Multidimensional data analysis with Excel pivot table
(EPT-based MDA) used as a research method technique- a research note
JOSEPH
KIM-KEUNG HO
Independent Trainer
Hong Kong, China
Abstract:
Though the topic of multidimensional data analysis and the Excel pivot
table function have been much examined in the Computer Science (CS) and the
Management Information Systems (MIS) fields, it has been neglected as a
research topic in the Research Methods (RM) field. This article examined the
value of multidimensional data analysis (MDA) with Excel pivot table (EPT) and
found it valuable and user-friendly as a research method technique. It offers
some illustration on how it is used to study a data set from a 2015
Facebook-based questionnaire survey conducted by the writer on perceptions of
literature review practices and concerns in Hong Kong. It is recommended that
the EPT-based MDA be considered as a useful RM technique with the researcher
adopting a number of information user behaviours simultaneously.
Key words: Computer
Science (CS), Excel pivot table (EPT), Excel pivot table-based multidimensional
data analysis (EPT-based MDA), executive information systems (EIS), information
user, literature review, Management Information Systems (MIS), multidimensional
data analysis (MDA), online analytical processing (OLAP), Research Methods (RM)
Introduction
The Excel function
of pivot table (EPT) is widely known and used by people doing quantitative data
analysis, e.g., for sales statistics study. It offers a usable way to conduct a
multidimensional data analysis (MDA) on a set of records with multiple fields. In
the academic literature, multidimensional data analysis (MDA) is very often
treated as a topic of study in the Computer Science field (e.g., Westerlund,
2008; Che et al., 2011), notably on
the topic of online analytical processing (OLAP) (Pilotsoftware.com, 2002;
Forsman, 1997). In the associated Management Information Systems (MIS) field
(Haag and Cummings, 2013; Laudon and Laudon, 2012), the function to study a
company's statistics, especially as related to key performance indicators, in a
multidimensional way has long been considered as a useful feature of an
executive information system (EIS) (Partanen and Savolanien, 1995; Nord and Nord,
1995). Specifically, Lungu and Bâra (2007) maintain that an EIS offers "a friendly
graphical interface with advanced capabilities of slicing and dicing through
data and easily get a new perspective over data by rotating dimensions and
drill down or roll up over hierarchical levels". In short, the value of MDA,
which the EPT function offers, for studying a structured data set with multiple
fields, e.g., as hierarchies of dimensions, is well recognized in the CS and
MIS fields. In the Research Methods (RM) field, however, the value of EPT-based
MDA as a quantitative research method technique has been neglected. This is reflected in the very few academic
articles found on the EBT-based MDA in academic journal publisher websites. Another
indication for its being neglected as a research method topic is that the terms
of "Excel pivot table" and "multidimensional data analysis"
do not appear in the subject index of Saunders et al. (2012) and Bryman and Bell (2011), both popular Research
Methods textbooks for business students. In an attempt to raise this research interest
on EBT-based MDA, this article takes up the task of discussing the value of EBT-based
MDA as a research method analysis technique.
Multidimensional data
analysis with Excel pivot table as a research method technique
It has been well
acknowledged that MDA is capable to produce valuable business information. As
Che et al. (2011) puts it: "Multidimensional
data analysis can observe and process data from several angles, obtaining
useful information for management decision-making departments and providing
effective support by turning business data into management data". For our discussion, a starting point
to apply multidimensional data analysis with Excel pivot table (EPT-based MDA) is
to consider a structured file with a number of fields (i.e., dimensions). In
the context of research methods practice, typically, such a structured file is
one on questionnaire survey, which is very often done online; subsequently,
data captured are exported to an Excel file. After some data cleansing, the
survey data file can be analysed with the Excel pivot table function. This is
illustrated in Exhibit 1 as follows:
Exhibit 1: a structured data file and the
Excel pivot table function
The location of the
Excel pivot table function is in Insert,
then PivotTable. Field names can
be chosen as row or column dimensions for the pivot table while the value
field(s) are the ones to be averaged or summed up, etc., in the pivot table. In
the subject of multidimensional data analysis (MDA), the data file is called a
data cube; it represents data along some measure of interest and can be
2-dimensional or more (www2.cs.iregina.ca, n.d). Each pivot table that draws on
the data cube represents a view of it. Summarization and drill-down of values
by column and row with hierarchies of dimensions can be shown in a pivot table.
In the jargons of multidimensional data analysis, summarization (or rollup) of
the data cube can be done "by traversing upwards through a concept
hierarchy. A concept hierarchy maps a set of low level concepts to higher
level, more general concepts" (www2.cs.iregina.ca, n.d).
When studying a
data cube, the researcher could examine the following figures in a set of
constructed pivot tables (i.e., views), with different combinations of fields
(i.e., dimensions and value fields):
a. Row and column
subtotals in a pivot table
b. Specific cell (i.e.,
an intersection cell between a row and a column) values in a pivot table [cell
values can be count, average, sum, maximum, minimum and product figures]
c. Cross comparison
of cell values in a pivot table or between pivot tables to discern data
patterns, associations and clusters
d. Extreme, average
and blank values in various cells to form a more critical and holistic
evaluation of cell values
To facilitating spotting,
exploring and evaluating patterns of value distribution in a set of constructed
pivot tables, the researcher, an information user[1] (Inmon,
Imhoff and Sousa, 2001), is recommended to behave concurrently like:
a. a tourist, who, albeit
unpredictable, "knows where in the structure of things to find almost
anything" (Inmon, Imhoff and Sousa, 2001);
b. a farmer, who "operates as
comfortably on detailed data as on summary data" (Inmon, Imhoff and Sousa,
2001);
c. an explorer who "operates with
a great degree of unpredictability", "looks over massive amounts of
details" and "creates assertions and hypotheses"(Inmon, Imhoff
and Sousa, 2001); and
d. a miner, who attempts to prove
assertions and hypotheses' validity (Inmon, Imhoff and Sousa, 2001) .
An information user
(in our case the researcher), is recommended to adopt an array of simultaneous
information usage behaviours with the Excel pivot tables; the rationale of it
is to make full use of the pivot table function and information from it to
support diverse research tasks in relation to
data exploration/ exploitation as well as deductive and inductive
reasoning based on the set of constructed pivot tables. Consequently, he/she should
be in a position to (i) form an overall impression of the questionnaire survey
findings in terms of data clustering and association, (ii) formulate some crude
theories on their research themes based on the observations of the pivot tables
and (iii) identify additional information needs and gaps for further research,
e.g., arising from some noticed strange and unexpected patterns in the pivot
tables. Consequently, some kind of knowledge discovery can be made from this
EPT-based MDA. More specifically, with the pivot table, inductive reasoning can
be employed to come up with some hypotheses or theories by studying the pivot
table figures. At the same time, deductive reasoning with specific theories can
also be attempted to explain the pivot table figures. All in all, the EPT-based
MDA is capable of complementing other quantitative research methods, e.g.,
multiple regression analysis, chi-squared test and various hypothesis testing
as well as other qualitative research methods, e.g., interview and
observations, (possibly as a follow-up investigation based on the pivot table
analysis). In addition, the pivot tables also serve as a means to convey data
analysis findings in the findings and analysis chapter of a dissertation
report. The next section is an example of EPT-based MDA on a data set of a
Facebook-based questionnaire survey conducted by the writer in 2015 on the
perceptions of literature review practices and concerns in Hong Kong.
An illustration of
using multidimensional data analysis with Excel pivot table to study survey
questionnaire data
A brief EPT-based MDA is conducted on a Facebook-based survey data set covering
perceptions on literature review practices and concerns in Hong Kong. This
questionnaire survey serves both descriptive and analytical survey purposes by
documenting current literature review perceptions and practices as well as explaining
why these perceptions and practices exist. The questionnaire survey was
conducted in January 2015 and reported in Ho (2015). For the brief account of
it here with regard to multidimensional data analysis, the writer treats
initially some of the survey questions (re: appendix
1) as constituting row and column dimensions while others as making up the calculated
value fields for counting, summation and averaging. This way, the row and
column dimensions are conceived for the data analysis exercise as independent
variables while the calculated value fields are considered as dependent
variables to spot and evaluate data patterns and associations. They are listed
as follows:
|
Survey
questions (Ho, 2015)
|
Dimensions
(row or column) or calculated value fields
|
|
Question 1: What is your
gender?
· Male
· Female
|
Dimension
|
|
· Question 2: What is your age?
· 18 to 27
· 28 to 37
· 38 to 47
· 48 to 57
· 58 to 67
· 68 or above
|
Dimension
|
|
Question 3: What is your
education background?
· Not yet a degree-holder
· Finished University Undergraduate Degree study
· Finished Master Degree study
· Finished Ph.D. Degree study (or equivalent)
|
Dimension
|
|
Question 4: What is your field
of education?
Business related
Non-business related
Both business and non-business
related
Unclassified
|
Dimension
|
|
Question 5: Did you (or are
you) learn the subject of “Literature Review” in Research Methods in your
formal education?
· Yes
· No
· Cannot remember
|
Dimension
|
|
Question 6: Do you (or did you)
feel that you have difficulty to understand the subject of Literature Review
during your study of Research Methods (or other courses) for your formal
education?
· Yes, I strongly feel so
· I have this feeling mildly
· I feel it is not difficult to understand
· No feeling at all/ Not applicable
|
Calculated value field
|
|
Question 7: Do you (or did you)
feel that academic journal articles are difficult to understand during your
study for your formal education?
· Yes, I strongly feel so
· I have this feeling mildly
· I feel it is not difficult to understand, in
general
· No feeling at all
|
Calculated value field
|
|
Question 8: Do you (or did you)
use the University e-library to access academic journal articles to do your
course assignments and dissertation projects?
· Yes, I do
· No, I don’t
· Cannot remember
|
Dimension
|
|
Question 9: Do you (or did you)
feel that academic articles are useful for literature review?
· Yes, very useful
· It is basically useful
· Not useful
· No idea
|
Calculated value field
|
|
Question 10: Do you (or did
you) feel that reading academic journal articles is able to improve your
professional competence?
· Yes, I strongly feel so
· I have this feeling mildly
· I don’t think so
· No idea
|
Calculated value field
|
|
Question 11: Do you have access
to academic journal libraries (not Google scholar) when you are not studying
for a formal education program?
· Yes, and convenient
· Yes, but not convenient
· Not able to access at all
· No idea
|
Dimension
|
|
Question 12: Are you interested
in improving your literature review skill in the near future?
· Yes, I am strongly interested
· I am mildly interested
· No, not interested
· No idea
|
Calculated value field
|
|
Question 13: Do you feel that
you are able to improve your literature review skill without reading academic
journal articles?
Yes, I strongly fee so
I have this feeling mildly
No, I do not feel this way
No idea
|
Calculated value field
|
|
Question 14: Do you enjoy
reading academic journal articles?
·
Yes, I
enjoy it very much
·
I do,
basically
·
No, I
don’t
·
No feeling
|
Calculated value field
|
Two brief pivot table analyses on the survey data, i.e., analysis 1 and analysis 2, are now reported as follows for illustration purpose.
Pivot
table analysis 1 on "perceived difficulty to study academic journal
articles"
This analysis adopts the following coding
scheme for the data analysis on survey question 7: Do you (or did you) feel that academic journal articles are
difficult to understand during your study for your formal education?
|
Yes, I strongly feel so:
|
3
|
|
I have this feeling mildly:
|
2
|
|
I feel it is not difficult to
understand, in general:
|
1
|
|
No feeling at all:
|
filtered;
not used in the analysis
|
Table 1: perceived difficulty to study academic
journal articles with the row dimension on education background and the column
dimension on field of education.
|
Average of Perceived difficulty to
study academic journal articles
|
Column Labels
|
|
|
|
|
|
|
Row Labels
|
Both business and non-business related
|
Business-related
|
Non-business related
|
Unclassified
|
(blank)
|
Grand Total
|
|
Finished Master Degree study
|
1.9
|
1.6
|
1.5
|
2.0
|
|
1.7
|
|
Finished Ph.D. Degree study (or
equivalent)
|
|
3.0
|
|
|
|
3.0
|
|
Finished university Undergraduate
Degree study
|
2.0
|
1.8
|
2.0
|
2.0
|
|
1.9
|
|
Not yet a degree-holder
|
2.5
|
2.1
|
2.0
|
|
|
2.1
|
|
(blank)
|
|
|
|
|
|
|
|
Grand Total
|
2.0
|
1.9
|
1.8
|
2.0
|
|
1.9
|
Interpretations: On perceived difficulty to study
academic journal articles, those with both business and non-business-related
fields experienced more difficulty to study academic journal articles than
others. Those with Ph.D. degrees also have more difficulty to study academic
journal articles. It appears important
to also take into consideration the numbers of respondents in each cell on
interpreting this set of pivot table values.
Table 2: perceived difficulty to study academic
journal articles with the row dimension on education background and the column
dimension on age range.
|
Average of Perceived difficulty to
study academic journal articles
|
Column Labels
|
|
|
|
|
|
|
Row Labels
|
18 to 27
|
28 to 37
|
38 to 47
|
48 to 57
|
(blank)
|
Grand Total
|
|
Finished Master Degree study
|
|
2.2
|
1.6
|
1.0
|
|
1.7
|
|
Finished Ph.D. Degree study (or
equivalent)
|
|
|
|
3.0
|
|
3.0
|
|
Finished university Undergraduate
Degree study
|
2.0
|
1.9
|
1.8
|
1.8
|
|
1.9
|
|
Not yet a degree-holder
|
1.8
|
2.4
|
1.8
|
3.0
|
|
2.1
|
|
(blank)
|
|
|
|
|
|
|
|
Grand Total
|
1.8
|
2.1
|
1.7
|
2.1
|
|
1.9
|
Interpretations: On perceived difficulty to study
academic journal articles, those in the age ranges of 26-37 and 48-57 have more
difficulty to study academic journal articles than others. Those in the age
range of 48 to 57 with Ph.D. degrees and no degree yet also have more difficulty
to study academic journal articles.
Table 3: perceived difficulty to study academic
journal articles with the row dimension on education background and the column
dimension on gender.
|
Average of Perceived difficulty to
study academic journal articles
|
Column Labels
|
|
|
|
|
Row Labels
|
Female
|
Male
|
(blank)
|
Grand Total
|
|
Both business and non-business
related
|
2.0
|
2.0
|
|
2.0
|
|
Business-related
|
1.9
|
2.1
|
|
1.9
|
|
Non-business related
|
1.3
|
1.9
|
|
1.8
|
|
Unclassified
|
2.0
|
|
|
2.0
|
|
(blank)
|
|
|
|
|
|
Grand Total
|
1.9
|
2.0
|
|
1.9
|
Interpretations: On perceived difficulty to study
academic journal articles, male respondents have more difficulty to study
academic journal articles than female respondents. This is especially the case
for respondents with non-business related education background.
Pivot table analysis 2
on "perceived relevance of academic article study to professional
competence improvement"
This analysis adopts the following coding scheme for the data analysis
on survey question 10: Do you (or did you) feel
that reading academic journal articles is able to improve your professional
competence?
|
Yes, I strongly feel so:
|
3
|
|
I have this feeling mildly:
|
2
|
|
I don't feel so
|
1
|
|
No idea
|
filtered;
not used in the analysis
|
Table 4: perceived relevance of academic article study
to professional competence improvement with the row dimension on education
background and the column dimension on field of education.
|
Average of Relevance of academic
article study to professional competence improvement
|
Column Labels
|
|
|
|
|
|
|
Row Labels
|
Both business and non-business
related
|
Business-related
|
Non-business related
|
Unclassified
|
(blank)
|
Grand Total
|
|
Finished Master Degree study
|
2.2
|
3.0
|
2.2
|
2.0
|
|
2.4
|
|
Finished Ph.D. Degree study (or equivalent)
|
|
2.0
|
|
|
|
2.0
|
|
Finished university Undergraduate
Degree study
|
2.5
|
2.3
|
2.2
|
|
|
2.3
|
|
Not yet a degree-holder
|
2.0
|
2.1
|
3.0
|
|
|
2.1
|
|
(blank)
|
|
|
|
|
|
|
|
Grand Total
|
2.3
|
2.3
|
2.3
|
2.0
|
|
2.3
|
Interpretations: On perceived relevance of
academic article study to professional competence improvement, respondents with
a master degree or no degree yet perceive higher relevance of academic journal
article study to professional competence development.
Table 5: perceived relevance of academic article study
to professional competence improvement with the row dimension on education
background and the column dimension on age range.
|
Average of Relevance of academic
article study to professional competence improvement
|
Column Labels
|
|
|
|
|
|
|
Row Labels
|
18 to 27
|
28 to 37
|
38 to 47
|
48 to 57
|
(blank)
|
Grand Total
|
|
Finished Master Degree study
|
|
2.0
|
2.5
|
3.0
|
|
2.4
|
|
Finished Ph.D. Degree study (or
equivalent)
|
|
|
|
2.0
|
|
2.0
|
|
Finished university Undergraduate
Degree study
|
2.0
|
2.3
|
2.3
|
2.5
|
|
2.3
|
|
Not yet a degree-holder
|
2.2
|
2.4
|
1.7
|
2.0
|
|
2.1
|
|
(blank)
|
|
|
|
|
|
|
|
Grand Total
|
2.2
|
2.3
|
2.2
|
2.4
|
|
2.3
|
Interpretations: On perceived relevance of
academic article study to professional competence improvement, respondents in
the age range of 48-57 perceive higher relevance of academic journal article
study to professional competence development. Non-degree holders in the age
range of 38-47 perceive the lowest relevance of academic journal article study
to professional competence development.
Table 6: perceived relevance of academic article study
to professional competence improvement with the row dimension on gender and the
column dimension on age range.
|
Average of Relevance of academic
article study to professional competence improvement
|
Column Labels
|
|
|
|
|
|
|
Row Labels
|
18 to 27
|
28 to 37
|
38 to 47
|
48 to 57
|
(blank)
|
Grand Total
|
|
Female
|
2.3
|
2.2
|
2.3
|
3.0
|
|
2.3
|
|
Male
|
2.0
|
2.4
|
2.1
|
2.3
|
|
2.2
|
|
(blank)
|
|
|
|
|
|
|
|
Grand Total
|
2.2
|
2.3
|
2.2
|
2.4
|
|
2.3
|
Interpretations: On perceived relevance of
academic article study to professional competence improvement, female
respondents perceive slightly higher relevance of academic journal article
study to professional competence development than male respondents. On the
other hand, male respondents in the age range of 28-37 has the highest
perceived relevance of academic journal article study to professional
competence development than others.
Overall, after the necessary data cleansing done, both the generation of
the prime Excel pivot tables and the exploration of the table by trying
different row/ column dimensions and
calculated value fields are simple and user-friendly. More importantly, the
pivot table analysis is able to reveal relatively detailed patterns of result
values that provide information for inductive and deductive reasoning. If the
file size for multiple data analysis is large, it also becomes more feasible to
consider more than one dimension in a pivot table row or column in the
EPT-based MDA. The pivot table analysis stimulates a researcher to generate
additional questions that require further research investigation using other
research methods. Lastly, the literature on EIS and OLAP sharpens the
researcher's objective and clarifies his/her information roles in conducting
the pivot table analysis. As such, the EPT-based MDA should not be considered merely
a tool to produce descriptive statistics in a quantitative research method
employed in a dissertation research project.
Concluding remarks
The Excel pivot table function is not a sophisticated tool for
multidimensional data analysis. Nevertheless, it's application value for an
EPT-based MDA should be recognized more. The discussion and the brief EPT-based
MDA illustration serve to establish (a) the EPT-based MDA as a very useful
research method technique and (b) the EPT function for MDA as a useful decision
support system for researchers. Although multidimensional data analysis and the
Excel pivot table function have been much examined in the computer science and
MIS literature, they deserve more attention from the Research Methods field to
study. For example, the decisional support ideas in multidimensional data
analysis and EIS from the CS and MIS fields should be transferred to the EPT-based
MDA as a research method technique in the Research Methods field; in this
regard, the researcher is recommended to take up multiple information user
roles to benefit more from the analysis exercise. Doing so enables the
EPT-based MDA to make much more contribution to enhance research methods
practices in the Research Methods field.
References
Bryman, A. and E. Bell. 2011. Business
Research Methods, 3rd edition, Oxford University Press.
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Sciences 10, Elsevier: 1210-1215.
Forsman, S. 1997. "OLAP Council
White Paper" OLAP Council (url address:
http://www.symcorp.com/downloads/OLAP_CouncilWhitePaper.pdf) [visited at April
5, 2018].
Haag, S. and M. Cummings. 2013. Management Information Systems for the
Information Age, 9th Edition, McGraw-Hill.
Ho, J.K.K. 2015. "Examining Literature Review Practices and
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Research in Science Society and Culture (IJIRSSC) 1(1): 1-13.
Inmon, W.H., C. Imhoff and R. Sousa. 2001. Corporate Information Factory, 2 edition, Wiley.
Laudon, K.C. and J.P. Laudon. 2012. Management
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Hall.
Lungu, I. and A. Bâra. 2007. "Executive Information Systems's
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Informaica Economică 3(43): 87-90.
Nord, J.H. and G.D. Nord. 1995. "Executive information
systems: A study and comparative
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Management 29, Elsevier: 95-106.
Partanen, K. and V. Savolainen.
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Pilotsoftware.com. 2002. "An
Introduction to OLAP: Multidimensional Terminology and Technology" A white
paper, Pilot Software Acquisition Corp., One Canal Park, Cambridge, MA.
Saunders, M., P. Lewis and A.
Thornhill. 2012. Research Methods for
Business Students, sixth edition, Pearson.
Westerlund, P. 2008. Business
Intelligence: Multidimensional Data Analysis. Master Thesis in Computing Science August 20, 30 ECTS Credits (url
address: http://www.diva-portal.org/smash/get/diva2:1137039/FULLTEXT01.pdf)
[visited at April 5, 2018].
Www2.cs.uregina.ca. n.d. "Data
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April 6, 2018].
Appendix
Appendix 1: The Facebook-based
survey questions (14 questions) dated January 23 to 30, 2015 and responses
statistics (Ho, 2015).
|
Survey questions
|
Survey statistics
|
|
Question 1: What is your
gender?
|
Male: 57 (44.5%)
Female: 71 (55.5%)
|
|
Question 2: What is your age?
|
18 to 27: 6 (4.7%)
28 to 37: 60 (46.9%)
38 to 47: 52 (40.6%)
48 to 57: 10 (7.8%)
58 to 67: 0 (0.0%)
68 or above: 0 (0.0%)
|
|
Question 3: What is your
education background?
|
Not yet a degree-holder: 34
(26.6%)
Finished University
Undergraduate Degree study: 70 (54.7%)
Finished Master Degree study:
22 (17.2%)
Finished Ph.D. Degree study (or
equivalent): 2 (1.6%)
|
|
Question 4: What is your field
of education?
|
Business related: 97 (75.8%)
Non-business related: 14
(10.9%)
Both business and non-business
related: 15 (11.7%)
Unclassified: 2 (1.6%)
|
|
Question 5: Did you (or are
you) learn the subject of “Literature Review” in Research Methods in your
formal education?
|
Yes: 86 (67.7%)
No: 33 (26.0%)
Cannot remember: 8 (6.3%)
|
|
Question 6: Do you (or did you)
feel that you have difficulty to understand the subject of Literature Review
during your study of Research Methods (or other courses) for your formal
education?
|
Yes, I strongly feel so: 24
(18.8%)
I have this feeling mildly: 58
(45.3%)
I feel it is not difficult to
understand: 30 (23.4%)
No feeling at all/ Not
applicable: 16 (12.5%)
|
|
Question 7: Do you (or did you)
feel that academic journal articles are difficult to understand during your
study for your formal education?
|
Yes, I strongly feel so: 26
(20.3%)
I have this feeling mildly: 60
(46.9%)
I feel it is not difficult to
understand, in general: 35 (27.3%)
No feeling at all: 7 (5.5%)
|
|
Question 8: Do you (or did you)
use the University e-library to access academic journal articles to do your
course assignments and dissertation projects?
|
Yes, I do: 93 (72.7%)
No, I don’t: 31 (24.2%)
Cannot remember: 4 (3.1%)
|
|
Question 9: Do you (or did you)
feel that academic articles are useful for literature review?
|
Yes, very useful: 65 (50.8%)
It is basically useful: 47
(36.7%)
Not useful: 3 (2.3%)
No idea: 13 (10.2%)
|
|
Question 10: Do you (or did
you) feel that reading academic journal articles is able to improve your
professional competence?
|
Yes, I strongly feel so: 47
(36.7%)
I have this feeling mildly: 59
(46.1%)
I don’t think so: 16 (12.5%)
No idea: 6 (4.7%)
|
|
Question 11: Do you have access
to academic journal libraries (not Google scholar) when you are not studying
for a formal education program?
|
Yes, and convenient: 23 (18.0%)
Yes, but not convenient: 30
(23.4%)
Not able to access at all: 61
(47.7%)
No idea: 14 (10.9%)
|
|
Question 12: Are you interested
in improving your literature review skill in the near future?
|
Yes, I am strongly interested:
41 (32.5%)
I am mildly interested: 46
(36.5%)
No, not interested: 28 (22.2%)
No idea: 11 (8.7%)
|
|
Question 13: Do you feel that
you are able to improve your literature review skill without reading academic
journal articles?
|
Yes, I strongly fee so: 13
(10.2%)
I have this feeling mildly: 17
(13.3%)
No, I do not feel this way: 75
(58.6%)
No idea: 23 (18.0%)
|
|
Question 14: Do you enjoy
reading academic journal articles?
|
Yes, I enjoy it very much: 9
(7.0%)
I do, basically: 58 (45.3%)
No, I don’t: 51 (39.8%)
No feeling: 10 (7.8%)
|
[1]
While information users, in
their managerial roles, are primarily interested in gaining knowledge with
business value, those as researchers, e.g., doing applied business research,
are preoccupied with obtaining knowledge with business as well as academic
values.
