SG7012 STRATEGIC BUSINESS ANALYTICS ASSIGNMENT SAMPLE 2023
Introduction
The research is according to the perceptions of human favors, choices and needs of everyday using products. The primary goal is to managing the advertisement system of gender patterns with beauty product or cosmetic product advertising or publicizing, which includes many types of cosmetic products like soap, shampoo, deodorant, conditioner, perfume, lotion, makeup, cologne and chemical hair color, skin care, razors, salon services and other feminine care , as well as the detected benefits of authorization advertising. Gender pigeonholes specifically use refine observation of what represents an attractive, desirable, and acceptable people, often exploiting visual gender roles, and here mainly employed in publicities for care products. There are a lot of male who are losing their masculinity and tree are a lot of female who are involving themselves with feminism. Here the advertisement of the product by examining the body structure and how they maintenance their masculinity power and to those female with the level of femininity sensed. The surveys are adjoined to the human strategies and how they are managing their daily lives using beauty products and how many people are involved with the product using possibilities. Even the case study is going to find out the humans who are using hygiene products daily and how the dealing service they are offering per year with the company’s conditions. The value of money is also important when selling goods. Finding the average of selling cosmetic products over the range of distribution. The public who are enjoying it might be male or female. How the cosmetics extra uses are exploiting the gender stereotypes, with the level of human cosmetic users. Here the analysis will also help to know about the differences of the human strategies to maintain the advertisement connection over the field of others responses, which reports how much people are reaching to the point of empowerment advertising. Especially the state no some keys that show all evidence with data.
Methodology
Management of the advertising is important to properly promote all types of products to the persons who are really interested along with the all using criteria’s. The method of advertising and promoting is really tough to those who need to enhance their selling to the huge market range coefficient. The unique differences of the visual community with advertising strategies also present more options that really assist to respond to human needs, choices and the favor of something individually. The types of advertising like online management and awareness of the uses of good cosmetics products. The common system is to be followed by the graph of human responses and frequent production management. The behavior of humans shows how much they are enhancing their use of quality services. The method of acquiring the whole responses with their biodatas really impacts the business enhancement and the cosmetic user range. Using too much cosmetics really gives bad side effects to the people with masculinity and femininity (Chiang, et al. 2018). People are exploiting these types of qualities and the basic human sense along with sexuality and positivizes. Where are the procedures to bring the whole reports of how much the products are helping the male or female differently? 70% of females are using cosmetics nowadays while the rest 30% are male. The examination of how many people are really using the products per day basis and how are their experiences. The advertisement system is organized through a thorough collection of the people’s data and their personal ages, income ranges and the educational qualifications and everything else. The culture of their gender stereotypes totally belongs with the strong advertisements of the data analysis of the needy people. Men or women are abused with many types of products, those are really needed in control of the company or the human using strategies and quality changing methods.
Findings and results
There are huge counts after finding out the whole advertising results came from research individually. The transforms and the frequencies with the visibility result of the people who are agreed or not and neutral about the surveys.
| Transform | Count of Transform |
| Agree | 30 |
| Disagree | 1 |
| Neutral | 6 |
| Somewhat Agree | 35 |
| Somewhat Disagree | 1 |
| Strongly Agree | 32 |
| Grand Total | 105 |
Table 1: Transformation
(Source: Excel)
Figure 1: Transformation
(Source: Excel)
The table/list above proves that the huge majority of the defendants somehow agree with the transform. 30 are in agreement where 35 number of respondents somehow agree, strongly agree (32), only a several number (2) of the defendants disagree with the declaration (Smys, and Joe, 2019). Out of the one hundred and five respondents, 97 either agree (92%), strongly agree, or somewhat agree that authorization advertising, which specifically communicates the extraordinary differences in every individual, would assist transform ethnic gender stereotypes. This proof helps the noticed benefits of empowerment publicity.
| Education | Frequency |
| Associate Degree | 6 |
| Bachelor Degree | 44 |
| Doctorate Degree | 10 |
| High school diploma | 3 |
| J.D. | 1 |
| M.D | 1 |
| Master Degree | 16 |
| Some undergraduate courses | 24 |
| Grand Total | 105 |
Table 2: Education
(Source: Excel)
Figure 2: Education
(Source: Excel)
This section above responds that there are a lot of educated and non-educated people who are involved with these surveys. The most of the consumers are bachelor degree they are 44 out of 105, and then the master degree consumers are 16 out of 105, there also have 24 out of 105 number of consumers with some undergraduates courses, there also have 10 out of 105 doctorate degree consumers with the 6 out of 105 number of associated degree consumers and 3 out of 105 high school diploma customers. But the fewer customers associated with the J.D. and M.D. degree they are only number 2 out of 105 consumers (Pröllochs, and Feuerriegel, 2020). The frequency proof with respondents saying that there are a lot of consumers from young ages with bachelor degrees are regularly using the cosmetics products and they are feeling good ness and fairness upon it. Here the frequency of the results are evaluated through the diagram of the educated users meaning that the most of the cosmetics are bought by the educated users who seriously know how to make their skin better or about the skin care solution than non-educated consumers. Although there are 70 % more girls who are using cosmetics than men (Maté, 2017). However the ladies are hugely aware to maintain their hygiene better than men, they are always conscious about their body hair and sensual organs, so they use the high range of different cosmetics or beauty products to improve their feminism.
| Income | Frequency |
| $0 to < $10,000 | 24 |
| $10,000 to < $20,000 | 21 |
| $110,000 to < $130,000 | 3 |
| $150,000 or more | 1 |
| $20,000 to < $30,000 | 15 |
| $30,000 to < $40,000 | 14 |
| $40,000 to < $50,000 | 3 |
| $50,000 to < $60,000 | 9 |
| $60,000 to < $70,000 | 5 |
| $70,000 to < $80,000 | 4 |
| $80,000 to < $90,000 | 1 |
| $90,000 to < $110,000 | 5 |
| Grand Total | 105 |
Table 3: Income
(Source: Excel)
Figure 3: Income
(Source: Excel)
According to the table of the above analyzed, 24 out of 105 consumers have earnings between $0 to $10,000 per year, 21 out of 105 consumer’s income between $10,000 to $20,000 annually, 15 out of 105 customers are earning between $20,000 to $30,000 per annual, there are 14 consumers out of 105 earning between $30,000 to $40,000 per year (Krishnamoorthi, and Mathew, 2018). 3 out of 105 consumer’s income range between $40,000 to $50,000 yearly, 9 out of 105 customers are earning between $50,000 to $60,000 per year, 5 out of 105 consumers are earning $60,00 to $70,000 yearly, 4 out of 105 consumers are making $70,000 to $80,000 per year. 1 out of 105 consumers are earning $80,000 to $90,000 annually, 5 out of 105 customers are earning $90,000 to $110,000 per year, 3 out of 105 are earning $110,000 to $130,000 per year and ultimately 1 out of 105 are earning over $150,000 annually. The total number of consumers are 105 where the male customers are 19 and the female consumers are 86.
| Gender | Count of Stereotype |
| Female | 86 |
| Male | 19 |
| Grand Total | 105 |
Table 4: Gender
(Source: Excel)
Figure 4: Gender
(Source: Excel)
According to the stereotype diagram the gender along with the stereotype are female or male are vastly connected to the consumer’s discussion, the resultant discussion is in under with the calculated number chart.
| Count of Empowerment% | Column Labels | ||
| Empowerment | Female | Male | Grand Total |
| 0% | 32 | 4 | 36 |
| 1% | 7 | 3 | 10 |
| 2% | 4 | 2 | 6 |
| 3% | 1 | 1 | |
| 5% | 11 | 2 | 13 |
| 7% | 1 | 1 | |
| 8% | 1 | 1 | 2 |
| 10% | 24 | 3 | 27 |
| 15% | 3 | 3 | |
| 20% | 2 | 2 | 4 |
| 25% | 1 | 1 | |
| 30% | 1 | 1 | |
| Grand Total | 86 | 19 | 105
|
Figure 5: Empowerment
(Source: Excel)
There are the range of count of empowerment through the surveys are summarized over the ,male and female consumers with percentages, when the percentage of empowerment is 0% the 32 female and the only 4 , male has responded with the advertisement and among the column labels the grand total is 36 out of 105, same as when the empowerment is 1% the female number is 7 with the 3 number of male and the total number is 10 out of 105, same when the 2 % of empowerment the number of female is 4 and number of male is 2 so the totally 6 out of 105. Same as when 3% empowerment the number of the female is 1 but male number is 0 out of 19 (Shi, and Wang, 2018). When 5% of empowerment the number of females is 11 and the male is 2 out of 19, when 7% the number of females is 1 or male is 0 out of total number 105. When the empowerment is 8% the female or male are same as the number of 1 out of 105 totally, when it becomes 10 % of empowerment the number of female has increased to 24 there the male is only 3 out of 19, same as when the empowerment is 15% the number of female is 3 and the number of male is 0 out of 19, when the empowerment is 20% enhanced the number of female is 2 and same the number of male is 2, and finally when the empowerment has enhanced to 25% then no number of female are there with 1 number of male same as when the empowerment was 30%.
| Count of Transform | Column Labels | ||||||
| Transformation | Agree | Disagree | Neutral | Somewhat Agree | Somewhat Disagree | Strongly Agree | Grand Total |
| 0% | 11 | 1 | 2 | 14 | 1 | 7 | 36 |
| 1% | 2 | 2 | 6 | 10 | |||
| 2% | 2 | 2 | 2 | 6 | |||
| 3% | 1 | 1 | |||||
| 5% | 3 | 4 | 6 | 13 | |||
| 7% | 1 | 1 | |||||
| 8% | 1 | 1 | 2 | ||||
| 10% | 10 | 2 | 7 | 8 | 27 | ||
| 15% | 2 | 1 | 3 | ||||
| 20% | 2 | 2 | 4 | ||||
| 25% | 1 | 1 | |||||
| 30% | 1 | 1 | |||||
| Grand Total | 30 | 1 | 6 | 35 | 1 | 32 | 105 |
Figure 6: Transform
(Source: Excel)
Figure 7: Ad Frequency stereotype and Reinforcing
(Source: Excel)
According to the figure of ad frequency stereotypes and reinforcing the advertisements range that followed by the consumers have many different flows with the reinforcing segments. There the high range of the reinforcing moderated by influential position, and the medium range of the advertisement and business strategies are reinforced as drastically, so the drastic range of the reinforcing is at medium phase of the ad frequency model. Some of the limited or trivial positions are also in the same range of the figure of the reinforcing frequency, so the stereotype and reinforcing position are deriving the condition of the management (Nalchigar, and Yu, 2017). The range is totally higher, arranged by the overseas growth of the management criteria. To the advertisement range of the condition managers are connected through the advertisement system and distribution procedure also transform to the ad frequency. The ad frequency of the reinforcing of the stereotype system of the advertisement range, the selling or using criteria has growth hugely spread to the market form and consumers area.
| Count of Transform | Transform | ||||||
| Education | Agree | Disagree | Neutral | Somewhat Agree | Somewhat Disagree | Strongly Agree | Grand Total |
| Associate Degree | 1 | 1 | 4 | 6 | |||
| Bachelor Degree | 15 | 1 | 2 | 19 | 7 | 44 | |
| Doctorate Degree | 2 | 1 | 6 | 1 | 10 | ||
| High school diploma | 2 | 1 | 3 | ||||
| J.D. | 1 | 1 | |||||
| M.D | 1 | 1 | |||||
| Master Degree | 4 | 2 | 6 | 4 | 16 | ||
| Some undergraduate courses | 7 | 1 | 1 | 15 | 24 | ||
| Grand Total | 30 | 1 | 6 | 35 | 1 | 32 | 105 |
Figure 7: Transform vs Education
(Source: Excel)
| Count of Transform | Gender | ||
| Transform | Female | Male | Grand Total |
| Agree | 25 | 5 | 30 |
| Disagree | 1 | 1 | |
| Neutral | 5 | 1 | 6 |
| Somewhat Agree | 26 | 9 | 35 |
| Somewhat Disagree | 1 | 1 | |
| Strongly Agree | 29 | 3 | 32 |
| Grand Total | 86 | 19 | 105 |
Figure 8: Transform vs Gender
(Source: Excel)
| Ad Frequency | |
| Mean | 45.45714 |
| Standard Error | 12.65691 |
| Median | 12 |
| Mode | 10 |
| Standard Deviation | 129.6948 |
| Sample Variance | 16820.73 |
| Kurtosis | 45.57855 |
| Skewness | 6.310269 |
| Range | 1100 |
| Minimum | 0 |
| Maximum | 1100 |
| Sum | 4773 |
| Count | 105 |
| Largest(1) | 1100 |
| Smallest(1) | 0 |
| Confidence Level (95.0%) | 25.09913 |
Table 5: Descriptive statistics of Ad frequency
(Source: Excel)
From the table of ad frequency the mean number of the ad frequency is 45.45714, and here the standard error 12.65691 has resulted. The frequency from the reinforcing, the median value is 12 and the mode is 10 for the consumer’s experiences. The standard deviation which has been resulted as 129.6948. The sample variance is 16820.73 for the result appearances with the kurtosis 45.57855 and the Skewness 6.310269 and the least range is 1100 where the minimum number is 0 and the maximum number is 1100 (Boldosova, and Luoto, 2019). The calculated Sum is 4773 from the count of the consumers 105. The largest value is 1100 and the smallest value is 0 with the confidence level of the consumers is 95.0% or 25.09913.
| Age | |
| Mean | 29.26667 |
| Standard Error | 1.152658 |
| Median | 24 |
| Mode | 23 |
| Standard Deviation | 11.81123 |
| Sample Variance | 139.5051 |
| Kurtosis | 2.180954 |
| Skewness | 1.796609 |
| Range | 49 |
| Minimum | 19 |
| Maximum | 68 |
| Sum | 3073 |
| Count | 105 |
| Largest(1) | 68 |
| Smallest(1) | 19 |
| Confidence Level (95.0%) | 2.285764 |
Table 6: Descriptive statistics of Age
(Source: Excel)
From the premeditated result of the above table 6 the age responsibility counted to the chart with the business enhance ability. Excel resulted with the mean number of ages who were influenced by the advertisement is 29.26667, here the standard error is 1.152658. The median number of masculine and feminism responds 24 as the mode is 23. The standard deviation is 11.81123 with variance of the sample 139.5051. The Kurtosis is 2.180954 with the number of skewness as 1.796609. The range of the age factor is up to 49 for the advertisement growth, the minimum range is 19 and the Maximus range is 68 as the format. The sum of the total numbers 3073 with counts of the consumers were involved 105. The largest number of the frequencies is 68 and the smallest number is 19. The confidence level for the advertisement of the customers increased to 95.0% or 2.285764.
| Spending | |
| Mean | 649.9238 |
| Standard Error | 78.70544 |
| Median | 400 |
| Mode | 500 |
| Standard Deviation | 806.4908 |
| Sample Variance | 650427.3 |
| Kurtosis | 10.47075 |
| Skewness | 2.910982 |
| Range | 4980 |
| Minimum | 20 |
| Maximum | 5000 |
| Sum | 68242 |
| Count | 105 |
| Largest(1) | 5000 |
| Smallest(1) | 20 |
| Confidence Level (95.0%) | 156.0758 |
Table 7: Descriptive statistics of Spending
(Source: Excel)
From the result of the spending values are calculated from the statistics of table no 7. The mean value of this section is 649.9238 with the standard error is 78.70544. From the spending range the median value has increased to 400 and then the mode is 500. The standard Deviation from the advertised spending frequency is 806.4908 with the sample variance 650427.3. The Kurtosis is 10.47075 and the skewness is 2.910982. The range of the spending statistics are 4980 with minimum efficiency as 20 and the maximum is 5000. The sum of total spending means 68242 for the 105 customers where the largest number is 5000 and the smallest number is 20. The confidence level of the spending frequency is 95.0% or 156.0758 for the spending numbers.
| Stereotype | |
| Mean | 43.38095 |
| Standard Error | 12.18002 |
| Median | 10 |
| Mode | 10 |
| Standard Deviation | 124.808 |
| Sample Variance | 15577.05 |
| Kurtosis | 44.4728 |
| Skewness | 6.236635 |
| Range | 1050 |
| Minimum | 0 |
| Maximum | 1050 |
| Sum | 4555 |
| Count | 105 |
| Largest(1) | 1050 |
| Smallest(1) | 0 |
| Confidence Level (95.0%) | 24.15343 |
Table 8: Descriptive statistics of stereotype
(Source: Excel)
According to the excel results of table 8, the statistics of the stereotypes describe the countdown of the values as the research about the advertisement reinforcing frequency and the empowerment with the strategies. The mean number of the stereotype is 43.38095 and the standard error is 12.18002 with the median number of 10 as same as the mode number is 10. The standard deviation resulted as 124.808 with the sample variance 15577.05. The Kurtosis number is 44.4728 as the skewness number is 6.236635. From the stereotype calculations the range of the stereotype goes to 1050 here the minimum value is 0 as the maximum value is 1050. The sum of these stereotyped resulted numbers is 4555 from the counted number of the consumers 105. Here the largest number is 1050 when the smallest number is 0. The confidence level has counted as 95.0% but the number is 24.15343 of the total stereotype results.
Discussion
There are a lot of consumers who utilize cosmetics or beauty products with an average range of uses, therefore the research has been analyzed through the 105 number of persons where the number of females is 86 and the number of males is 19. The average users are maintaining their hygiene on a daily basis, while some of the consumers are wasting their products and the female users are more than according to the consumers chart. The annual income range of the consumers with the advertisement strategies which has been followed by the system of data analysis theorem. The advertising process through the television, internet, radio, billboard, newspaper, mail and with daily magazines hugely carried the business growth and the user’s empowerment (Chen, and Nath, 2018). A lot of gender role stereotypes are purely included with the advertise management system where the female medium empowerment is better than the male product advertising empowerment examination. There are a lot of skin care products produced across the higher reputed companies on a daily basis like face wash , face serum, shampoo, conditioner, body oil, hair serum, hair oil, soap, lotion, skin fairness products, makeup products etc. the stereotype of male and female users promote are value range over the product advertising methods, the graph of the selected consumers report that the empowerment of advertising and using the cosmetics products the women or female has a different type of vast users range that seriously enhancing the graph of users or consumers with the business ,module of cosmetics products making companies. 70% of female consumers are generally on the top of advertisements with gender stereotypes who are big users. The male are showing masculinity while the female are making more feminism that is bathe main fact of the gender role with the users. Some male users are becoming the female types with some cosmetics products and they are negating that they are not pure masculine. The properties of advertisement growth as influential statistics and the managing accessibility are going to enhance the market range from the management criteria, where the limited and trivial position is maintained by the accessibility stereotypes. Obviously the matters are based on the fundable of the advertising stereotypes and the reinforcing obligations. When the products are increasing the market range with the consumers drastic uses the 75% of women are included to the using appearances. The market values still have developed by most of the females who are using the cosmetics products and the high range cosmetics goods. There are also the same statistics of uses of the products as followed by the startups graph where the consumers are giving the best range for every product and increasing the market value for their advertisement and frequency of reinforcing management. Where the management of the advertisement with the new consumers who have showed their reinforcing (Hindle, and Vidgen, 2018). The products users are giving the different results to the provider’s management system with the good reviews which are monotint above the table, along with the female and male connector from the surveys are differently discussed about their product using range and the value of their product using system, where they are eventually included with their data and advertisement procedures. The physical shape of the bodies are representing the male or female types. The male or female structure is different, but there are so many cosmetics users who are hiding their looks by using the products, so today the empowerment for cosmetics is giving different concepts. Management of the advertising strategies frequently promote the subject of the consumer perceptions that are conducted by the reporters, to find out the gender role for the range of cosmetics users with the advertisement respondents. There is the discussion over the income statements of the consumers and how much they agree with the advertising of products and using those along with their gender roles. The strongly agreed are most of the female and disagrees come from most of the male persons. The average percentage of views of the advertisement of utilization and the impact of the empowerment advertising. The parathion of the 4 tables give the charts based upon the ad frequency of the reinforcing to the market range and the business spreading through the consumers with their details of uses as their ages or earnings and educations (Esswein, and Chamoni, 2018). The spending or stereotypes are clearly discussing the market spend for human awareness and their needs and their differences of any kind of products and what type of people are involved with these. The female or make nature and their alternatives activities also debited through based on the public’s response.
Conclusion and Recommendations
Along with the 105 responses, the analysis between female and male consumers who are the perfect users of the cosmetics products and their income chart with the agreements and their educational qualifications also included the models. The section purely describes the users of the cosmetics products, the attraction of the users with their body shape and the diversity of beauty which are represented by male and female, the empowerment of advertising the range of the products and the social influence through the users comes from advertising. There are the various types of gender roles to which the consumers have responded through the advertisement strategies. The various types of people with their educational background and the different kinds of choices with their uses are already mentioned above. The consumers need to increase their personal using properties with the level of their stereotype graph and the excel sheets of their Chinese and income ranges. The annual income of the consumers with their use of a range of cosmetics products are getting the huge substantial growth for the future business advertisement and users credit. The mentioned people from different types of body language and characteristics activities were briefly debated for their ad senses and marketing senses as customers. The management system also provided the distribution and histograms, and the descriptive statistics measures with the promotion of the variables for the male and female patterns of users. There are mentioned about the technical analyzing formats for with the appropriate conclusion over the consumers need. The females are giving the best graph as the cosmetic product using values and systemic business range. The percentages of their empowerment advertising gives the over evaluated makeover. The advertisement method and the product spreading method are giving the best responses over the minimum optimization of their critical analysis. The male are masculine enough so they are not too much interested for the cosmetics using, but the female gave their own personality with the beauty product using, which makes them beauty looking and gorgeous. There are so many huge results from the above tables, where the female and male are making the consumers range to the different priorities and purposes to the consumer’s goal. The page is surely arranged by many concepts yet these will help the readers.
Reference
Aydiner, A.S., Tatoglu, E., Bayraktar, E., Zaim, S. and Delen, D., 2019. Business analytics and firm performance: The mediating role of business process performance. Journal of business research, 96, pp.228-237.
Boldosova, V. and Luoto, S., 2019. Storytelling, business analytics and big data interpretation: Literature review and theoretical propositions. Management Research Review.
Chen, L. and Nath, R., 2018. Business analytics maturity of firms: an examination of the relationships between managerial perception of IT, business analytics maturity and success. Information Systems Management, 35(1), pp.62-77.
Chiang, R.H., Grover, V., Liang, T.P. and Zhang, D., 2018. Strategic value of big data and business analytics.
Conboy, K., Mikalef, P., Dennehy, D. and Krogstie, J., 2020. Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda. European Journal of Operational Research, 281(3), pp.656-672.
Delgosha, M.S., Hajiheydari, N. and Saadeatmantesh, H., 2020. Semantic structures of business analytics research: applying text mining methods. Information Research, 25(2).
Esswein, M. and Chamoni, P., 2018. Business Analytics in Finance Department–A Literature Review. Muticonferenz Wirtschaftsinformatik, Luneburg.
Grover, V., Chiang, R.H., Liang, T.P. and Zhang, D., 2018. Creating strategic business value from big data analytics: A research framework. Journal of Management Information Systems, 35(2), pp.388-423.
Hindle, G.A. and Vidgen, R., 2018. Developing a business analytics methodology: A case study in the foodbank sector. European Journal of Operational Research, 268(3), pp.836-851.
Junior, C.H., Oliveira, T. and Yanaze, M., 2019. The adoption stages (Evaluation, Adoption, and Routinisation) of ERP systems with business analytics functionality in the context of farms. Computers and electronics in agriculture, 156, pp.334-348.
Krishnamoorthi, S. and Mathew, S.K., 2018. Business analytics and business value: A comparative case study. Information & Management, 55(5), pp.643-666.
Liu, Y., Han, H. and DeBello, J., 2018. The challenges of business analytics: successes and failures.
Maté, A., Trujillo, J. and Mylopoulos, J., 2017. Specification and derivation of key performance indicators for business analytics: A semantic approach. Data & Knowledge Engineering, 108, pp.30-49.
Nalchigar, S. and Yu, E., 2017, July. Conceptual modeling for business analytics: a framework and potential benefits. In 2017 IEEE 19th Conference on Business Informatics (CBI) (Vol. 1, pp. 369-378). IEEE.
Nam, D., Lee, J. and Lee, H., 2019. Business analytics adoption process: An innovation diffusion perspective. International Journal of Information Management, 49, pp.411-423.
Nam, D., Lee, J. and Lee, H., 2019. Business analytics use in CRM: A nomological net from IT competence to CRM performance. International Journal of Information Management, 45, pp.233-245.
Nielsen, S., 2018. Reflections on the applicability of business analytics for management accounting–and future perspectives for the accountan
Power, D.J., Heavin, C., McDermott, J. and Daly, M., 2018. Defining business analytics: an empirical approach. Journal of Business Analytics, 1(1), pp.40-53.
Pröllochs, N. and Feuerriegel, S., 2020. Business analytics for strategic management: Identifying and assessing corporate challenges via topic modeling. Information & Management, 57(1), p.103070.
Ratia, M., Myllärniemi, J. and Helander, N., 2019. The potential beyond IC 4.0: the evolution of business intelligence towards advanced business analytics. Measuring Business Excellence.
Shi, Z. and Wang, G., 2018. Integration of big-data ERP and business analytics (BA). The Journal of High Technology Management Research, 29(2), pp.141-150.
Smys, S. and Joe, C.V., 2019. Big data business analytics as a strategic asset for health care industry. Journal of ISMAC, 1(02), pp.92-100.
Stransky, M., Reder, R., Huber, S. and Hauer, G., 2019. Change of the Role of a Controller through Business Analytics. In Digitalen Wandel gestalten (pp. 75-86). Springer Gabler, Wiesbaden.
Sun, Z., Strang, K. and Firmin, S., 2017. Business analytics-based enterprise information systems. Journal of Computer Information Systems, 57(2), pp.169-178.
Wang, S., Yeoh, W., Richards, G., Wong, S.F. and Chang, Y., 2019. Harnessing business analytics value through organizational absorptive capacity. Information & Management, 56(7), p.103152.
Wang, Z., Wang, N., Su, X. and Ge, S., 2020. An empirical study on business analytics affordances enhancing the management of cloud computing data security. International Journal of Information Management, 50, pp.387-394.
Know more about UniqueSubmission’s other writing services:
