saving techniques, effective and efficient utilization of resources, intelligent data processing, mining, fusion, storage, and management, context awareness and ambient intelligence.

IoT Enabling Technologies: These center around technologies that drive pervasive / ubiquitous systems some of which include but not limited to IPv6, NFC, RFID and Microprocessors.

Interoperable and Adaptive Information Systems: These include but are not limited to Decision Support Systems, collaborative and co-operative systems and other forms of systems that support interfacing of multiple elements and entities.

Mobile IoT: Smart phone applications for generating and consuming data, crowd sourced data, e-commerce, mobile advertising, B2B, B2C and C2C connectedness.

Cloud Computing: Including security, storage and access to data stored in the cloud;

service provisioning and resource utilization; cloud communication protocols;

interoperability among users and devices with respect to linked data.

Editors Prof. Amos DAVID & Prof. Charles UWADIA

9782954676036

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