Project based Learning to Support Enterprise Business Analytics

Education

The Role of Cross Functional Groups to Enhance Cognitive Outcomes

Biswadip Ghosh

Dept. of Computer Information Systems, Metropolitan State University of Denver, Colorado, U.S.A.

Keywords: Business Analytics, Project based Learning, Cognitive Outcomes, Motivation Theory.

Abstract: Enterprise business analytics (BA) tools have gained significant attention as a viable option for

manipulating large data sets during complex business decision making. However, the cross-functional,

boundary spanning nature of these applications make them particularly difficult to learn for users, who

predominantly work in functional silos. A typical enterprise BA project involves aggregating large datasets

from multiple functional areas, discovering relationships in the data and building models to help visualize

and evaluate the selected key performance indicators (KPI). However, most BA learning programs

emphasize tool procedural or skill based knowledge, which does not allow end users to understand the

broader scope of enterprise analytics project implementations. Cross functional group project based learning

programs are needed to provide real world experiences, increasing the end user’s motivation to learn and

enhancing their cognitive outcomes. There is also a need to create validated models to assess the outcomes

of these learning programs. This research study develops and conducts an innovative project based learning

program among the users of a leading ERP vendor’s analytics tool and collects survey data to confirm the

benefits of such group project based learning programs in enhancing the participant’s motivation to learn

and improving their cognitive outcomes that emphasize cross functional concepts.

1 INTRODUCTION

Organizations generate enormous amounts of

operational data that contain valuable patterns,

relationships and business information. In seeking

improvements in their decision-making processes,

more and more organizations are turning to data-

driven decision making (Gartner, 2013). Business

analytics (BA) applications are specialized tools for

data analysis, query, and reporting that support

organizational decision-making (Chaudhuri, Dayal

and Narasayya, 2011). These tools enable

interactive access and manipulation of data in order

to gain valuable insights and can support

management decision making processes across a

broad range of business functions.

The organizational benefits of BA is mainly

gained by transcending the immediate focus for

achieving functional optimizations, that are “silo”-ed

and localized. This is accomplished by utilizing BA

applications that can aggregate cross functional

datasets extracted from other enterprise systems

such as Enterprise Resource Planning (ERP) or

external big data sources, to create new organization

wide capabilities. Examples include supply chain

management (SCM) integrated with customer

relationship management (CRM) for KPI’s (Key

Performance Indicators) that support “360 degree

views” (IBM, 2014). Successful BA

implementations require: (i) a holistic approach that

span multiple functional areas of the business, (ii)

identification and modelling of suitable KPI’s in the

chosen BA tool, (iii) adoption of data lifecycle

management practices for collection, cleaning,

aggregation and refresh activities, (iv) learning

programs to help users leverage BA technology to

manipulate large data sets and operationalize

analytics algorithms and (v) effective end-user

support. Organizational benefits can vary

significantly depending on the level of training and

insight of the business leaders, who are the ultimate

stakeholders of the BA applications (Bose, 2009).

There are three targeted goals of most end-user

learning programs (Gupta, Bostrom, Huber, 2010):

(1) skill-based goals (tool procedural) that target the

user’s ability to use the system, (2) cognitive goals

Ghosh B..

Project based Learning to Support Enterprise Business Analytics Education - The Role of Cross Functional Groups to Enhance Cognitive Outcomes.

DOI: 10.5220/0005341600050013

In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 5-13

ISBN: 978-989-758-108-3

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

(tool conceptual or business procedural) that focus

on the use of the system to solve business problems

and (3) meta-cognitive goals that focus on building

the individual’s beliefs regarding their own abilities

with the system. The typical BA end-user learning

programs are focused on skill-based procedural

outcomes. Such programs do not allow the users to

grasp the cross functional knowledge needed to

identify the relationships in the data and select

“holistic” KPI’s, which transcend the domains of

their functional work area (Gupta, Bostrom and

Huber, 2010). Instead focusing the learning

program on cognitive outcomes (rather than skill-

based outcomes) could build business-conceptual

“big picture” knowledge that allows the users to

apply the BA tool to solve enterprise wide business

problems (Macris, 2011).

In a cross functional project based learning

approach, the users are placed into small groups and

asked to define and address a “real-life” business use

case or scenario for the BA tools. The individuals

learn from the knowledge of group members, who

come from different functional areas to define a set

of cross functional KPI’s and build a logic driven

model that can be used to measure the KPI’s. They

share and combine their individual learning to

support building a “big picture of the organization”

and the collective group discourse (Wang and

Ramiller, 2009). They proceed to identify diverse

sources of data from across the organization. Such a

learning approach holds promise to address the

difficulties of applying BA tools to improve business

outcomes in individual processes without adversely

impacting broader organizational performance

(Chang and Chou, 2011).

The focus of the research on the assessment of

technology mediated learning programs has been on

how various factors such as types of tools,

instructional methods, the target system and

individual differences influences individual learning

outcomes (Bostrom, et.al, 1990). Compeau, et.al.

(1995) proposed a framework of key factors in the

management of end-user training that highlights

different phases of training such as initiation, formal

and informal and post training and addressed the

issue of transfer of learning to the workplace.

Project based training, which emphasizes the casual

transfer of knowledge among group members,

blends the formal and informal phases. PBL

participants learn from each other as well as from

the program content (Marcris, 2011; Leidner and

Jarvenpaa, 1995) and execute the learning program

in a genuine setting. It has been difficult to assess

cognitive outcomes of BA training during the

learning period as benefits need to be measured over

time, post learning, once the users are back on their

jobs (Gupta, Bostrom and Huber, 2010). Published

end user learning research does not report any

suitable measurement models that can be used

during the learning period to make cognitive

outcome assessments, thus creating a gap in the

research literature. The authenticity of the

environment posed by project based learning, which

demands participants execute genuine workplace

tasks, supports the assessment of cognitive outcomes

(Santhanam and Sein, 1995). Developing a

framework to allow end users to self-assess the

outcomes at the end of the training program holds

promise.

2 RESEARCH GOALS

The goals of this research are to develop an

innovative project based BA learning program that

can enhance the participant’s motivation to learn

along with a measurement model (extended from the

Technology Mediated Learning (TML) framework

in Gupta, Bostrom and Huber, 2010) to explore the

factors that contribute to higher cognitive outcomes.

Using a field study among participants, who took

part in an innovative project-based learning

program, this study has the following research goals:

1. Ascertain the benefits of project based learning

for BA tools on individual motivation and

cognitive outcomes.

2. Build and validate a measurement model of end

user BA learning that extends the TML

framework by adding a construct for group

interactions.

3. Validate that the above model can successfully

predict the level of cognitive outcomes of the

participants.

4. Understand the relationships between model

constructs - individual motivation, group

interactions and project based learning program

characteristics on cognitive outcomes.

3 BACKGROUND THEORY

In the Technology Mediated Learning (TML)

framework, the learning structures (or scaffolds)

support the delivery of the learning content (Gupta,

Bostrom and Huber, 2010). The learning structures

together with the content impact the learning

outcomes of the participants. Individual differences

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

such as motivation, also play a role in how the

learning program can impact each end-users’

learning process and outcome (Gupta, Bostrom and

Huber, 2010). Enterprise BA application users must

grasp and integrate cross-functional knowledge so

they can communicate and work cooperatively with

users in other business functions (Wang and

Ramiller, 2009). Based on situated learning theory,

effective group learning programs must require that

group members reflect upon their learning and

contribute their experiences, observations and

insights back into the group’s collective discourse in

a team-based collaborative setting (Wang and

Ramiller, 2009). Such learning content also fosters

joint work, the need for business problem solving

and reflection and sharing of insights among the

team members (Ryan and Deci, 2000).

Shared cognition theory focuses on individual

learning within a social situation, allowing for social

interactions that support the individual’s cognitive

development with help from more capable team

members and peers. Each participant brings their

own experience and expertise to share their

knowledge with the team. There is a constant

interaction and collaboration among participants that

allows each individual to develop more improved

skills in solving problems, than if they worked alone

(Sharda, et.al., 2004). The joint experience allows

each participant to explore the scenarios from other

user’s perspectives and helps them to create new

meanings and explanations through shared

understanding and practical use to perform specific

tasks (Chang and Chou, 2011).

Motivation theory has also been often used to

understand the individuals’ IT adoption and learning

behaviour (Ryan and Deci, 2000). Motivation

theory suggests that individual behaviour is

determined by two fundamental types of motivation:

extrinsic (utilitarian) motivation and intrinsic

(hedonic) motivation (Ryan and Deci, 2000).

Extrinsic motivation refers to performing an activity

because it is perceived to be instrumental in

achieving valued outcomes that are distinct from the

activity itself, such as improving job performance,

pay, or promotion (Ryan and Deci, 2000). Extrinsic

motivation has been found as significant predictors

of BA tool adoption (Igbaria, Parasuraman and

Baroudi, 1996). On the other hand, intrinsic

motivation emphasizes the importance of having an

enjoyable and playful learning experience (Sallam,

et, al., 2011). Intrinsic motivation refers to

performing an activity for no apparent reinforcement

other than the process of performing the activity per

se, such as participation in learning (Ryan and Deci,

2000). In the context of learning new technologies,

extrinsic motivation emphasizes an individual’s

personal gain associated with the technology (Ryan

and Deci, 2000).

4 RESEARCH MODEL

The research model is displayed in Figure 1. The

research constructs along with research hypotheses

are defined in the following subsections.

Figure 1: Research Model.

4.1 Cognitive Outcomes (CO)

Cognitive outcomes (CO) focus on the mental

awareness and judgments of the participants. If

cognitive outcomes are emphasized in the learning

program, then the participants build the capability to

apply their learning in real world scenarios (Gupta,

Bostrom and Huber, 2010). They grasp the path to

apply the acquired knowledge of BA tools and

methods towards effective modelling and analysis of

businesses scenarios so that appropriate KPI’s can

be selected and calculated from organizational data.

Cognitive outcomes also include the growth of self

confidence to allow the transfer of the learning to

new situation that require understanding the

ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto

EnhanceCognitiveOutcomes

interactions of multiple parts of a complex

organization.

4.2 Project based Learning (PBL)

Impacts CO

Project based learning (PBL) content refers to

instructional methods that encourage users to work

together to accomplish shared goals, beneficial to all

(Marcris, 2011; Alavi, Wheeler and Valacich, 1995;

Leidner and Jarvenpaa, 1995). PBL is a

constructivist approach that engages participants in

solving real world problems. Learning structures

refer to the scaffolds that support the delivery of the

content. Learning from peers is an important

component in project based learning as “peers

contribute to task orientation, persistence and

motivation to achieve” (Leidner and Jarvenpaa,

1995). To mimic real-world problems, which are

typically ill-structured, the assigned projects should

be loosely defined initially to require the groups to

collaborate extensively in order to characterize the

project scope. When the project groups are given a

great deal of autonomy to design and build their BA

project, it forces the participants to grasp integrative

knowledge so that they can communicate and

cooperate closely with other members (Chang and

Chou, 2011). Collaborative project based learning

(PBL) content refers to the presence of these

characteristics of collaboration – joint work, the

need for business problem solving and reflection and

sharing of insights among the team members (Alavi,

Wheeler and Valacich, 1995). The learning program

must also put forward and emphasize a rigorous and

proven methodology to launch the group efforts on a

strong foundation. This way, the groups understand

the process to navigate the typical pitfalls of a BA

project - conflicting goals, contexts, obstacles and

unknowns. The content and structure of the PBL

program influences group work, therefore, we state:

Hypothesis #1: Project Based Learning (PBL) has a

Significant Positive effect on the Level of Group

Interaction (GI).

BA applications can offer several benefits that

include improving timeliness and quality of the

decision making process, providing actionable

information delivered at the right time, enabling

better forecasting, helping streamline operations,

reducing wasted resources and labor/inventory costs,

and improving customer satisfaction (Chaudhuri,

Dayal, Narasayya, 2011; Yeoh and Koronios, 2010

and Negash, 2004). The repeated interaction

between participants in the project groups creates a

set of norms, trust and mutual understanding that

bind the participants together and facilitate better

interactions, both during and post learning (Chang

and Chou, 2011). These project interactions allow

the members to exchange practical knowledge and

fill in the gaps in their understanding of the BA

application and cross functional impacts. The

knowledge sharing and repeated group interactions

fostered by the PBL program during the

collaborative group project promotes greater

cooperation, bridges gaps in understanding and

increases cognitive learning outcomes (Chang and

Chou, 2011). The users learn the practical use of

BA tool and methods by participating in genuine

real world experiences.

Hypothesis #2: Project Based Learning (PBL) has a

Significant Positive Effect on the Level of Cognitive

Outcomes (CO).

Problem based learning that uses authentic,

complex scenarios created an impetus for learning in

order to apply that knowledge to solve the problem

assigned (Uribe, Klein and Sullivan, 2003). Group

projects require individuals to cooperate and work

together but have significant learning benefits of

efficiency and productivity (Baskin, Barker and

Woods, 2005). Such projects allow individuals to

learn to face authentic situations, to share multiple

perspectives and support each other to accomplish a

greater outcome by imposing control over individual

behaviours to meet the group’s expectations of

performing group assigned roles. Group projects

become agents of socialization and control and act

as a motivational tool (Baskin, Barker and Woods,

2005). Therefore, we expect that if individuals

perceive these benefits of project based learning,

they may become more motivated to learn and

effectively utilize BA applications.

Hypothesis #3: Project Based Learning (PBL) has a

Significant Positive Effect on the level of Individual

Motivation (MV).

4.3 Group Interactions (GI)

Group theories suggest that many factors can

influence the outcomes of group-based learning

(Sharda, et.al., 2004). This includes group

characteristics, such as composition (level of

homogeneity and heterogeneity), amount of group

cooperation and the nature of group

communications. Group influence has been found

to emanate from a variety of sources (Agarwal,

2000; Lewis, Agarwal and Sambamurthy, 2003),

including co-worker, supervisor, and friends. In

working organizations, co-workers and supervisors

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

are influential in determining technology acceptance

behavior (Schmitz and Fulk, 1991).

For successful cognitive outcomes, group

interactions must be optimized along with training

content and delivery structures (Sharda, et.al, 2004).

In collaborative group learning, the team members

share goals and learn together by working jointly

and solving the problems posed by the project. The

group interactions play a critical role in the learning

environment through the size and heterogeneity of

the team. The more diversity in the team, there is

more likely to be integration of knowledge from

multiple functional areas. Research has shown that

when team members are from differing

backgrounds, the discussions and knowledge sharing

is more intense leading to create more group

decisions (Sharda, et.al., 2004). Group interactions

impact learning outcomes by developing diverse

knowledge and building broader perspectives that

span business functions (Seethamraju, 2008).

Hypothesis #4: The Level of Group Interaction (GI)

has a Significant Positive Effect on the level of

Cognitive Outcomes (CO).

Group interactions (GI) comprise factors such as

if team members shared diverse view points and if

such interactions were valued as well as the nature

of cooperation and the level of dialog achieved

within the team. Greater cooperation and dialog

among a diverse team allows them to build

identification giving them a broader vision to further

enhance cross functional learning (Chang and Chou,

2011). In group based training programs, team

members from different functional areas work

together and influence each other’s motivation by

voicing demands for contributions. The level of

interaction within the group also facilitates

individual engagement with the learning program.

Hypothesis #5: The level of Group Interaction (GI)

has a Significant Positive Effect on the level of

Individual Motivation (MV).

4.4 Individual Motivation (MV)

Individual differences influence the formation of

mental models, which represent the outcomes of the

learning process (Gupta, Bostrom and Huber, 2010).

“States” (such as motivation) are general influences

on performance that vary over time and include

temporal factors such as motivation level and

interest level while “traits” (such as preferred

learning style are static aspects of information

processing affecting a broad range of outcomes over

time (Bostrom, Olfman and Sein, 1990).

Motivation theory has been used often to

understand individuals’ IT use and learning

behaviour (Van Der Heijden, 2004; Tharenou,

2001). Motivation theory suggests that individual

behaviour is determined by two fundamental types

of motivation: extrinsic (utilitarian) motivation and

intrinsic (hedonistic) motivation (Alavi, Wheeler,

and Valacich, 1995). In the context of project based

learning programs, the individual characteristics

from the TML framework is measured using

individual motivation as states and individual

learning style as traits. (Note: individual learning

styles is used as a demographic variable and is not

part of the research model of this study

Hypothesis #6: The level of Individual Motivation

(MV) Moderates the Relationship between Project

based Learning (PBL) and Cognitive Outcomes.

(CO).

5 DATA COLLECTION

The study involved a 4 week face to face learning

program for a leading vendors BA tools. There were

74 participants in the 4 week program and they were

provided 2 hours/week of instruction about analytics

methods, principles and case studies as part of the

theoretical portion of the learning program. This was

coupled with a practicum that required the

participants to use the CRISP-DM (www.crisp-

dm.eu) methodology to define and implement a

business analytics project. As the methodology

requires the involvement of business users to help

define user scenarios, the participants were given

access to a BA consultant and clients in the energy

industry. A large data set was extracted from the

client company’s ERP system and provided to the

participants to work with. The data set contained

financial, production (OPEX), materials, exploration

project management (CAPEX), human resources,

and operational maintenance, training and safety

data.

The 74 participants were divided into small

groups (4-5 members) and assigned a BA project

scope, such as human resources, supply chain,

financial management and energy exploration. The

first objective of the participants was to thoroughly

understand, from a business perspective, what their

assigned business customer really wanted to

measure and accomplish with the BA project. The

participants documented the business use cases and

made decisions on how to utilize the data set to

support the KPI’s deemed necessary by the business

ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto

EnhanceCognitiveOutcomes

user. The groups then designed and built BA

dashboards that displayed the functional variables

and relationships (in the data). They designed

quantitative KPI models to add “what-if” scenarios

with the BA tools. Contacts in the client energy

company and the BA consultant were available

during the entire duration of the project to answer

questions and review project scope and designs.

Three formal face to face review meetings were

arranged to review dashboard projects at weekly

intervals with the BA consultant to establish a

realistic performance expectations.

The project work was supplemented with

lectures on various topics such as business analytics

models and case studies, requirement gathering and

documentation, dashboard design, data modelling

and management, and project management. The

sequence and content of the 4 week project based

BA learning program is listed in Table 1. A survey

was administered at the end of the 4

week after the

projects were submitted by the groups.

Table 1: Ba learning program schedule.

Wk Theoretical Topics Practical Group Work

1. BA Case Study (Ghosh

and Scott, 2011)

2. Business Use Cases &

Requirements

3. CRISP-DM

4. Data Visualization

1. Analyze Case Study and

Understand nuts and bolts

of a BA Project

2. Use Visualization Tool on

data sets to understand data

relationships.

5. Key Performance

Indicators (KPI)

6. Modelling approaches

7. Information Lifecycle

and Data Quality

3. BA Tool training

4. Identify suitable KPI for

the use cases

5. Build logic based model

8. Data management and

storage options (ETL)

10.Predictive Analytics and

data Relationships

6. Identify Input data and

sources

7. Data Storage Design

8. Build and test prelim project

& user reviews

11. Unstructured data, Text

Mining, Big Data, Real

Time Analytics

12. BA project Feasibility

9. Add “what-if analysis”

model to BA project

10.Build, test, final project

11. Project Retrospective

6 ANALYSIS AND RESULTS

The demographic data of the participants is listed in

Table 2. It shows that the preferred learning style of

the participants was “learning by doing”, followed

by “learning by thinking and watching”. All of

these learning styles are supported by the group

project based learning format used in this BA

program. Some of the participants commented on

the group interactions and the need to interact to

define the projects.

The survey data was analyzed with SPSS to

ascertain measurement validity of the multi item

survey constructs. The constructs – Group

Interactions (GI), Intrinsic Motivation (IM),

Extrinsic Motivation (EM) and Cognitive Outcomes

(CO) were modelled as reflective, while the Project

Based Learning (PBL) construct was modelled as

formative. The Individual Motivation (MV)

construct was modelled as a second order construct

using the IM and EM constructs. The measurement

model, the paths and relationships among the

constructs were tested with Smart-PL structured

equation modelling (SEM) software to test the

hypotheses.

Table 2: Survey Demographics (Total =74 responses).

Variable Mean Standard Deviation

Hrs/week

on Project

4.43 hours 1.74 hours

Preferred

Learning

Styles

Learn by Doing (48); Learn by Thinking (27),

Learn by watching (22); Learn by Feeling (7)

(Note: Some users Selected multiple learning

styles)

BA Project

Scope

Human Resources (19); Supply Chain (21);

Financial Controlling (22), Energy Exploration

Operations (12)

Participant

Comments

about

Project

Experience

1. Needs more definition; 2. Defining the

scope of what to accomplish made it difficult;

3. Interesting project. 4. Most difficult part was

dealing with the messy data. 5. Project was

frustrating and at first, then we figured it out;

6. Working with 3rd party was difficult; 7.

Smaller groups would allow faster design. 8.

Group work was very helpful.

In PLS, validation is done using the composite

reliabilities (CR) and average variance extracted

(AVE) from the measurement model. The CR

should be greater than 0.7. The AVE measures the

variance captured by the indicators relative to

measurement error and it should be greater than 0.5.

Moreover, the square root of each construct’s AVE

needs to be greater than the correlation of the

construct to the other latent variables to demonstrate

discriminant reliability. As seen from Table 3 and

4, the composite reliabilities for all measures were

high ranging from 0.7045 to 0.9088. Moreover, the

AVE values are above 0.5 and the square root of the

AVE of each construct is greater than the correlation

of that construct with other constructs, respectively

(Table 3). Consequently, evidence for internal

consistency and reliability of the measurement

model are supported by these results (Tables 3 & 4).

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

Table 3: Latent Variable Correlations & Square Root of

AVE (in bold).

EM GI IM CO PBL

0.8184

GI 0.4327

0.7824

IM 0.6217 0.4748

0.9088

OUT 0.5838 0.4488 0.6275

0.8054

PBL 0.6233 0.6068 0.6270 0.7019

0.7045

Smart-PLS, v2.0, was used to test the 6 research

hypothesis in the model. A bootstrap re-sampling

procedure was conducted using 200 samples and

path coefficients were re-estimated using each of

these samples (Chin, 1998). The results of the

hypotheses testing is shown in Table 5 and indicate

support for 5 hypothesis at the 99% confidence

level. The hypothesis H4 (Group Interactions with

Cognitive Outcomes) was only supported at the 95%

confidence level (not at 99% confidence).

Table 4: Measurement Model Reliability, R-square.

Construct AVE Composite

Reliability

R-Square Cronbach

Alpha

GI 0.6122 0.8873 0.3682 0.8439

IM 0.8260 0.9047 n/a 0.7895

EM 0.6698 0.8143 n/a 0.6907

CO 0.6488 0.8805 0.6344 0.8189

PBL n/a n/a 0.4964 n/a

MV n/a n/a 0.4931 n/a

Table 5: Hypothesis Testing (99% significance).

Hypothesis Path StdErr T-Stat Sig

H1: PBL -> GI 0.6254 0.0651 9.3268 YES

H2: PBL -> CO 0.4385 0.2777 1.9690 YES

H3: PBL -> MV 0.4419 0.1175 3.8873 YES

H4: GI -> CO 0.3447 0.2589 1.6956

H5: GI -> MV 0.3215 0.1498 2.2727 YES

H6: PBL * MV-> CO 0.2516 0.0576 2.5270 YES

*- hypothesis H4 significant at 95% confidence

The results of the PLS analysis finds evidence to

support the notion that project based BA learning

programs promote strong group interactions that

drive to increase participant motivation. The

contents of the learning program, such as the use of

authentic real-world scenarios, the involvement of

external business people and the diversity in the

participant backgrounds supports building higher

cognitive outcomes of the participants. The level of

individual motivation is also found to moderate this

relationship between PBL and cognitive outcomes.

The level of group interactions also increases the

level of cognitive outcomes (at the 95% confidence

level).

7 CONCLUSIONS

Many organizations find that data-driven decision

making is difficult to implement due to reasons such

as poor quality and incompatibilities of transactional

data, complicated algorithms to process the data and

the end-user time involvement necessary for

learning to use the analytics techniques and tools.

Business analytics (BA) applications have gained

significant attention as a viable option to incorporate

the use of large sets of data to address the challenges

of making complex business decisions. Market

leading BA tools have the potential to facilitate data

driven decision making by allowing easier data

manipulation, visualization and processing.

However, the complexity and diversity of BA tools

and their functional boundary spanning nature make

their learning difficult at the individual level. Recent

research has found that individual motivation and

group support as important determinants that

influence an individual’s decision to learn and use

BI tools.

This study aims to contribute to the body of

knowledge by developing an innovate project based

learning program for BA tools and building a model

to measure the effect of the PBL program on

individual motivation and cognitive outcomes of the

participants. The developed PBL program allows

users to learn the concepts of BA collectively and is

supported by a market leading BA tool. The unique

features of the program are (1) use of actual real

world client data and (2) availability of client

business users to allow the participants to collect

analytics business requirements, (3) the functional

diversity of group members and (4) the iterative

approach to the project development using periodic

reviews. A validated model of measuring BA

cognitive outcomes from PBL is a product of the

study.

7.1 Future Work

While this study was done in a face to face learning

setting, much of today’s end user learning occurs

online. The author plans to extend the study to an

online learning setting with the same BA learning

program to assess cognitive outcomes. The

generalization of the research model to the online

environment will allow its use for Massive Online

Courses (MOOC) and provide a vehicle for

measuring cognitive outcomes of the participants.

MOOCs represent a new educational delivery

opportunity, whose potential pedagogical impact

needs to be researched (Fox, 2013). According to a

ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto

EnhanceCognitiveOutcomes

Gartner survey (Gartner, 2013), business analytics

and analytics were a CIO’s top technology priority

in 2012 and 2013 and it is expected that the global

market for BI tools would reach $14 billion in 2013.

The term “Big Data” is used to refer to the emergent

field of analytics using data being created external to

the company. Currently 4 EB of data are created

each day and this number is doubling every 3 years.

Recent IBM estimates suggest that 4.4 million big

data analytics jobs related to collecting and

processing such data made available through the

internet will be created by 2015 (IBM, 2014). The

possibility to efficiently deploy effective BA

learning to a much wider community by

supplementing online content with real world project

experience presents unique prospects.

REFERENCES

Agarwal, R. 2000., Individual Acceptance of Information

Technologies in Framing the domains of IT

management: Projecting the future through the past,

Pinaflex Publishers, ISBN 97818936709, pp. 85-104.

Alavi, M., Wheeler, B.C. and Valacich, J.S. 1995. Using

IT to Reengineer Business Education: An Exploratory

Investigation of Collaborative Tele Learning, MIS

Quarterly 19(3), pp. 293-312.

Baskin, C., Barker, M. and Woods, P. 2005. When group

work leaves the classroom does group skills

development also go out the window?, British Journal

of Educational Technology 36 (1), pp. 29-31.

Bose, R. 2009. Advanced Analytics: Opportunities and

Challenges, Industrial Management & Data Systems,

109(2), pp. 155-172.

Bostrom, R.P., Olfman, L. and Sein, M.K. 1990. The

Importance of Learning Style in End-User Training,

MIS Quarterly 14(1), pp. 101-119.

Chang, H.H. and Chou, H.W. 2011. Drivers and effects of

Enterprise Resource planning post-implementation

learning, Behaviour and Information Technology 30

(2), March-April, pp. 251-259.

Chin, W.W. 1998. , Issues and Opinions on Structural

Equation Modeling, MIS Quarterly 22(1), pp. 7-16.

Chaudhuri, S., Dayal, U., and Narasayya, V. 2011. An

Overview of Business Analytics Technology,

Communications of the ACM, 54(8), pp. 88-98.

Compeau, D., Olfman, L., Sein, M. and Webster, J. 1995.

End-user training and learning, Communications of the

ACM, 39 (7), pp. 24-26.

Fox, A. 2013. From MOOCs to SPOCs, Communications

of the ACM, 56 (12), pp. 38-40.

Gartner, 2013. Gartner Executive Program Survey of

More Than 2,000 Cios Shows Digital Technologies

Are Top Priorities in 2013, in: Book Gartner

Executive Program Survey of More Than 2,000 Cios

Show.

Ghosh, B., Scott, J. E. 2011. Antecedents and Catalysts

for Developing a Healthcare Analytic Capability.

Communications of the Association for Information

Systems, Volume 29, article 11, pp. 1-11.

Gupta, S., Bostrom, R.P. and Huber, M. 2010. End-User

Training Methods: What we know, Need to Know,

The DATABASE for Advances in Information

Systems 41(4), pp. 9-39.

IBM, “IBM narrows Big Data Skills Gap by Partnering

with More than 1,000 Global Universities”,

http://www-03.ibm.com/press/us/en/pressrelease/

41733.wss Accessed on June 24, 2014.

Igbaria, M., Parasuraman, S., and Baroudi, J.J. 1996., "A

Motivational Model of Microcomputer Usage",

Journal of management information systems, pp. 127-

143.

Leidner, D.E. and Jarvenpaa, S.L. 1995. The use of

Information Technology to enhance Management

School education: A theoretical view. MIS Quarterly

19(3), pp. 265-291.

Lewis, W., Agarwal, R., and Sambamurthy, V. 2003.

Sources of Influence on Beliefs About Information

Technology Use: An Empirical Study of Knowledge

Workers, MIS Quarterly, 27(4), pp. 657-678.

Macris, A.M. 2011. Enhancing Enterprise Resource

Planning users’ understanding through ontology-based

training. Computers in Human Behavior. 27(4), pp.

1450-1459.

Negash, S. 2004., Business Analytics, Communications of

the Association for Information Systems, 13(1), pp.

177-195.

Ryan, R.M. and Deci, E.L. 2000. Intrinsic and Extrinsic

Motivations: Classic Definitions and New Directions,

Contemporary Educational Psychology 25, pp. 54-67.

Santhanam, R. and Sein, M. 1995. Improving End-usr

Proficiency: effects of Conceptual Training and Nature

of Interaction. Information Systems Research 5(4), pp.

378-399.

Sallam, R.L., Richardson, J., Hagerty, J., and Hostmann,

B. 2011., Magic Quadrant for Business Analytics

Platforms", in Book Magic Quadrant for Business

Analytics Platforms, Gartner.

Schmitz, J., and Fulk, J. 1991., Organizational Colleagues,

Media Richness, and Eletronic Mail: A Test of the

Social Influence Model of Technology Use,

Communication Research, 18(4), pp. 487-523.

Seethamraju, R. 2008. Enhancing Student Learning

through ERP Business Simulation Game, Proceedings

of the AIS SIG-ED IAIM Conference.

Sharda, R., Romano Jr., N.C., Lucca, J.A., Weiser, M.,

Scheets, G., Chung, J.M. and Sleezer, C.M. 2004.

Foundation for the Study of Computer-Supported

Collaborative Learning Requiring Immersive

Presence, Journal of Management Information

Systems 20(4), pp. 31-63.

Tharenou, P. 2001. The Relationship of Training

Motivation to Participation in Training and

Development, Journal of Occupational and

Organizational Psychology, 74(5), pp. 599-621.

CSEDU2015-7thInternationalConferenceonComputerSupportedEducation

Uribe, D., Klein, J.D. and Sullivan, H. 2003. The Effect of

Computer Mediated Collaborative Learning on

Solving ill-Defined Problems, ETR&D 51(1), pp. 5-

19.

Van Der Heijden, H. 2004., User Acceptance of Hedonic

Information Systems, MIS Quarterly, pp. 695-704.

Wang, P. and Ramiller, N.C. 2009. Community Learning

in Information Technology Innovation, MIS Quarterly

33(4), pp. 709-734.

Yeoh, W., and Koronios, A. 2010., Critical Success

Factors for Business Analytics Systems, Journal of

computer Information systems, 50(3), pp. 23.

APPENDIX

The Survey Instrument is below:

Average hours/week spent on Project_____

Business Function ___________________

PJ1-The project methodology was well defined from

the beginning

PJ2-I believe the project was a complex and

authentic business scenario

PJ3-The project gave me a lot of autonomy and

freedom

PJ4- The project required joint work and problem

solving

PJ5- The project required the sharing of diverse and

multi-functional insights

CO1-I mastered cross functional knowledge, which

transcends just knowing the technical skills

CO2- I was able to identify learning strategies that I

can use in future projects

CO3-I am confident that I can do another business

analytics project in the future

CO4- The project improved my appreciation of the

value of using business analytics tools

GI1-There was a lot of teamwork and cooperation in

my group

GI2- My contributions were valued by my group

members

GI3-I learnt from the knowledge shared by my

group members

GI4-My group engaged in a lot of dialog and

discussion

GI5-I learned from the different functional

perspectives shared by group members

IM1-I worked hard on the project as I wanted to

learn as much as I could

IM-2-I worked hard on the project as the project

game me a lot of personal satisfaction

IM3-I worked hard on the project as it was very

challenging

EM1-My team mates motivated me to work harder

on the project

EM2-I worked harder on the project as I did not

want to let my team mates down

EM3-I worked harder on the project only to get a

better evaluation.

When I learn …. (Rate the following from 1-4):

I like to deal with my feelings _____

I like to think about ideas ______

I like doing things ___

I like to listen and watch _____

ProjectbasedLearningtoSupportEnterpriseBusinessAnalyticsEducation-TheRoleofCrossFunctionalGroupsto

EnhanceCognitiveOutcomes