BUSI 701 LIBERTY UNIVERSITY Week_7_Economic_Data_Annotated_Bibiliography_ASSIGNMENT_2024.pdf

ANNOTATED BIBLIOGRAPY 1

Economic Data

Edmund Y. Asare

School of Business, DBA: Accounting Cognate, Liberty University

ANNOTATED BIBLIOGRAPY 2

Acharya, A., Singh, S. K., Pereira, V., & Singh, P. (2018). Big data, knowledge co-creation and

decision making in fashion industry. International Journal of Information Management,

42, 90-101. https://doi.org/10.1016/j.ijinfomgt.2018.06.008

This research explores the role of big data in the fashion industry and its impact on

decision-making and knowledge co-creation. The authors argue that the fashion industry

is increasingly relying on big data analytics to gain insights into consumer preferences

and trends. They highlight the importance of knowledge co-creation, which involves

collaboration between different stakeholders in the industry, such as designers, retailers,

and consumers, to generate new knowledge and make informed decisions. In their study,

various applications of big data analytics, such as trend forecasting, customer

segmentation, and supply chain optimization were investigated to provide a

comprehensive review of the existing literature on big data analytics in the fashion

industry. The article examines the challenges and opportunities associated with the use of

big data in the fashion industry, including data privacy concerns and the need for skilled

data analysts. The study presents a conceptual framework for understanding the process

of knowledge co-creation in the fashion industry. The authors propose that knowledge

co-creation involves data collection and analysis, knowledge generation, and decision

making, arguing that effective knowledge co-creation requires collaboration and

information sharing among different stakeholders. Their findings depict the usefulness of

big data analytics to fashion companies in making more timely and accurate decisions by

providing insights into consumer preferences and market trends. They emphasize the

importance of integrating big data analytics into the decision-making process to improve

business performance and competitiveness.

ANNOTATED BIBLIOGRAPY 3

Awan, U., Shamim, S., Khan, Z., Zia, N. U., Shariq, S. M., & Khan, M. N. (2021). Big data

analytics capability and decision-making: The role of data-driven insight on circular

economy performance. Technological Forecasting & Social Change, 168, 120766.

https://doi.org/10.1016/j.techfore.2021.120766

The authors examine the relationship between big data analytics capability, decision-

making, and circular economy performance. The authors conducted a survey-based study,

collecting data from 250 manufacturing firms in Pakistan, designed to measure the

variables of interest, including big data analytics capability, decision-making, and

circular economy performance. The findings of the study indicate a positive relationship

between big data analytics capability and decision-making which mediates the

relationship between big data analytics capability and circular economy performance.

The authors argue that firms with higher big data analytics capability are more likely to

make informed decisions based on data-driven insights, which, positively impact the

firm’s circular economy performance. The study emphasizes the role of data-driven

insights in decision-making and its subsequent impact on performance, however, some

limitations include the study being conducted in the manufacturing sector of Pakistan,

which may limit the generalizability of the findings to other industries or countries. In

addition, the data was collected through self-reported surveys, which may introduce

response bias. The valuable insights churned out by the study have implications for

managers and policymakers seeking to enhance their organization’s performance in the

circular economy through the effective use of big data analytics.

ANNOTATED BIBLIOGRAPY 4

Merendino, A., Dibb, S., Meadows, M., Quinn, L., Wilson, D., Simkin, L., & Canhoto, A.

(2018). Big data, big decisions: The impact of big data on board level decision-making.

Journal of Business Research, 93, 67-78. https://doi.org/10.1016/j.jbusres.2018.08.029

This research explores the impact of big data on board level decision-making in

organizations. The authors argue that big data has the potential to significantly influence

decision-making processes at the highest level of management. The study aims to

understand how big data is used by boards, the challenges they face in utilizing it, and the

implications for decision-making. The authors conducted a qualitative study using semi-

structured interviews with board members from various organizations, finding out that

big data is increasingly being used by boards to inform decision-making. The study also

found that big data provides boards with access to real-time and comprehensive

information, enabling them to make more informed and data-driven decisions, while

identifying patterns and trends leading to better strategic planning and risk management.

The authors highlight several challenges faced by boards in utilizing big data including

the complexity of data analysis, the need for data literacy among board members, and

concerns about data privacy and security. The researchers suggest that organizations need

to invest in data analytics capabilities and provide training to board members to

overcome these challenges. In their argument, the authors imply that board need to adapt

their decision-making processes to incorporate big data effectively and proposed a

framework that outlines the steps boards can take to leverage big data, including

identifying relevant data sources, investing in data capabilities and fostering a data-driven

culture within the organization.

ANNOTATED BIBLIOGRAPY 5

Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business

intelligence and decision making using big data analytics. Information Processing &

Management, 58(6), 102725. https://doi.org/10.1016/j.ipm.2021.102725

This article explores the role of organizational business intelligence (OBI) and decision

making in the context of big data analytics. The authors argue that with the increasing

availability of big data, organizations need to leverage advanced analytics techniques to

extract valuable insights and make informed decisions. The article provides a

comprehensive overview of the key concepts, challenges, and opportunities associated

with OBI and decision making using big data analytics. The authors begin by discussing

the concept of OBI and its importance in modern organizations highlighting the need for

organizations to collect, analyze, and interpret data to gain a competitive advantage. The

article emphasizes the role of big data analytics in enabling organizations to extract

meaningful insights from large and complex datasets and delves into the challenges

associated with OBI and decision making using big data analytics. The authors identify

issues such as data quality, data integration, privacy concerns, and the need for skilled

personnel arguing that organizations need to address these challenges to effectively

leverage big data analytics for decision making. The article concludes with a discussion

on the future trends and directions in OBI and decision making using big data analytics.

The authors highlight the importance of real-time analytics, predictive analytics, and the

integration of structured and unstructured data. They also emphasize the need for

organizations to develop a data-driven culture and invest in the necessary infrastructure

and skills.

ANNOTATED BIBLIOGRAPY 6

Ulman, Musteen, M., & Kanska, E. (2021). Big data and decision‐making in international

business. Thunderbird International Business Review., 63(5), 597–606.

https://doi.org/10.1002/tie.22225

This article explores the role of big data in decision-making processes within the context

of international business providing a detailed analysis of the various ways in which big

data is collected, analyzed, and utilized to make informed decisions in international

business operations. The authors begin by highlighting the increasing importance of big

data in the global business environment arguing that the availability of vast amounts of

data and advanced analytics tools provide businesses with valuable insights that can

optimize decision-making processes. Additionally, they emphasize the potential benefits

of leveraging big data in international business, such as improved market intelligence,

better risk management, and enhanced supply chain operations. The article then delves

into the different sources and types of big data in international business exploring both

structured and unstructured data sources, including social media, digital platforms,

customer databases, and sensor data explaining how these data sources can be combined

and analyzed to extract actionable insights and support decision-making. The authors

discuss the challenges associated with harnessing big data for decision making in

international business highlighting issues such as data quality, privacy concerns, data

integration, and the need for analytical capabilities. Additionally, they outline the

managerial challenges that arise from using big data, including organizational alignment,

talent acquisition and training, and resistance to change. To address these challenges, the

authors propose a framework for effective decision-making using big data in international

ANNOTATED BIBLIOGRAPY 7

business which includes four key steps: data collection, data integration and analysis,

decision making, and implementation, arguing that by following this framework,

businesses can maximize the value of big data in their decision-making processes. The

article is well-researched and presents a balanced perspective on the topic, making it a

valuable resource for scholars and practitioners interested in the intersection of big data

and international business.

ANNOTATED BIBLIOGRAPY 8

References