Perceived information pollution: conceptualization, measurement, and nomological validity

Currently, employees are facing information explosion in the presence of disruptive information and communication technologies of industry 4.0. With the prevalent nature of information pollution, employees are suffering to process large volume of information in order to access quality information. The objective of present study is to develop a measurement scale of perceived information pollution in the context of workplace. Furthermore, this study aims to assess the nomological validity of the proposed construct.,This study has employed a sequential exploratory mixed-method design to develop and validate the measurement scale of perceived information pollution. The population of the present study comprised of the employees who work in the operations and credit department of banking sector. The present study has used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) to analyze data in AMOS.,The present study has developed the second-order measurement scale of perceived information pollution. The perceived information pollution comprises of five dimensions – accessible, intrinsic, contextual, representational, and distractive information pollution. This study has also confirmed the nomological validity of the information pollution in relation to employee's job satisfaction, work effort, and learning effort.,Management may employ the five dimensions as a benchmark in revealing polluted information as well as enhancing information quality through information processing.,This study has contributed to the literature of information management by providing a five-dimensional scale of perceived information pollution and confirming its nomological validity.

[1]  Pamela J. Wisniewski,et al.  When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity , 2010, Comput. Hum. Behav..

[2]  Dong Kyoon Yoo,et al.  Knowledge quality: Antecedents and consequence in project teams , 2011, J. Knowl. Manag..

[3]  Lila Rao-Graham,et al.  Towards defining dimensions of knowledge systems quality , 2007, Expert Syst. Appl..

[4]  P. Bentler,et al.  Cutoff criteria for fit indexes in covariance structure analysis : Conventional criteria versus new alternatives , 1999 .

[5]  Giovanni Schiuma,et al.  Knowledge, learning, and innovation: research and perspectives , 2013, J. Knowl. Manag..

[6]  Andrew K. Shenton,et al.  Information behaviour and information literacy: The ultimate in transdisciplinary phenomena? , 2011, J. Libr. Inf. Sci..

[7]  Frank W. Dewhurst,et al.  Linking chief knowledge officers with customer capital through knowledge management practices in the Spanish construction industry , 2010 .

[8]  Scott B. MacKenzie,et al.  Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.

[9]  Laura Maruster,et al.  Knowledge claim evaluation: a fundamental issue for knowledge management , 2010, J. Knowl. Manag..

[10]  Pradeep Kumar Ray,et al.  Towards an ontology for data quality in integrated chronic disease management: A realist review of the literature , 2013, Int. J. Medical Informatics.

[11]  Tung-Ching Lin,et al.  Understanding knowledge management system usage antecedents: An integration of social cognitive theory and task technology fit , 2008, Inf. Manag..

[12]  Mehwish Waheed,et al.  Knowledge quality , 2016 .

[13]  Ajay K. Kohli,et al.  Learning and Performance Orientation of Salespeople: The Role of Supervisors , 1998 .

[14]  Sheila Corrall,et al.  Developing librarians as teachers: A study of their pedagogical knowledge , 2010, J. Libr. Inf. Sci..

[15]  Iris Reychav,et al.  Information overload, psychological ill-being, and behavioral intention to continue online healthcare information search , 2017, Comput. Hum. Behav..

[16]  H. Gardner.  Creativity: An interdisciplinary perspective , 1988 .

[17]  Markus Helfert,et al.  An assessment technique for information quality support in emergency response , 2012 .

[18]  Yi-Shun Wang,et al.  A conceptual model for assessing blog-based learning system success in the context of business education , 2016 .

[19]  Peter M. Bentler,et al.  Structural Equation Modeling , 2003 .

[20]  Daniel Neagu,et al.  Assessing information quality of e-learning systems: a web mining approach , 2011, Comput. Hum. Behav..

[21]  Yunzhi Wang,et al.  Comparing digital libraries with social media from the dual route perspective , 2019, Online Inf. Rev..

[22]  W. Reinartz,et al.  An Empirical Comparison of the Efficacy of Covariance-Based and Variance-Based SEM , 2009 .

[23]  Sheila Corrall,et al.  Information literacy strategy development in higher education: An exploratory study , 2008, Int. J. Inf. Manag..

[24]  Anne Morris,et al.  The problem of information overload in business organisations: a review of the literature , 2000, Int. J. Inf. Manag..

[25]  Stuart E. Madnick,et al.  Overview and Framework for Data and Information Quality Research , 2009, JDIQ.

[26]  Mark A. Runco,et al.  The psychoeconomic approach to creativity , 1992 .

[27]  N. Ahmad,et al.  The assessment of perceived information pollution in banking sector , 2018 .

[28]  Graham Coult.  MANAGING THE 'INFORMATION POLLUTION' , 2008 .

[29]  Sylvain Senecal,et al.  Communicating online information via streaming video: the role of user goal , 2017, Online Inf. Rev..

[30]  Rashid Nawaz,et al.  A multi-dimensional construct of perceived information pollution in the era of rife infollution , 2019, VINE Journal of Information and Knowledge Management Systems.

[31]  Gary Oddou,et al.  Repatriating knowledge: variables influencing the “transfer” process , 2009 .

[32]  T. D. Plessis,et al.  Effect of online social networking on employee productivity , 2009 .

[33]  Bahaudin G. Mujtaba,et al.  The Business School in the 21st Century and Beyond: Integrating a Knowledge Management Philosophy , 2009 .

[34]  Jing Zhou.  Feedback valence, feedback style, task autonomy, and achievement orientation: Interactive effects on creative performance. , 1998 .

[35]  Hajar Mousannif,et al.  Data quality in internet of things: A state-of-the-art survey , 2016, J. Netw. Comput. Appl..

[36]  Narasimhaiah Gorla,et al.  Organizational impact of system quality, information quality, and service quality , 2010, J. Strateg. Inf. Syst..

[37]  NoelThomas,et al.  Data quality in internet of things , 2016 .

[38]  Francisco-Javier García-Marco,et al.  Libraries in the digital ecology: reflections and trends , 2011 .

[39]  Miguel A. Rodríguez,et al.  Internet vs. travel agencies on pre-visit destination image formation: An information processing view , 2008 .

[40]  Linda C. Smith,et al.  Information quality work organization in wikipedia , 2008 .