[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ skip to main content
research-article

A new perspective of BDA and information quality from final users of information: : A multiple study approach

Published: 01 December 2023 Publication History

Abstract

Although organizational factors related to big data analytics (BDA) and its performance have been studied extensively, the number of failed BDA projects continues to rise. The quality of BDA information is a commonly cited factor in explanations for such failures and could prove key to improving project performance. Using the resource-based view (RBV) lens, data analytics literature, business strategy control, and an empirical setup of two studies based on marketing and information technology managerial data, we draw on the dimensions of the balanced scorecard (BSC) as an integrating framework of BDA organizational factors. Specifically, we tested a model –from two different perspectives– that would explain information quality through analytical talent and organizations' data plan alignment. Results showed that both managers have a different understanding of what information quality is. The characteristics that make marketing a better informer of information quality are identified. In addition, hybrid (embedded) type analyst placements are seen to achieve better performance. Moreover, we add greater theoretical rigour by incorporating the moderating effect of the use of big data analytics in companies. Finally, the BSC provided a greater causal understanding of the resources and capabilities within a data strategy.

Highlights

Information quality as a new theoretical lens that explains the performance of BDA.
The alignment of the data plan as a fundamental objective in a data strategy.
The best performing hybrid talent organization in BDA.
The marketing perspective as the best informant of the BDA studies.
The Scorecard as an integrating framework of the data plan, with methodological and strategic effects.

References

[1]
R. Agarwal, V. Dhar, Big data, data science, and analytics: The opportunity and challenge for IS research, Information Systems Research 25 (3) (2014) 443–448,.
[2]
P. Akhtar, J.G. Frynas, K. Mellahi, S. Ullah, Big data-savvy teams’ skills, big data-driven actions and business performance, British Journal of Management 30 (2) (2019) 252–271,.
[3]
S. Akter, S. Fosso-Wamba, A. Gunasekaran, R. Dubey, S.J. Childe, How to improve firm performance using big data analytics capability and business strategy alignment, International Journal of Production Economics 182 (2016) 113–131,.
[4]
S. Akter, S. Fosso Wamba, M. Barrett, K. Biswas, How talent capability can shape service analytics capability in the big data environment, Journal of Strategic Marketing 27 (6) (2019) 521–539,.
[5]
Aral, S., & Weill, P. (2007). I.T. Assets, Organizational Capabilities and Firm Performance: Do Resource Allocations and Organizational Differences Explain Performance Variation? OrganizationScience, 18(5), 763–780. https://doi.org/10.1287/orsc.1070.0306.
[6]
U. Awan, S.H. Bhatti, S. Shamim, Z. Khan, P. Akhtar, M.E. Balta, The role of big data analytics in manufacturing agility and performance: Moderation–mediation analysis of organizational creativity and of the involvement of customers as data analysts, British Journal of Management 33 (3) (2022) 1200–1220,.
[7]
M. Bahrami, S. Shokouhyar, The role of big data analytics capabilities in bolstering supply chain resilience and firm performance: a dynamic capability view, Information Technology & People 35 (5) (2022) 1621–1651,.
[8]
J. Baker, H. Singh, The roots of misalignment: Insights on strategy implementation from a system dynamics perspective, Journal of Strategic Information Systems 28 (4) (2019),.
[9]
H. Barham, T. Daim, The use of readiness assessment for big data projects, Sustainable Cities and Society 60 (2020),.
[10]
J. Barney, Firm resources and sustained competitive advantage, Journal of Management 17 (1) (1991) 99–120,.
[11]
J. Barney, M. Wright, D.J. Ketchen, The resource-based view of the firm: Ten years after, Journal of Management 27 (2001) 625–641,.
[12]
R.M. Baron, D.A. Kenny, The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations, Journal of Personality and Social Psychology 51 (6) (1986) 1173–1182.
[13]
D. Barton, D. Court, Making advanced analytics work for you, Harvard Business Review 90 (10) (2012) 78–83,.
[14]
D. Barton, D. Court, Making advanced analytics work for you spotlight on big data, Harvard Business Review October (2012) 78.
[15]
A. Bharadwaj, O.A. El Sawy, P.A. Pavlou, N. Venkatraman, Digital business strategy: Toward a next generation of insights, MIS Quarterly: Management Information Systems 37 (2) (2013) 471–482,.
[16]
R. Chavez, W. Yu, C. Gimenez, B. Fynes, F. Wiengarten, Customer integration and operational performance: The mediating role of information quality, Decision Support Systems 80 (2015) 83–95,.
[17]
H.-M. Chen, R. Kazman, R. Schütz, F. Matthes, How Lufthansa capitalized on big data for business model renovation, MIS Quarterly Executive 16 (4) (2017) 19–34.
[18]
L. Chen, H. Liu, Z. Zhou, M. Chen, Y. Chen, IT-business alignment, big data analytics capability, and strategic decision-making: Moderating roles of event criticality and disruption of COVID-19, Decision Support Systems 161 (2022),.
[19]
G.W. Cheung, R.B. Rensvold, Structural equation modeling: A evaluating goodness-of- fit indexes for testing measurement invariance, Structural Equation Modeling: A Multidisciplinary Journal 9 (2009) (2009) 233–255.
[20]
Chiang, H.L., Grover, V., Liang, T., Zhang, D., & Hoang, P. (2018). Editorial Introduction. 35(2), 381–382.
[21]
W.W. Chin, G.A. Marcoulides (Ed.), The partial least squares approach to structural equation modelling, Modern Methods for Business Research, 1998, pp. 295–336.
[22]
L.A. Clark, D. Watson, Constructing validity: Basic issues in objective scale development, Psychological Assessment 7 (1995) 309–319,.
[23]
I. Constantiou, J. Kallinikos, New games, new rules: Big data and the changing context of strategy, Journal of Information Technology 30 (1) (2015) 44–57,.
[24]
Davenport, T. (2018a). The Analytics Team. In INFORMS Analytics Body of Knowledge (pp. 49–76). https://doi.org/https://doi.org/10.1002/9781119505914.ch3.
[25]
Davenport, T.H., & Bean, R. (2019). Big Data and AI Executive Survey 2019 (2019th ed.). Retrieved from www.newvantage.com.
[26]
Davenport, Thomas. (2006). Competing on analytics. Harvard Business Review, 84(1), 98–107. Retrieved from http://search.ebscohost.com.ezproxy.usal.es/login.aspx?direct=true&db=bth&AN=19117901&site=ehost-live.
[27]
T. Davenport, Big data at work: dispelling the myths, uncovering the opportunities, in: T.H. Davenport (Ed.), Review Press, first ed., Harvard Business, Boston, 2014.
[28]
T. Davenport, How strategists use “big data” to support internal business decisions, discovery and production, Strategy & Leadership 42 (4) (2014) 45–50,.
[29]
T. Davenport, The analytics team, Informs Analytics Body of Knowledge (2018) 49–76,.
[30]
T. Davenport, J. Harris, Competing on analytics: The new science of winning, first ed., Harvart Business School Press, Boston, 2007.
[31]
T. Davenport, J.G. Harris, Automated decision making comes of age, Mitosz Sloan Management Review 46 (4) (2005) 83–89. 〈http://sloanreview.mit.edu/article/automated-decision-making-comes-of-age/〉.
[32]
T. Davenport, D. Leandro, What ’ S your data strategy ?, Harvard Business Review June (2017) 112–122.
[33]
R.Y. Du, O. Netzer, D.A. Schweidel, D. Mitra, Capturing marketing information to fuel growth, Journal of Marketing 85 (1) (2020) 163–183,.
[34]
R. Dubey, A. Gunasekaran, S.J. Childe, C. Blome, T. Papadopoulos, Big data and predictive analytics and manufacturing performance: Integrating institutional theory, resource-based view and big data culture, British Journal of Management 30 (2) (2019) 341–361,.
[35]
Duncan, B.A. D., & Rollings, M. (2021). A Practical Data and Analytics Strategy and Operating Model for Midsize Enterprises. Retrieved June 29, 2023, from Gartner, Inc. website: https://emtemp.gcom.cloud/ngw/globalassets/en/information-technology/documents/insights/743027-a-practical-data-and-analytics-strategy-and-operating-model-for-midsize-enterprises.pdf.
[36]
European Community. (2003). Types of companies according to size. Retrieved from Diario Oficial de la Unión Europea website: https://europa.eu/european-union/about-eu/institutions-bodies/european-commission_es.
[37]
Falk, R., & Miller, N.B. (1992). A Primer for Soft Modeling. Open Journal of Business and Management, 2(April), 103. Retrieved from http://books.google.com/books/about/A_Primer_for_Soft_Modeling.html?id=3CFrQgAACAAJ.
[38]
A. Field, Discovering statistics using spss, third ed., Sage Pulications Ltd, Ed., London, 2009.
[39]
C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research 18 (1981) 39–50.
[40]
S. Fosso-Wamba, S. Akter, M. de Bourmont, Quality dominant logic in big data analytics and firm performance, Business Process Management Journal 25 (3) (2019) 512–532,.
[41]
S. Fosso-Wamba, S. Akter, A. Edwards, G. Chopin, D. Gnanzou, How “big data” can make big impact: Findings from a systematic review and a longitudinal case study, International Journal of Production Economics 165 (2015) 234–246,.
[42]
S. Fosso-Wamba, R. Dubey, A. Gunasekaran, S. Akter, The performance effects of big data analytics and supply chain ambidexterity: The moderating effect of environmental dynamism, International Journal of Production Economics 222 (September 2019) (2020),.
[43]
S. Fosso-Wamba, A. Gunasekaran, S. Akter, S.J. fan Ren, R. Dubey, S.J. Childe, Big data analytics and firm performance: Effects of dynamic capabilities, Journal of Business Research 70 (2017) 356–365,.
[44]
S. Fosso Wamba, S. Akter, L. Trinchera, M. De Bourmont, Turning information quality into firm performance in the big data economy, Management Decision 57 (8) (2019) 1756–1783,.
[45]
R. Furr, V. Bacharach, I. Fourth (Ed.), Psychometrics: An Introduction, SAGE Publications, 2021.
[46]
M. Furukawa, S. Hirobayashi, T. Misawa, A study on the “flexibility” of information systems (Part 3): MIS flexibility planning scheme for IT/business strategy alignment, International Journal of Business and Management 9 (6) (2014),.
[47]
J. Gao, Z. Sarwar, How do firms create business value and dynamic capabilities by leveraging big data analytics management capability, Information Technology and Management (2022),.
[48]
Gaskin, J. & Lim, J. (2016). Model Fit Measures. Retrieved from AMOS Plugin website: 〈http://statwiki.kolobkreations.com/index.php?title=Main_Page〉.
[49]
Gaskin, J., & James, M. (2019). HTMT Plugin for AMOS. Retrieved from http://statwiki.gaskination.com/index.php?title=Plugins.
[50]
J.E. Gerow, V. Grover, J. Thatcher, Alignment’s nomological network: Theory and evaluation, Information & Management 53 (5) (2016) 541–553,.
[51]
M. Ghasemaghaei, The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage, International Journal of Information Management 50 (2020) 395–404,.
[52]
M. Ghasemaghaei, S. Ebrahimi, K. Hassanein, Data analytics competency for improving firm decision making performance, Journal of Strategic Information Systems 27 (1) (2018) 101–113,.
[53]
N. Gorla, T.M. Somers, B. Wong, Organizational impact of system quality, information quality, and service quality, Journal of Strategic Information Systems 19 (3) (2010) 207–228,.
[54]
W.A. Günther, M.H. Rezazade Mehrizi, M. Huysman, F. Feldberg, Debating big data: A literature review on realizing value from big data, Journal of Strategic Information Systems 26 (3) (2017) 191–209,.
[55]
M. Gupta, J.F. George, Toward the development of a big data analytics capability, Information and Management 53 (8) (2016) 1049–1064,.
[56]
S. Gupta, A.K. Kar, A. Baabdullah, W.A.A. Al-Khowaiter, Big data with cognitive computing: A review for the future, International Journal of Information Management 42 (2018) 78–89,.
[57]
Hagen, C., Khan, K., Ciobo, M., & Wall, D. (2013). Big Data and the Creative Destruction of Today ’ s Business Models. AT Kearney Publication, pp. 1–18. Retrieved from www.atkearney.com.
[58]
Hair, J., Black, W., Barry, B., & Anderson, R. (2019). Multivariate Data Analysis (8th ed.; C. Learning, Ed.). Hampshire: Annabel Ainscow.
[59]
Joseph Hair, S. Marko, R. Christian, S. Guderman, Advanced issues in partial least squares structural equation modeling, Vol. 2155, Sage, Ed., Los Angeles, 2018.
[60]
Hassna, G., & Lowry, P. (2016). Big data capability, customer agility, and organization performance: A dynamic capability perspective. International Conference on Information Systems (ICIS 2016). Dublin.
[61]
A.F. Hayes, Introduction to mediation, D.A. Kenny (Ed.), Moderation, and conditional process analysis, Vol. 1, second ed., The Guilford Press, New York, 2018.
[62]
J. Henseler, C.M. Ringle, M. Sarstedt, A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the Academy of Marketing Science 43 (1) (2015) 115–135,.
[63]
Hirschlein, N., Meckenstock, J.-N., & Dremel, C. (2022). Towards Bridging the Gap Between BDA Challenges and BDA Capability: A Conceptual Synthesis Based on a Systematic Literature Review. https://doi.org/10.24251/HICSS.2022.748.
[64]
L. Hu, P. Bentler, Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling 6 (1) (1999) 1–55,.
[65]
C.D. Huang, Q. Hu, Achieving IT-business strategic alignment via enterprise-wide implementation of balanced scorecards, Information Systems Management 24 (2) (2007) 173–184,.
[66]
J.P. Isson, J. Harriott, Win with advanced business analytics, creating business value from your data, first ed., John Wiley & Sons, Inc, New Jersey, 2013.
[67]
G.R. Iversen, D.G. Kleinbaum, L.L. Kupper, K.E. Muller, Applied regression analysis and other multivariate methods, Journal of the American Statistical Association 84 (407) (1989) 839,.
[68]
D.L. Jackson, J.A. Gillaspy, R. Purc-stephenson, Reporting practices in confirmatory factor analysis: An overview and some recommendations, Psychological Methods 14 (1) (2009) 6–23,.
[69]
K.A. Jehn, A multimethod examination of the benefits and detriments of intragroup conflict, Administrative Science Quarterly 40 (2) (1995) 256–282,.
[70]
S. Jorfi, K. Md Nor, L. Najjar, H. Jorfi, The impact of IT flexibility on strategic alignment, International Journal of Business and Management 6 (8) (2011),.
[71]
S. Jorfi, K. Md Nor, L. Najjar, H. Jorfi, The impact of IT flexibility on strategic alignment (with Focus on Export), International Journal of Business and Management 6 (8) (2011) 264–270,.
[72]
Kalaiselvi, K. (2020). Big Data Analytics and Intelligence. In P. Tanwar, V. Jain, C.-M. Liu, & V. Goyal (Eds.), Big Data Analytics and Intelligence (pp. 1–16). https://doi.org/10.1108/978–1-83909–099-820201005.
[73]
K. Kambatla, G. Kollias, V. Kumar, A. Grama, Trends in big data analytics, Journal of Parallel and Distributed Computing 74 (7) (2014) 2561–2573,.
[74]
R. Kaplan, C.S. Chapman, A.G. Hopwood, M. D. B. T.-H. of M. A. R. Shields (Eds.), Conceptual Foundations of the Balanced Scorecard, Vol. 3, Handbook of Management Accounting Research, 2009, pp. 1253–1269,.
[75]
R. Kaplan, D. Norton, Strategic learning and the balanced scorecard, Strategy & Leadership 24 (5) (1996) 18–24,.
[76]
Kaplan, R., & Norton, D. (2004). Strategy Maps: Converting Intangible Assets Into Tangible Outcomes. Harvard Business Review Press.
[77]
R.S. Kaplan, D.P. Norton, The balanced scorecard, Gestion, Barcelona, 2002, p. 2000.
[78]
R.S. Kaplan, D.P. Norton, The execution premium, first ed., Harvard Business School Publishing Corporation, Ed.), Boston, 2008.
[79]
R. Kaplan, D. Norton, Tha balanced scorecard, first ed., Harvard Business School Press, Barcelona, 2012.
[80]
R. Kaplan, D. Norton, The Balanced Scorecard (3rd ed.; Gestión 2000, Ed.), Harvard Business Press, Barcelona, 2016.
[81]
G. Kearns, R. Sabherwal, Strategic alignment between business and information technology: A knowledge-based view of behaviors, outcome, and consequences, Journal of Management Information Systems 23 (2007) 129–162,.
[82]
G. Kim, B. Shin, O. Kwon, Investigating the value of sociomaterialism in conceptualizing IT capability of a firm, Journal of Management Information Systems 29 (3) (2012) 327–362,.
[83]
D. Kiron, P.K. Prentice, R.B. Ferguson, The analytics mandate, Mitosz Sloan Management Review 55 (4) (2014) 1. 〈http://sloanreview.mit.edu/analytics-mandate〉.
[84]
B. Kitchens, D. Dobolyi, J. Li, A. Abbasi, Advanced customer analytics: Strategic value through integration of relationship-oriented big data, Journal of Management Information Systems 35 (2) (2018) 540–574,.
[85]
R.B. Kline, Principles and practice of structural equation modeling, Guilford Press, New York, 2011.
[86]
S. Laumer, C. Maier, T. Weitzel, Information quality, user satisfaction, and the manifestation of workarounds: a qualitative and quantitative study of enterprise content management system users, European Journal of Information Systems 26 (4) (2017) 333–360,.
[87]
S. Lavalle, E. Lesser, R. Shockley, M. Hopkins, N. Kruschwitz, Big data, analytics and the path from insights to value, Mitosz Sloan Management Review 52 (2) (2011) 21–32,.
[88]
Linkedin. (2019). About LinkedIn. Retrieved July 29, 2019, from Official site website: https://about.linkedin.com/.
[89]
J. Luftman, K. Lyytinen, Z. Tal-ben, Enhancing the measurement of information technology (IT) business alignment and its influence on company performance, Journal of Information Technology 32 (2017) 26–46,.
[90]
K. Lyytinen, M. Newman, Explaining information systems change: A punctuated socio-technical change model, European Journal of Information Systems 17 (6) (2008) 589–613,.
[91]
Martinsons, M., Davison, R., & Tse, D. (1999). The balanced scorecard. A Foundation for the Strategic Management of Information Systems, 25(1), 71–88. Retrieved from http://www.scopus.com/inward/record.url?scp=0033076569&partnerID=8YFLogxK.
[92]
A. Mcafee, E. Brynjolfsson, Spotlight on big data big data: The management revolution, Harvard Business Review October (2012) 1–9.
[93]
McKinsey & Company. (2011). Big data: The next frontier for innovation, competition, and productivity. In McKinsey Global Institute. https://doi.org/10.1080/01443610903114527.
[94]
A. McWilliams, D. Siegel, Corporate social responsibility: A theory of the firm perspective, The Academy of Management Review 26 (1) (2001) 117–127,.
[95]
C. Meshing, How to avoid big data project failures, Integral Leadership Review 13 (4) (2013) 117–124.
[96]
P. Mikalef, J. Krogstie, I.O. Pappas, P. Pavlou, Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities, Information and Management 57 (2) (2020),.
[97]
K. Milis, R. Mercken, The use of the balanced scorecard for the evaluation of information and communication technology projects, International Journal of Project Management 22 (2) (2004) 87–97,.
[98]
M. Morales-Serazzi, O. Gonzalez-Benito, M. Martos-Partal, Achieving useful data analytics for marketing: Discrepancies in information quality for producers and users of information, BRQ Business Research Quarterly (2021),.
[99]
O. Müller, M. Fay, J. vom Brocke, The effect of big data and analytics on firm performance: An econometric analysis considering industry characteristics, Journal of Management Information Systems 35 (2) (2018) 488–509,.
[100]
R. Naidoo, A communicative-tension model of change-induced collective voluntary turnover in IT, Journal of Strategic Information Systems 25 (4) (2016) 277–298,.
[101]
Y. Niu, L. Ying, J. Yang, M. Bao, C.B. Sivaparthipan, Organizational business intelligence and decision making using big data analytics, Information Processing & Management 58 (6) (2021),.
[102]
J.C. Nunnally, Psychometric theory 3E, p. 752 Tata McGraw-Hill Education, 2010, p. 752.
[103]
T.D. Oesterreich, E. Anton, F. Teuteberg, Y.K. Dwivedi, The role of the social and technical factors in creating business value from big data analytics: A meta-analysis, Journal of Business Research 153 (2022) 128–149,.
[104]
Olenski, S. (2015). Big Data Solving Big Problems. Retrieved September 5, 2018, from Forbes website: https://www.forbes.com/sites/steveolenski/2015/03/19/big-data-solving-big-problems/#20cd00ce5b8e.
[105]
M. Peteraf, The cornerstones of competitive advantage: A resource‐based view, Strategic Management Journal 14 (1993) 179–191,.
[106]
S. Petter, W. DeLone, E. McLean, Information systems success: The quest for the independent variables, Journal of Management Information Systems 29 (4) (2013) 7–62,.
[107]
Phillips, A. (2016). IJMR-hosted debate:’Who will succeed in the new era of data discovery’. In The Market Research Society (Ed.), International Journal of Market Research (Vol. 58, pp. 473–484). https://doi.org/10.2501 /IJMR-2016–028.
[108]
A.A. Qaffas, A. Ilmudeen, N.K. Almazmomi, I.M. Alharbi, The impact of big data analytics talent capability on business intelligence infrastructure to achieve firm performance, Foresight (2022),.
[109]
Qualtrics. (2019). About Qualtrics. Retrieved July 29, 2019, from Official site website: https://www.qualtrics.com/es/research-core/.
[110]
S. Raghunathan, Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis, Decision Support Systems 26 (4) (1999) 275–286,.
[111]
Reggio, G., & Astesiano, E. (2020). Big-Data/Analytics Projects Failure: A Literature Review. 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), 246–255. https://doi.org/10.1109/SEAA51224.2020.00050.
[112]
B.H. Reich, I. Benbasat, Measuring the linkage between business and information technology objectives, MIS Quarterly 20 (1) (1996) 55–81,.
[113]
S.J. Ren, S. Fosso-Wamba, S. Akter, R. Dubey, S.J. Childe, Modelling quality dynamics, business value and firm performance in a big data analytics environment, International Journal of Production Research 7543 (April 2017) (2016) 1–16,.
[114]
M. Riesener, C. Dölle, G. Schuh, C. Tönnes, Framework for defining information quality based on data attributes within the digital shadow using LDA, Procedia CIRP 83 (2019) 304–310,.
[115]
D. Royer, M. Meints, Enterprise identity management – towards a decision support framework based on the balanced scorecard approach, Business & Information Systems Engineering 1 (3) (2009) 245–253,.
[116]
R. Sabherwal, S. Sabherwal, T. Havaknor, Z. Steelman, How does strategic alignment affect firm performance? The roles of information technology investment and environmental uncertainty, MIS Quarterly 43 (2019) 453–474,.
[117]
Sathi, A. (2014). Engaging customers using big data: how marketing analytics are transforming business (1st ed.). https://doi.org/10.1057/9781137386199.
[118]
T. Schmiedel, J. vom Brocke, J. Recker, Development and validation of an instrument to measure organizational cultures’ support of business process management, Information & Management 51 (1) (2014) 43–56,.
[119]
V. Sena, S. Bhaumik, A. Sengupta, M. Demirbag, Big data and performance: What can management research tell us, British Journal of Management 30 (2) (2019) 219–228,.
[120]
Shadish, W., Cook, T., & Campbell, T. (2002). Experiments and Generalized Causal lnference. In Experimental and quasi-experimental designs for generalized causal inference (pp. 1–81). https://doi.org/10.1198/jasa.2005.s22.
[121]
U. Sivarajah, M.M. Kamal, Z. Irani, V. Weerakkody, Critical analysis of Big Data challenges and analytical methods, Journal of Business Research 70 (2017) 263–286,.
[122]
S. Sleep, J. Hulland, Is big data driving cooperation in the c-suite? The evolving relationship between the chief marketing officer and chief information officer, Journal of Strategic Marketing 27 (8) (2019) 666–678,.
[123]
Softtek (2019). The 5 challenges facing Big Data. Retrieved November 15, 2022, from https://softtek.eu/corporate/los-5-desafios-a-los-que-se-enfrenta-el-big-data/.
[124]
Y. Sun, A. Jeyaraj, Information technology adoption and continuance: A longitudinal study of individuals’ behavioral intentions, Information & Management 50 (7) (2013) 457–465,.
[125]
S. Suoniemi, L. Meyer-Waarden, A. Munzel, A.R. Zablah, D. Straub, Big data and firm performance: The roles of market-directed capabilities and business strategy, Information & Management 57 (7) (2020),.
[126]
D. Teece, G. Pisano, A. Shuen, Dynamic capabilities and strategic management, Strategic Management Journal 18 (7) (1997) 509–533,.
[127]
R. Torres, A. Sidorova, M.C. Jones, Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective, Information and Management 55 (7) (2018) 822–839,.
[128]
Value, C., Analytics, B.D., & Verhoef, P.C. (2016). Creating Value with Big Data Analytics. https://doi.org/10.4324/9781315734750.
[129]
F. Van de Vijver, Y. Poortinga, Towards an integrated analysis of bias in cross-cultural assessment, European Journal of Psychological Assessment 13 (1997) 29–37,.
[130]
P. Verhoef, E. Kooge, N. Walk, Creating Value with Big Data Analytics: Making smarter marketing decisions, first ed., Routledge, New York, 2016.
[131]
J. Wang, Y. Guan, M. Atiquzzaman, N. Yen, Z. Xu (Eds.), Information Talent Training Mode Reform in the Era of Artificial Intelligence and Big Data BT - 2021 International Conference on Big Data Analytics for Cyber-Physical System in Smart City, Singapore: Springer Singapore, 2022.
[132]
X. Wang, L. White, X. Chen, Big data research for the knowledge economy: past, present, and future, Industrial Management & Data Systems 115 (9) (2015),.
[133]
Y. Wang, L. Kung, S. Gupta, S. Ozdemir, Leveraging big data analytics to improve quality of care in healthcare organizations: A configurational perspective, British Journal of Management 30 (2) (2019) 362–388,.
[134]
Z. Wenting, S. Brax, V. Mervi, R. Risto, The influences of contract structure, contracting process, and service complexity on supplier performance, International Journal of Operations & Production Management 39 (4) (2019) 525–549,.
[135]
D.A. Whetten, What constitutes a theoretical contribution, Academy of Management Review 14 (4) (1989) 490–495,.
[136]
White, A. (2019). Gartner Blog Network. Retrieved January 3, 2019, from Our Top Data and Analytics Predicts for 2019 website: https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/.
[137]
B.H. Wixom, P.A. Todd, A theoretical integration of user satisfaction and technology acceptance, Information Systems Research 16 (1) (2005) 85–102,.
[138]
B.H. Wixom, B. Yen, M. Relich, Maximizing value from business analytics, MIS Quarterly Executive 12 (2) (2013) 111–123,.
[139]
C. Xie, X. Xu, Y. Gong, J. Xiong, Big data analytics capability and business alignment for organizational agility: A fit perspective, Journal of Global Information Management 30 (1) (2022) 1–27,.
[140]
Z. Xu, G.L. Frankwick, E. Ramirez, Effects of big data analytics and traditional marketing analytics on new product success: A knowledge fusion perspective, Journal of Business Research 69 (5) (2016) 1562–1566,.
[141]
W.M.S. Yafooz, Z.B.A. Bakar, S.K.A. Fahad, A.M. Mithon, Business intelligence through big data analytics, data mining and machine learning, Advances in Intelligent Systems and Computing 1016 (2020) 217–230,.
[142]
C. Yang, C. Wu, Perception difference between users and information professionals: A case study of TaiPower, Proceedings of the American Society for Information Science and Technology 40 (3) (2005) 119–127,.
[143]
J. Zeng, K.W. Glaister, Value creation from big data: Looking inside the black box, Strategic Organization (2017) 1–36,.

Index Terms

  1. A new perspective of BDA and information quality from final users of information: A multiple study approach
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Please enable JavaScript to view thecomments powered by Disqus.

          Information & Contributors

          Information

          Published In

          cover image International Journal of Information Management: The Journal for Information Professionals
          International Journal of Information Management: The Journal for Information Professionals  Volume 73, Issue C
          Dec 2023
          183 pages

          Publisher

          Elsevier Science Publishers B. V.

          Netherlands

          Publication History

          Published: 01 December 2023

          Author Tags

          1. Big data analytics
          2. Organizational factors
          3. Strategic alignment
          4. Balanced scorecard
          5. Information quality

          Qualifiers

          • Research-article

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 0
            Total Downloads
          • Downloads (Last 12 months)0
          • Downloads (Last 6 weeks)0
          Reflects downloads up to 13 Dec 2024

          Other Metrics

          Citations

          View Options

          View options

          Media

          Figures

          Other

          Tables

          Share

          Share

          Share this Publication link

          Share on social media