Description
Much research has focused on the benefits and impacts organizations perceive from the use of business intelligence tools demonstrating how organizations gain a competitive advantage through the use of Business analytics.
Exploring Factors that influence the
Knowledge Worker in a Business
environment
Coleen Rose GRIFFIN
a1
, Murray SCOTT and William GOLDEN
a
J.E. Cairnes School of Business and Economics, National University of
Ireland, Galway, Ireland
Abstract. Much research has focused on the benefits and impacts
organizations perceive from the use of business intelligence tools
demonstrating how organizations gain a competitive advantage through the use
of Business analytics. Advanced visualization tools lead to better decision-
making as more advanced visualizations allow managers and executives to
make better decisions. However, Big Data presents unprecedented challenges
for the knowledge worker and a new as yet untested environment within which
to work. Independent success factors have been shown to be critical to gaining
overall IS success however, currently there is a lack of focus on what
antecedent factors influence the successful usage of BI systems by knowledge
workers. Therefore, this research proposes to explore the factors that influence
the successful usage of BI systems by Knowledge Workers in a BI and
Business Big Data environment.
Keywords. Knowledge Worker, Big Data, Business Intelligence, System
Usage, IS Success
Introduction
In today’s cost-conscious environment, Ross (1999) asserts that organizations expect
IT to reduce operating costs, show improvements in business processes, standardization
1 Corresponding author
of processes to ensure quality and reducing cycle times, to show data visibility to help
knowledge workers make better business decisions with latest real-time data
1
. The
key to thriving in a competitive marketplace is staying ahead of the competition.
Making sound business decisions based on accurate and current information takes more
than intuition. Data analysis, reporting, and query tools can help business users wade
through a sea of data to synthesize valuable information - today these tools collectively
fall into a category called "Business Intelligence." Research highlights the impact and
relationship of BI capabilities and BI success focusing on how organizations can use BI
systems to achieve measurable financial benefit
2,3
.
BI is used to gain insights that inform business decisions and can be used to
automate and optimize business processes. New BI technologies and Big Data present
great opportunities for organizations to leverage but also create a new set of usage
challenges for the knowledge worker. Big Data i.e., the volume of information, the
variety of data and the velocity of data, creates an emerging problem for the knowledge
worker given the growing need for diverse analytical skills, needed to gain value from
the vast amount of data available. As companies increasingly treat their data as a
corporate asset and leverage it for competitive advantage, the need to create value from
Big Data and new BI tools has intensified pressure on the knowledge worker to
successfully adopt new systems and provide the promised new insights. As a result
understanding successful usage in this new context is key to achieving the desired
outcomes and benefits of Big Data.
In order to achieve the business benefits from IT tools, Staehr et al (2012)
argue that the following conditions need to be achieved for post-implementation
success
4
:
? Influence of early phases in the IT cycle on subsequent phases
? Ongoing resourcing for post-implementation phase to develop in-house
knowledge
? Establishing metrics
? Effective Change Management processes in-place.
? Education and quality training for in-house resources.
? Software to fit the needs of the business.
? People – Availability of experienced and skilled-staff to meet business
benefits.
These factors demonstrate the important role antecedent factors play in the
successful outcome of a system implementation and moreover highlight the critical role
of the knowledge worker/user of the system; for instance, four of the seven conditions
above relate to the knowledge worker. There has been much research focusing on the
business benefits achieved through the implementation of BI tools but there is a gap in
the research relating to the antecedent factors that determine whether such technologies
will be successfully used and deliver the desired benefits. Indeed much prior research
in the area of systems usage has demonstrated that simply providing a technology is not
sufficient for a successful outcome, the needs and context of the users must be
considered.
The primary research question addressed by this study is therefore: What
factors influence the successful usage of BI systems by Knowledge Workers in a
BI/Big Data environment? This research will evaluate the success factors of the
Knowledge worker and identify what are the important antecedent factors influencing
usage success factors from the perspective of the knowledge worker in a relation to
Business Intelligence tools. At the time of writing, this PhD research is in the
literature review phase. This paper is structured in the following manner. Section 2 is
a literature review. Section 3 discusses the research question and objectives. Section 4
discusses the research design and schedule of the research.
1. Literature Review
This literature review develops a framework to guide the research in certain specific
contexts. It starts with a background and terminology associated with the knowledge
worker and success factors related to the knowledge worker. The following section
defines Business Intelligence (BI) and Big Data evaluating the literature then correlates
this to the knowledge worker in the changing IT environment. The study then
evaluates the area of system usage and what antecedent factors may make system usage
successful for the knowledge worker in a Big Data/Business Intelligence environment.
1.1. Knowledge Worker
A knowledge worker is anyone who works for a living at the tasks of developing or
using knowledge. This might be someone who works at any of the tasks of planning,
acquiring, searching, analyzing, organizing, storing, programming, distributing,
marketing, or otherwise contributing to the transformation and commerce of
information and those who work at using the knowledge so produced. Research has
demonstrated that there are a multitude of factors that can influence the knowledge
worker in terms of productivity, job satisfaction, involvement, organizational
commitment, and turnover intentions
5
. Various strategies have been developed in
recognition of the important impact that these factors have on the outcome of IS
implementation initiatives. There were several themes around motivation and
employee empowerment around the theme of the knowledge worker in reference to
leadership and job assignments
6,7
. Gardner and Schermerhorn (2004) discuss the need
and impact of authentic leaders who arouse willingness to work hard to perform at
one’s best
8
. Beneficial employee treatment i.e, fairness, supervisor support, and
favorable job conditions can be linked to positive organizational support from
employees and knowledge workers. Walumba et al (2011) discuss how knowledge
workers can work in remote parts of the world via virtual teams; these workers require
little supervision, enjoy being rewarded for ideas that they generate but require an
authentic leader who values aligned with company mission
7
. Authentic leaders need
to develop deep sense of trust in group members requiring input from team members
7
.
Mak and Sockel (1999) assert job satisfaction is a key factor for IT
employee motivation, which otherwise threatens productivity of IT operations
9
. IT
professionals are motivated by job assignments, appropriate job training workshops
and seminars for learning new technologies. Coelho et al. (2011) believe when
managers promote creativity and innovation in the work environment this creates
increased employee satisfaction
10
. Organizations such as PepsiCo use digital
innovation to deliver more effective merchandising making simpler and cutting
business processes making easier for knowledge worker
11
.
Even if there is strong commitment and motivation from the knowledge
worker, Chatzimouratidis et al. (2011) assert output or results from decision support
systems are meaningless unless methods to analyze the data have been invested via
web-based learning, job rotations, mentorship, case studies, and so forth
6
. Such
research is critical to ensure that the benefits of decision support systems are not lost.
In summary, there are a multitude of factors that can potentially impact how effectively
the knowledge workers function within the organization and indeed in relation to their
interactions with new technologies. The area of decision support has been shown to
require a particular focus in the past, however further research needs to be done to
evaluate what factors impact the successful usage and results of decision support
systems within the context of new business intelligence technologies such as Big Data.
1.2 . Business Intelligence
Business intelligence (BI) has been defined as both a process and a product. Jourdan et
al (2006) asserts that the process is composed of methods that organizations use to
develop useful information, or intelligence, that can help organizations survive and
thrive in the global economy
12
. The broad category of applications and technologies
for gathering, storing, analyzing, and providing access to data to help knowledge
workers make better business decisions by predicting behavior of their competitors,
suppliers, customers, technologies, acquisitions, markets, and products. Business
analytics (BA) is the practice of iterative, methodical exploration of an organization’s
data with emphasis on statistical analysis. Business analytics are used by companies
committed to data-driven decision making. BA is used to gain insights that inform
business decisions and can be used to automate and optimize business processes. Data-
driven companies treat their data as a corporate asset and leverage it for competitive
advantage. Successful business analytics depends on data quality, skilled knowledge
workers who understand the technologies and the business and an organizational
commitment to data-driven decision making
2
.
Tooling the knowledge worker to fully utilize the BI tools for advanced
capabilities is key for organizations to achieve maximum gain from BI and to enable
knowledge workers to function productively
13
. ERP software companies are
empowering knowledge workers by offering more flexibility in the tools with less
reliance on technical teams, which is seemingly cost-effective to the business.
Knowledge workers are becoming more advanced super-users through increased
exposure and experience in ERP applications. The number of programmers and
dependency on IT is decreasing gradually as systems develop with training of power
users for example instead of report developers which reduces the costs of IT
consultants while also adding diversity to the roles of the business users. De-
centralizing BI support skills and shifting them to the business is cost-effective but then
a need for skills development and training exists.
Rowe and White (2012) suggest that powerful Business Intelligence
(BI) analytics tools are given to managers to have access to decision-making so a need
for BI tools that are interactive and visual emerge
14
. Quality training and education of
these managers will need to take place so managers understand the concepts of
analytics, interpreting the data, and the capabilities of the tools. There is clearly a shift
in these BI tools to tailoring to the business audience. In addition, issues such as data
collection, storage, and processing specific to Big Data analytics are becoming more
important factors and have critical implications for the knowledge worker.
Stoica and Dragos (2010) highlights that there is a relationship between the
needs of the knowledge worker and accessibility and usability of analytic tools to
enable more educated analytical business decisions
15
. Studies analyze how the BI
strategy impacts the BI benefits by analyzing dimensions such as strategic alignment,
governance, people, organizational culture and data and technology infrastructure
13
.
Shanks and Bekmamedova (2012) argue that BA systems contribute to competitive
advantage but only when dynamic capabilities enabled by business analytics
technology lead to improved firm performance
16
. There is a gap however between the
needs and context of the knowledge worker in the new Big Data environment and the
accessibility and usability of the current tools.
Grabski et al (2011) note further research is needed into benefits arising
from use and integration of Business Analytics as BI&A has emerged as an area of
study for researchers and practitioners
17
. The challenges and opportunities associated
with successfully implementing BI& Analytics therefore require additional research in
this area
18
.
1.3. Big Data
Big Data is an important contemporary challenge as organizations are experiencing
exponential growth of data with large pools that can be captured, communicated,
stored, aggregated, and analyzed but there are endless business and economic
possibilities with big data that could have huge value. Making quicker and easier
decisions with better data reporting is the ultimate goal of BI. Data analysis can be
argued to be the core of decision-making in business applications. ERP systems are
not bringing business benefit if executives cannot make accurate decisions based on
data in the systems. With mobile connectivity, social media, geo-sensors, cheap data
storage, we are in an era of information explosion which leads to the next frontier in
innovation, competition, and productivity
19
. Big Data is an important issue today and
will continue to be a growing concern as 90% of the data in the world today has been
created in the last two years alone
20
.
Big Data presents a new set of challenges for both executives and
knowledge workers as the data is available but how do we extract value from this data
through analysis? Currently, executives are unable get a global view of the data as
everyone not on same software and some regions do not have same IT maturity. Data
integrated from multiple sources is usually complex and costs usually under-estimated.
Executives are cautious of making investments into analytics tools as convinced the
organizations are not ready as they don’t understand the data they currently have with
the current tools but they know there is value in the data they currently have
2
.
There is a shortage of talent necessary in organizations to take advantage of
Big Data by knowledge workers with sophisticated statistical and analytical skills to
accommodate the volume, velocity, and variety of data as well as privacy, security, and
data governance issues
19,21
. This study will further analyze the impact the successful
usage of the BI tools in the Big Data environment by the knowledge workers i.e,
empowered through training and developing analytical skills.
2.4. System Usage and IS Success
Information Systems research provides solid theoretical frameworks with which to
represent the important aspects of BI use and its antecedents. “Use” is a much
researched construct and provides a basis for exploring usage perceptions of the
Knowledge Worker in a Big Data context
22
. Use is further a core component of the
D&M IS Success Model, which provides a well-established framework with which to
explore the impact of antecedent factors on success dimensions
23
.
Recently, Petter et al (2013) reviewed the important independent variables
that impact IS Success and provided four specific determinants within which to
theoretically examine success
24
.
Figure 1: Determinants of IS Success
24
The strongest determinants for Use include organizational competence,
extrinsic motivation, and IT infrastructure
24
. The approach and results of this
investigation and including previous studies suggest new directions for research into
the nature of system usage, its antecedents and consequences, which can be usefully
applied to understanding success in the new domain on BI/Big Data
22
. Both studies for
example encourage further research for interactions among antecedents (how
interactions among task, user, and structure contribute to a higher or lower user
success), antecedents of specific IS dimensions (how the relationship between task,
project and organizational variables impact the ‘Intention to Use’, and specific
association among antecedents such as the relationship between technology experience
and individual impact.
Burton-Jones and Gallivan (2007) suggest the multi-level approach appears
to be a promising way to obtain rich insights into the nature and use of information
systems in organizations (and in higher levels of collectives, such as industries or
societies), increase the accuracy of the languages we use to describe system usage in
research and practice, and increase the rigor and relevance of research on its emergence
and change and its antecedents and consequences
25
.
2. Research question and objectives
Much research has focused on the benefits and impacts organizations perceive from the
use of business intelligence tools demonstrating how organizations gain a competitive
advantage through the use of Business analytics. Advanced visualization tools lead to
better decision-making as more advanced visualizations allow managers and executives
to make better decisions. However, Big Data presents unprecedented challenges for
the knowledge worker and a new as yet untested environment within which to work.
Independent success factors have been shown to be critical to gaining overall IS
success however, currently there is a lack of focus on what antecedent factors influence
the successful usage of BI systems by knowledge workers. Therefore, this research
proposes to explore the factors that influence the successful usage of BI systems by
Knowledge Workers in a BI and Business Big Data environment. The following
research questions are proposed as a result:
1. What are the important antecedent Success factors for BI/Big Data
systems, from the perspective of the knowledge worker?
2. How do antecedent success factors impact successful usage of BI/Big
Data systems, from the perspective of the knowledge worker?
3. What elements characterize “successful usage” of BI/Big Data systems,
from the perspective of the knowledge worker?
If the D&M IS Success Model is a reasonably robust description of the
dependent variable of IS research, then what are the independent variables that “cause”
IS success or influences IS Success in a BI/Big Data context? What determinants have
been shown to relate positively to IS success? Are there factors, particularly those that
are under the control of management, that can act as levers to improve the chances of
success of their investments in the area of Big Data (Petter et al., 2013)? The
theoretical framework of this study is based on Figure 1 – Determinants of IS Success
– and intends to identify antecedents of Project and Organizational characteristics -
Management Support, Task Characteristics - Task difficulty, and Users and Social
Characteristics – Enjoyment and measure the impact on successful usage factors in a
BI environment in relation to the knowledge worker in a BI/Big Data environment.
3. Research Design and Proposed Schedule
This research intends to follow a positivist methodology in identifying and measuring
the influence of independent and dependent constructs representing success. The
design intends to include a structured literature review in order to identify relevant
variables and then iteratively develop the list with sample participants prior to a main
survey study. The following stages present a brief outline of stages.
? Stage 1: Develop a comprehensive list of antecedent dimensions
in line with the Petter et al (2013) model and the Usage concept
from the existing literature.
o April – June 2014
? Stage 2: Conduct exploratory studies with knowledge workers to
identify any missing dimensions and confirm an overall set of
factors.
o July – September 2014
? Stage 3: Develop a final research model containing antecedent
dimensions and propose hypotheses.
o October 2014 – May 2015
? Stage 4: Conduct case studies with selected organizations e.g.,
PepsiCo, TekLink, Epsilon, SAP, Northwestern Hospital, and
Accenture via face-to-face, semi-structured, in-depth interviews,
surveys, focus groups in order to study the proposed model and
test hypotheses.
o June – December 2015
? Stage 5: Analysis of data and Discussion.
o Jan – April 2016
? Stage 6: Final Review and Submission.
o May – October 2016
References
[1.] Ross J. Surprising Facts About Implementing ERP. IT Pro 1999.
[2.] Barton D, Court D. Making Advanced Analytics Work For You. Harvard
Business Review 2012.
[3.] Chen H, Chiang R, Storey V. Business Intelligence and Analytics: From Big
Data to Big Impact. Business Intelligence Research 2012;Volume
36
gs.1165-1188
[4.] Staehr L, Shanks G, Seddon PB. An Explanatory Framework for Achieving
Business Benefits from ERP Systems. Journal of the Association for
Information Systems 2012;13(6):424-465
[5.] Zopiatis A, Constanti P, Theocharous A. Job involvement, commitment,
satisfaction and turnover: Evidence from hotel employees in Cyprus. Tourism
Management 2014;41:129-140
[6.] Chatzimouratidis A, Theotokas I, Lagoudis IN. Decision support systems for
human resource training and development. The International Journal of
Human Resource Management 2011;23(4):662-693
[7.] Walumbwa, Christensen, Hailey. Authentic leadership and the knowledge
economy: Sustaining motivation and trust among knowledge workers.
Organizational Dynamics 2011:110-118
[8.] Gardner W, Schermerhorn. Unleashing Individual Potential:: Performance
Gains Through Positive Organizational Behavior and Authentic Leadership.
Organizational Dynamics 2004;33(3 ).
[9.] Mak B, Sockel H. A confirmatory factor analysis of IS employee motivation
and retention. Information & Management 1999;38:265-276
[10.] Coelho F, Augusto M, Lages. Contextual Factors and the Creativity of
Frontline Employees: The Mediating Effects of Role Stress and Intrinsic
Motivation. Journal of Retailing 2011;87:31-45
[11.] Reynolds P, Quaadgras A, Ritter D. Directing Digital Innovation to Maximize
Business Impact at PepsiCo. Volume 14Feb 2014.
[12.] Jourdan Z, Rainer K, Marshall T. Business Intelligence: An Analysis of the
Literature. Information Systems Management 2006gs.121-131.
[13.] Shanks G, Sharma R, Seddon P, Reynolds P. The impact of strategy and
maturity on business analytics and firm performance:A review and research
agenda. 2012; University of Wollongong. p pgs.1-11.
[14.] Rowe N, White D. A Sum Greater than its Parts: Adding SAP Business
Objects to SAP Business Warehouse. Aberdeen Group 2012.
[15.] Stoica PN, Dragos. A New Business Dimension -Business Analytics.
Accounting and Management Information Systems 2010;Volume 9gs. 603-
618
[16.] Shanks G, Bekmamedova N. The Impact of Strategy on Business Analytics
Success. 2012; Geelong.
[17.] Grabski SV, Leech SA, Schmidt PJ. A Review of ERP Research: A Future
Agenda for Accounting Information Systems. Journal of Information Systems
2011;25(1):37-78
[18.] Chen H, Chiang R, Storey V. Business Intelligence and Analytics: From Big
Data to Big Impact. Business Intelligence Research 2012;Volume
36gs.1165-1188.
[19.] Manyika J, Chui M, Brown B. Big Data: The next frontier for
innovation,competition,and productivity. McKinsey Global Institute 2011.
[20.] Esoinosa JA, Armour F, Kaisler S. Big Data and Business Analytics: Defining
a Framework. CITGE Research Center 2012gs.1-18.
[21.] Wixom B, Ross J, Beath C. Capturing Value frm Big Data at comScore
through Platform, People, and Perception. MIT - Center for Information
Systems Research; 2013.
[22.] Burton-Jones A, Straub D. Reconceptualizing System Usage: An Approach
and Empirical Test. Information Systems Research 2006;17(3):228-246.
[23.] DeLone WH, McLean ER. The DeLone and McLean Model of Information
Systems Success: A Ten-Year Update. Journal of Management Information
Systems 2003;19(4):9-30.
[24.] Petter S, DeLone W, McLean ER. Information Systems Success: The Quest
for the Independent Variables. Journal of Management Information Systems
2013;29(4):7-62
[25.] Burton-Jones A, Gallivan MJ. Toward a Deeper Understanding of System
Usage in Organizations: A Multi-Level Perspective. MIS Quarterly
2007;31(4):657-679.
doc_336854681.pdf
Much research has focused on the benefits and impacts organizations perceive from the use of business intelligence tools demonstrating how organizations gain a competitive advantage through the use of Business analytics.
Exploring Factors that influence the
Knowledge Worker in a Business
environment
Coleen Rose GRIFFIN
a1
, Murray SCOTT and William GOLDEN
a
J.E. Cairnes School of Business and Economics, National University of
Ireland, Galway, Ireland
Abstract. Much research has focused on the benefits and impacts
organizations perceive from the use of business intelligence tools
demonstrating how organizations gain a competitive advantage through the use
of Business analytics. Advanced visualization tools lead to better decision-
making as more advanced visualizations allow managers and executives to
make better decisions. However, Big Data presents unprecedented challenges
for the knowledge worker and a new as yet untested environment within which
to work. Independent success factors have been shown to be critical to gaining
overall IS success however, currently there is a lack of focus on what
antecedent factors influence the successful usage of BI systems by knowledge
workers. Therefore, this research proposes to explore the factors that influence
the successful usage of BI systems by Knowledge Workers in a BI and
Business Big Data environment.
Keywords. Knowledge Worker, Big Data, Business Intelligence, System
Usage, IS Success
Introduction
In today’s cost-conscious environment, Ross (1999) asserts that organizations expect
IT to reduce operating costs, show improvements in business processes, standardization
1 Corresponding author
of processes to ensure quality and reducing cycle times, to show data visibility to help
knowledge workers make better business decisions with latest real-time data
1
. The
key to thriving in a competitive marketplace is staying ahead of the competition.
Making sound business decisions based on accurate and current information takes more
than intuition. Data analysis, reporting, and query tools can help business users wade
through a sea of data to synthesize valuable information - today these tools collectively
fall into a category called "Business Intelligence." Research highlights the impact and
relationship of BI capabilities and BI success focusing on how organizations can use BI
systems to achieve measurable financial benefit
2,3
.
BI is used to gain insights that inform business decisions and can be used to
automate and optimize business processes. New BI technologies and Big Data present
great opportunities for organizations to leverage but also create a new set of usage
challenges for the knowledge worker. Big Data i.e., the volume of information, the
variety of data and the velocity of data, creates an emerging problem for the knowledge
worker given the growing need for diverse analytical skills, needed to gain value from
the vast amount of data available. As companies increasingly treat their data as a
corporate asset and leverage it for competitive advantage, the need to create value from
Big Data and new BI tools has intensified pressure on the knowledge worker to
successfully adopt new systems and provide the promised new insights. As a result
understanding successful usage in this new context is key to achieving the desired
outcomes and benefits of Big Data.
In order to achieve the business benefits from IT tools, Staehr et al (2012)
argue that the following conditions need to be achieved for post-implementation
success
4
:
? Influence of early phases in the IT cycle on subsequent phases
? Ongoing resourcing for post-implementation phase to develop in-house
knowledge
? Establishing metrics
? Effective Change Management processes in-place.
? Education and quality training for in-house resources.
? Software to fit the needs of the business.
? People – Availability of experienced and skilled-staff to meet business
benefits.
These factors demonstrate the important role antecedent factors play in the
successful outcome of a system implementation and moreover highlight the critical role
of the knowledge worker/user of the system; for instance, four of the seven conditions
above relate to the knowledge worker. There has been much research focusing on the
business benefits achieved through the implementation of BI tools but there is a gap in
the research relating to the antecedent factors that determine whether such technologies
will be successfully used and deliver the desired benefits. Indeed much prior research
in the area of systems usage has demonstrated that simply providing a technology is not
sufficient for a successful outcome, the needs and context of the users must be
considered.
The primary research question addressed by this study is therefore: What
factors influence the successful usage of BI systems by Knowledge Workers in a
BI/Big Data environment? This research will evaluate the success factors of the
Knowledge worker and identify what are the important antecedent factors influencing
usage success factors from the perspective of the knowledge worker in a relation to
Business Intelligence tools. At the time of writing, this PhD research is in the
literature review phase. This paper is structured in the following manner. Section 2 is
a literature review. Section 3 discusses the research question and objectives. Section 4
discusses the research design and schedule of the research.
1. Literature Review
This literature review develops a framework to guide the research in certain specific
contexts. It starts with a background and terminology associated with the knowledge
worker and success factors related to the knowledge worker. The following section
defines Business Intelligence (BI) and Big Data evaluating the literature then correlates
this to the knowledge worker in the changing IT environment. The study then
evaluates the area of system usage and what antecedent factors may make system usage
successful for the knowledge worker in a Big Data/Business Intelligence environment.
1.1. Knowledge Worker
A knowledge worker is anyone who works for a living at the tasks of developing or
using knowledge. This might be someone who works at any of the tasks of planning,
acquiring, searching, analyzing, organizing, storing, programming, distributing,
marketing, or otherwise contributing to the transformation and commerce of
information and those who work at using the knowledge so produced. Research has
demonstrated that there are a multitude of factors that can influence the knowledge
worker in terms of productivity, job satisfaction, involvement, organizational
commitment, and turnover intentions
5
. Various strategies have been developed in
recognition of the important impact that these factors have on the outcome of IS
implementation initiatives. There were several themes around motivation and
employee empowerment around the theme of the knowledge worker in reference to
leadership and job assignments
6,7
. Gardner and Schermerhorn (2004) discuss the need
and impact of authentic leaders who arouse willingness to work hard to perform at
one’s best
8
. Beneficial employee treatment i.e, fairness, supervisor support, and
favorable job conditions can be linked to positive organizational support from
employees and knowledge workers. Walumba et al (2011) discuss how knowledge
workers can work in remote parts of the world via virtual teams; these workers require
little supervision, enjoy being rewarded for ideas that they generate but require an
authentic leader who values aligned with company mission
7
. Authentic leaders need
to develop deep sense of trust in group members requiring input from team members
7
.
Mak and Sockel (1999) assert job satisfaction is a key factor for IT
employee motivation, which otherwise threatens productivity of IT operations
9
. IT
professionals are motivated by job assignments, appropriate job training workshops
and seminars for learning new technologies. Coelho et al. (2011) believe when
managers promote creativity and innovation in the work environment this creates
increased employee satisfaction
10
. Organizations such as PepsiCo use digital
innovation to deliver more effective merchandising making simpler and cutting
business processes making easier for knowledge worker
11
.
Even if there is strong commitment and motivation from the knowledge
worker, Chatzimouratidis et al. (2011) assert output or results from decision support
systems are meaningless unless methods to analyze the data have been invested via
web-based learning, job rotations, mentorship, case studies, and so forth
6
. Such
research is critical to ensure that the benefits of decision support systems are not lost.
In summary, there are a multitude of factors that can potentially impact how effectively
the knowledge workers function within the organization and indeed in relation to their
interactions with new technologies. The area of decision support has been shown to
require a particular focus in the past, however further research needs to be done to
evaluate what factors impact the successful usage and results of decision support
systems within the context of new business intelligence technologies such as Big Data.
1.2 . Business Intelligence
Business intelligence (BI) has been defined as both a process and a product. Jourdan et
al (2006) asserts that the process is composed of methods that organizations use to
develop useful information, or intelligence, that can help organizations survive and
thrive in the global economy
12
. The broad category of applications and technologies
for gathering, storing, analyzing, and providing access to data to help knowledge
workers make better business decisions by predicting behavior of their competitors,
suppliers, customers, technologies, acquisitions, markets, and products. Business
analytics (BA) is the practice of iterative, methodical exploration of an organization’s
data with emphasis on statistical analysis. Business analytics are used by companies
committed to data-driven decision making. BA is used to gain insights that inform
business decisions and can be used to automate and optimize business processes. Data-
driven companies treat their data as a corporate asset and leverage it for competitive
advantage. Successful business analytics depends on data quality, skilled knowledge
workers who understand the technologies and the business and an organizational
commitment to data-driven decision making
2
.
Tooling the knowledge worker to fully utilize the BI tools for advanced
capabilities is key for organizations to achieve maximum gain from BI and to enable
knowledge workers to function productively
13
. ERP software companies are
empowering knowledge workers by offering more flexibility in the tools with less
reliance on technical teams, which is seemingly cost-effective to the business.
Knowledge workers are becoming more advanced super-users through increased
exposure and experience in ERP applications. The number of programmers and
dependency on IT is decreasing gradually as systems develop with training of power
users for example instead of report developers which reduces the costs of IT
consultants while also adding diversity to the roles of the business users. De-
centralizing BI support skills and shifting them to the business is cost-effective but then
a need for skills development and training exists.
Rowe and White (2012) suggest that powerful Business Intelligence
(BI) analytics tools are given to managers to have access to decision-making so a need
for BI tools that are interactive and visual emerge
14
. Quality training and education of
these managers will need to take place so managers understand the concepts of
analytics, interpreting the data, and the capabilities of the tools. There is clearly a shift
in these BI tools to tailoring to the business audience. In addition, issues such as data
collection, storage, and processing specific to Big Data analytics are becoming more
important factors and have critical implications for the knowledge worker.
Stoica and Dragos (2010) highlights that there is a relationship between the
needs of the knowledge worker and accessibility and usability of analytic tools to
enable more educated analytical business decisions
15
. Studies analyze how the BI
strategy impacts the BI benefits by analyzing dimensions such as strategic alignment,
governance, people, organizational culture and data and technology infrastructure
13
.
Shanks and Bekmamedova (2012) argue that BA systems contribute to competitive
advantage but only when dynamic capabilities enabled by business analytics
technology lead to improved firm performance
16
. There is a gap however between the
needs and context of the knowledge worker in the new Big Data environment and the
accessibility and usability of the current tools.
Grabski et al (2011) note further research is needed into benefits arising
from use and integration of Business Analytics as BI&A has emerged as an area of
study for researchers and practitioners
17
. The challenges and opportunities associated
with successfully implementing BI& Analytics therefore require additional research in
this area
18
.
1.3. Big Data
Big Data is an important contemporary challenge as organizations are experiencing
exponential growth of data with large pools that can be captured, communicated,
stored, aggregated, and analyzed but there are endless business and economic
possibilities with big data that could have huge value. Making quicker and easier
decisions with better data reporting is the ultimate goal of BI. Data analysis can be
argued to be the core of decision-making in business applications. ERP systems are
not bringing business benefit if executives cannot make accurate decisions based on
data in the systems. With mobile connectivity, social media, geo-sensors, cheap data
storage, we are in an era of information explosion which leads to the next frontier in
innovation, competition, and productivity
19
. Big Data is an important issue today and
will continue to be a growing concern as 90% of the data in the world today has been
created in the last two years alone
20
.
Big Data presents a new set of challenges for both executives and
knowledge workers as the data is available but how do we extract value from this data
through analysis? Currently, executives are unable get a global view of the data as
everyone not on same software and some regions do not have same IT maturity. Data
integrated from multiple sources is usually complex and costs usually under-estimated.
Executives are cautious of making investments into analytics tools as convinced the
organizations are not ready as they don’t understand the data they currently have with
the current tools but they know there is value in the data they currently have
2
.
There is a shortage of talent necessary in organizations to take advantage of
Big Data by knowledge workers with sophisticated statistical and analytical skills to
accommodate the volume, velocity, and variety of data as well as privacy, security, and
data governance issues
19,21
. This study will further analyze the impact the successful
usage of the BI tools in the Big Data environment by the knowledge workers i.e,
empowered through training and developing analytical skills.
2.4. System Usage and IS Success
Information Systems research provides solid theoretical frameworks with which to
represent the important aspects of BI use and its antecedents. “Use” is a much
researched construct and provides a basis for exploring usage perceptions of the
Knowledge Worker in a Big Data context
22
. Use is further a core component of the
D&M IS Success Model, which provides a well-established framework with which to
explore the impact of antecedent factors on success dimensions
23
.
Recently, Petter et al (2013) reviewed the important independent variables
that impact IS Success and provided four specific determinants within which to
theoretically examine success
24
.
Figure 1: Determinants of IS Success
24
The strongest determinants for Use include organizational competence,
extrinsic motivation, and IT infrastructure
24
. The approach and results of this
investigation and including previous studies suggest new directions for research into
the nature of system usage, its antecedents and consequences, which can be usefully
applied to understanding success in the new domain on BI/Big Data
22
. Both studies for
example encourage further research for interactions among antecedents (how
interactions among task, user, and structure contribute to a higher or lower user
success), antecedents of specific IS dimensions (how the relationship between task,
project and organizational variables impact the ‘Intention to Use’, and specific
association among antecedents such as the relationship between technology experience
and individual impact.
Burton-Jones and Gallivan (2007) suggest the multi-level approach appears
to be a promising way to obtain rich insights into the nature and use of information
systems in organizations (and in higher levels of collectives, such as industries or
societies), increase the accuracy of the languages we use to describe system usage in
research and practice, and increase the rigor and relevance of research on its emergence
and change and its antecedents and consequences
25
.
2. Research question and objectives
Much research has focused on the benefits and impacts organizations perceive from the
use of business intelligence tools demonstrating how organizations gain a competitive
advantage through the use of Business analytics. Advanced visualization tools lead to
better decision-making as more advanced visualizations allow managers and executives
to make better decisions. However, Big Data presents unprecedented challenges for
the knowledge worker and a new as yet untested environment within which to work.
Independent success factors have been shown to be critical to gaining overall IS
success however, currently there is a lack of focus on what antecedent factors influence
the successful usage of BI systems by knowledge workers. Therefore, this research
proposes to explore the factors that influence the successful usage of BI systems by
Knowledge Workers in a BI and Business Big Data environment. The following
research questions are proposed as a result:
1. What are the important antecedent Success factors for BI/Big Data
systems, from the perspective of the knowledge worker?
2. How do antecedent success factors impact successful usage of BI/Big
Data systems, from the perspective of the knowledge worker?
3. What elements characterize “successful usage” of BI/Big Data systems,
from the perspective of the knowledge worker?
If the D&M IS Success Model is a reasonably robust description of the
dependent variable of IS research, then what are the independent variables that “cause”
IS success or influences IS Success in a BI/Big Data context? What determinants have
been shown to relate positively to IS success? Are there factors, particularly those that
are under the control of management, that can act as levers to improve the chances of
success of their investments in the area of Big Data (Petter et al., 2013)? The
theoretical framework of this study is based on Figure 1 – Determinants of IS Success
– and intends to identify antecedents of Project and Organizational characteristics -
Management Support, Task Characteristics - Task difficulty, and Users and Social
Characteristics – Enjoyment and measure the impact on successful usage factors in a
BI environment in relation to the knowledge worker in a BI/Big Data environment.
3. Research Design and Proposed Schedule
This research intends to follow a positivist methodology in identifying and measuring
the influence of independent and dependent constructs representing success. The
design intends to include a structured literature review in order to identify relevant
variables and then iteratively develop the list with sample participants prior to a main
survey study. The following stages present a brief outline of stages.
? Stage 1: Develop a comprehensive list of antecedent dimensions
in line with the Petter et al (2013) model and the Usage concept
from the existing literature.
o April – June 2014
? Stage 2: Conduct exploratory studies with knowledge workers to
identify any missing dimensions and confirm an overall set of
factors.
o July – September 2014
? Stage 3: Develop a final research model containing antecedent
dimensions and propose hypotheses.
o October 2014 – May 2015
? Stage 4: Conduct case studies with selected organizations e.g.,
PepsiCo, TekLink, Epsilon, SAP, Northwestern Hospital, and
Accenture via face-to-face, semi-structured, in-depth interviews,
surveys, focus groups in order to study the proposed model and
test hypotheses.
o June – December 2015
? Stage 5: Analysis of data and Discussion.
o Jan – April 2016
? Stage 6: Final Review and Submission.
o May – October 2016
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