Senjie Bao Hanson Lee

Abstract

Data warehousing has been widely used in organizations to support management decision making. Successful data warehousing projects can bring valuable information to organizations; however, there are large portions of data warehousing projects failed. Previous research suggests several factors are crucial to a data warehousing project success. This paper examined several most commonly discussed critical success factors affecting two key dimensions of data warehousing success which are data quality and system quality. Based on the previous discussion on data warehousing success factors, this paper proposed a data warehousing success factors model to map the success factors to data warehousing success. Finally, this study finds that strong data governance together with other technical factors have a strong influence on data quality while system quality is affected by several types of success factors.

Keywords

Data warehousing, success factors, data quality, system quality.

INTRODUCTION

A data warehouse is an integrated data repository specially designed and created to support all levels of management decision making process (March and Hevner 2005). Its data can be extracted from various internal or external existing systems with different formats and transformed, cleaned, aggregated before stored in the data warehouse (Wixom and Watson 2001). Data warehouse was emerged as a platform to integrate data from different sources to support decision making (Shin 2003) and helps to improve business performance including discover the most favoured product, identify key customers for organization’s business, and improve operational efficiency (Cooper et al. 2000). It has been used to deliver useful information for managers for decision making since early 1990s (Shim et al. 2002) and become more and more crucial to businesses as the market is being more global and sophisticated, and customers are becoming more informed. Organizations in this increasingly competitive and volatile business context need to acquire more information to make right decisions. Thus, they have used data warehouses to help to conduct various tasks such as customer service and target marketing (Shin 2003). With the help from recent technologies such as cloud computing and Hadoop platform, the analytical capabilities of data warehousing have been significantly improved and provide real time analysis results for large volume of data. Data warehouse is changing the way organizations conduct business by providing information draw from data especially in sales and marketing (Wixom and Watson 2001).

Data warehouse can bring large benefits to organizations through effective business intelligence; however, having a data warehouse in house does not guarantee a success. The process of building a data warehouse is costly and risky (Jukic 2006). A survey reported that nearly two thirds of companies having a data warehouse rated their data warehouse successfully meeting business expectations (Stedman 1998). A more recent study shows the failure rate of a data warehouse project is around half to three quarters (Hwang and Xu 2008). The reason to this high failure rate varies from company to company, but previous research has found several common reasons. Watson and Haley (1997) found the data warehouses at that time were unable to provide users easy access to timely and high quality data. Watson, Gerard, Gonzalez, Haywood, and Fenton (1999) also found lack of top management support, sponsorship, and end user involvements are most common reasons for data warehouse project failure.

Similar to other information system projects, a number of factors can affect the success of a data warehouse project. Wixom and Watson (2001) developed a model to evaluate and measure several success factors identified by empirical studies. They analysed three key dimensions of data warehouse success namely data quality, system quality, and perceived net profits. Their analysis result shows data quality and system quality are associated with perceived net profits, and several factors contribute to system quality (Wixom and Watson 2001). However, they found the success factors they studied in their survey do not have significant association with data quality which suggests the factors affecting data quality are not among the factors they researched and further research should be conducted to discover factors that affecting data quality.

Based on their research, this paper examines key factors affecting two key dimensions of data warehouse success (data quality and system quality) from a wider range of factors in order to identify critical success factors for both dimensions. As Wixom and Watson’s research has clearly pointed out the association between data quality, system quality and net benefit, this paper will not focus on the relationship among them. Instead, this paper will open the success factors affecting data quality and system quality to a broader range and identify key factors for data quality which is not identified in Wixom and Watson’s paper based on literature research.

Data Quality

Data Quality Definition

Data quality is also named information quality by some researchers (Nelson et al. 2005). It is frequently discussed in previous data warehouse academic papers and also the fundamental of building a valuable data warehouse (Watson and Haley 1997). Mahanti (2014) defines data quality as the capability of the data available in the data warehouse to meet the business requirements which can be explained as a fitness between data and the given business context. Nelson, Todd, and Wixom (2005) summarised two views on data quality: an intrinsic view which considers data quality on its value itself and a context-based view which considers data quality on its degree on helping end user to complete a task. Therefore, data quality is closely associated with end user net benefits and the ability that system can improve the user’s work performance (Shin 2003).

Data Quality Dimensions

Researchers have developed a few metrics to measure data quality from both intrinsic and contextual view (Nelson et al. 2005). Mahanti (2014) raised 7 dimensions to measure data quality including completeness, conformity, consistency, accuracy, duplication, integrity, and timeliness. However, based on Wang and Strong’s (1996) research, the dimensions can be reduced to few key determinant dimensions such as accuracy, completeness, timely, and format (Nelson et al. 2005). For each of them, the assessment should be tied to specific context that the end user will use the data to perform tasks.

Accuracy

The correctness to map the real world information to the information stored in the data warehouse. The information provided by the data warehouse should be correct, meaningful, consistent, clear, objective and believable.

Completeness

Completeness is the extent of all possible states of real world objectives are captured and stored in the data warehouse. The assessment of this dimension should be based on the information user. The data should be considered as complete as long as the information is enough to support the user’s decision making.

Timely

Timely refers to the information stored in the data warehouse should be up to date or precisely reflects the real world. Its assessment should be based on different purpose and highly depend on the task and user perceptions.

Format

Format means the way of presenting the information is understandable and interpretable. Research results show that the way of presenting information is highly contingent on user’s mental model and the task (Vessey 1991). Therefore, its assessment should be based on the perception of user for different tasks.

Success Factors for Data Quality

Wixom and Watson (2001) build a research model to discover 7 critical success factors’ influence on data warehousing success and how these factors affecting data warehousing success through organizational implementation success, project implementation success, and technical implementation success. These factors are identified by previous research including management support, champion, resources, user participation, team skills, source systems, and development technology. However, through the regression analysis on data collected from 111 organizations, they found the R² value for the factors covered in their research model was 0.016 which mean these factors are not crucial to data quality.

Chenoweth, Corral and Demirkan (2006) provide a new insight into data warehouse success. They provided a case study on a large organization which implemented a data warehouse with some successful units and some failed units and identified 7 key interventions for data warehouse success based on adaptive structuration theory. However, each of the interventions identified in their research can be linked to a success factor in other success factor literature. The seven interventions are: top management support, user championship, data completeness, user accessibility, functional fit, training & education, resources support.

Mahanti (2014) summarised 10 most commonly discussed critical success factors with 35 attributes of those factors for data quality derived from both academic and practitioners. The 10 critical success factors are: leadership and top management commitment, organizational infrastructure, culture change, training and education, strong data governance, teamwork, business user involvement, use of data profiling tools, project prioritization and selection, and documentation. The survey result shows the top 5 factors are: strong data governance, teamwork, culture change, documentation, and organizational infrastructure (Mahanti 2014). This result is reasonable as strong data governance can ensure the data warehouse aligned to the business context and business requirements.

Hwang and Xu (2008) group previous identified success factors into 4 categories as operational, technical, schedule, and economic factors. They found that higher quality data can provide higher quality information and bring more benefits to user. Finally, the users’ individual benefits can bring benefits to the organization. Their survey result shows the technical factors are more influential on data quality and no association on system quality. This result does not consistent with Wixom and Waston’s (2001) research. Hwang and Xu (2008) believed this is due to the actual factors included in this category.

System Quality

System Quality Definition

Beside data quality, system quality is another critical success factor to achieve net benefits of data warehousing (Wixom & Watson 2001). System quality is associated with the performance of the systems that produce information. How system quality is defined and measured is a question. It is stated in a literature that system quality can be defined and measured through operational measures of ease of use (Rai et al. 2002). Similarly, Davis (1989) addressed that the factors of system quality are closely related with user perceptions of interaction with the system and therefore high quality systems should be perceived as easier to use.

System Quality Dimensions

Nelson, Todd and Wixom (2005) studied critical success factors for data warehousing in a more system perspective. They started from an assumption that there are unique system dimensions that acts as determinants to system quality. The study therefore focused on 5 key system dimensions that are assumed to indirectly influence user satisfaction with the system quality in regards to usage of three key business intelligence tools (analysis, query and predefined report). The 5 Key dimensions that were analysed in this study are:

Accessibility – The degree to which a system and the information can be accessed with relatively low effort.
Reliability – The dependability of a system over time.
Response time – The degree to which a system offers quick (or timely) responses to requests for information or action.
Flexibility – The degree to which a system can adapt to a variety of user needs and to changing conditions.
Integration – The degree to which a system facilitates the combination of information from various sources to support business decisions.

Throughout their study, it was found that reliability is a key system dimension and is the most influential determinant to system quality. Accessibility and flexibility are, in the same magnitude, the next influential determinants. Integration also appears to have an influence across the three tools, as one of the main purposes of data warehousing is to integrate data sourced from various systems. However, it is appeared that response time was not significant for the three tools. It is interpreted that response time is not a critical determinant to the user satisfaction with system quality, as data warehouses studied in this paper were not typically used for an ongoing, real-time information process.

Success Factors for System Quality

Wixom and Watson (2001) studied critical success factors of data warehousing by analysing several key factors known to be related to three key aspects of implementation success that are assumed to influence quality of data warehouse. The key factors and three aspects of implementation success analysed by Wixom and Watson are:

Organisational implementation success – Management support, Champion, Resource, User participation
Project implementation success – Champion, Resources, user participation, team skills
Technical implementation success – Team Skills, Source Systems, and Development technology

Wixom and Watson (2001) found that not every aspects of implementation success have significant effects on system quality. Their study suggested that only organisational and project implementation aspects have significant effects on system quality while technical implementation aspect does not. It is interpreted that it will be much easier for an organisation to build a flexible and integrated data warehouse when organisational issues are effectively removed and a well-managed team is responsible for the project.

Hwang and Xu’s academic research paper (2008) on critical success factors for data warehousing is similar to Wixom and Watson’s study (2001) discussed above in that it focused on similar factors although their approach was slightly different. They analysed factors by grouping them into four key aspects. The 4 aspects of the key factors analysed in this paper are:

Operational factor – Clearly defined business needs, top management support, user participation
Technical factor – Source data quality, proper development technology, adequate IS staff and consultant, project management
Schedule factor – Practical implementation scheduling, proper planning and scoping project
Economic factor – Adequate funding, measurable business benefits

Although Hwang and Xu’s study (2008) focused on similar factors to Wixom and Watson’s study (2001), the result turned out to be different. It was concluded that operational and economic factors have significant influences on system quality, as those factors are enablers for achieving quality data through data warehouse. The influence of technical factor was not significant in system quality while interestingly was it in data quality. One of the key findings of Hwang and Xu’s study (2008) was that system quality is a factor that has a positive influence on information quality while, in Wixom and Watson’s study (2001), such a cross effect between data quality and system quality was not deeply nor significantly supported.

Key findings & Implications

Based on our investigation, we combined the results of the academic research papers we studied and created a critical success factor model as shown in the Figure 1. Then, we have identified four key findings in relation to key factors and the relationships of those with quality of data warehouse.

Figure1: Data Warehousing Critical Success Factor Model — Figure 1: Data Warehousing Critical Success Factor Model

The first key finding is that, given that data quality and system quality are the primary factors of the success of data warehousing, the key factors that affect system quality and information quality are quite different. Wixom and Watson’s study (2001) suggested that the three key aspects of implementation success have significant associations with system quality only. Other academic research papers we studied such as Hwang and Xu’s study (2008) also suggested that operational

and economic factors are only significantly associated with system quality while technical quality is associated only with information quality. The figure 1 mapped those relationships to show how each of the key factors are related with data quality and system quality of data warehouse.

Secondly, we found that technical factors do have more significant influence on data quality than the system quality, while organisational and operational factors do have more significant influence on system quality. The academic research papers we investigated had a hypothesis that technical factors are associated with system quality as system quality should be determined by technical performance. However, the results of the papers suggested that technical factors actually do not have strong relationship with system quality. Rather, organisational and operational factors such as management support and governance are significantly related with system quality. The technical factors are more associated with information quality than system quality because the most significant measure of data quality such as accessibility, completeness, timeliness and format are the aspects that can be significantly affected by technical issues, especially in transformation process of the source data.

The third point is that the success of data warehousing is determined by user perception and satisfaction of interaction with the data warehouse, and therefore the types of critical success factors may vary in dependence upon users’ circumstances and their purposes of use the data warehouse. The study of Nelson, Todd and Wixom (2005) showed that the degree of the association between critical success factors and the quality of data warehouse varies in difference of their usage of data warehouse, such as analysis, query and predefined report.

Finally, it is found that system quality has a strong influence on data quality and data quality is the most important factor of individual benefits which lead to organisational benefits. The result of Hwang and Xu’s study (2008) suggested that system quality has a significant association with information quality. It makes sense because data is the main product of the data warehouse system. This is an important point as it indicates that the success of the data warehousing can be achieved only when the basic benefits of it are realised.

Limitations

The model we build in this study is based on previous research literatures. Some views from those data warehousing literatures are not consistent with each other. For example, Wixom and Watson (2001) found technical factors do not affect data quality while Hwang and Xu (2008) found technical factors have significant influence on data quality. This controversial result may due to different research methodologies adopted by academics as mentioned in Hwang and Hu’s (2008) study that the classification of technical factors is different. Other reason may due to the different business context has changed over time. Our study makes adjustments on those conflicting results to build the research model; however, due to limited resource and time, this model has not been verified by quantitative analysis. Thus, further research should be conducted to verify the success factors identified in our model.

Conclusion

This paper studied and discussed critical success factors of data warehousing by investigating factors that were identified and analysed to find association with the success of data warehousing in several academic research papers. The primary goal of this study was to understand relationships between the keys factors and the success of data warehousing. As suggested in many research papers, data quality and system quality are the primary factors to the success of data warehousing. Therefore we initially focused on finding key factors that have strong influences on data quality and system quality. Then we focused on finding relationships between the factors and the quality of data and systems. Although each of the academic research papers has slightly different discussions and conclusions, we analysed the relationships by combining common aspects of those papers, and have come out with some key findings as discussed in the above section, which we believe will be useful to understand how and why data warehousing can succeed and fail.

REFERENCES

Chenoweth, T., Corral, K. and Demirkan, H. 2006, “Seven Key Interventions for Data Warehouse Success,” Communications of ACM (49:1), January, pp 114-119.

Cooper, B.L., Watson, H.J., Wixom, B.H., and Goodhue, D.L. 2000. “Data Warehousing Supports Corporate Strategy at First American Corporation,” MIS Quarterly (24:4), pp 547-567.

Davis, F.D. 1989. “Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology,” MIS Quarterly (13:3), September, pp 319-340.

Hwang, M.I., and Xu, H. 2008. “A Structural Model of Data Warehousing Success,” Journal of computer information systems, Fall, pp 48-56.

Jukic, 2006. “Modeling Strategies and Alternatives for Data Warehousing Projects,” Communications of the ACM (49:4), pp 83-99.

Mahanti, R. 2014. “Critical Success Factors for Implementing Data Profiling: The First Step Toward Data Quality,” Software Quality Professional (16:2), March, pp 13-26.

March, S.T., and Hevner, A.R. 2005. “Integrated Decision Support System: A Data Warehousing Perspective,” Decision Support Systems, vol. 43, pp 1031-1043.

Nelson, R.R., Todd, P.A., and Wixom, B.H. 2005. “Antecedents of Information and System Quality: An Empirical Examination Within the Context of Data Warehousing,” Journal of Management Information Systems (21:4), pp 199-235.

Rai, A., Lang, S.S., Welker, R.B. 2002, “Assessing the Validity of IS Success Models: An Empirical Test and Theoretical Analysis,” Information Systems Research (13:1), pp 50-69.

Shim, J.P., Warkentin, M., Courtney, J.F., Power, D.J., Sharda, R., and Carlsson, C. 2002. “Past, Present, and Future of Decision Support Technology,” Decision Support Systems (33:2), pp 111-126.

Shin, B. 2003. “An Exploratory Investigation of System Success Factors in Data Warehousing,” Journal of the Association for Information Systems, Vol. 4, pp 141-170.

Stedman, C. 1998. “Warehousing Projects Hard to Finish,” Computerworld (32:12), p 29.

Vessey, I. 1991. “Cognitive Fit: A Theory-based Analysis of The Graphs Versus Tables Literature,” Decision Sciences (22: 2), pp 219-240.

Wang, R.Y., and Strong, D.M. 1996. “Beyond accuracy: What Data Quality Means to Data Consumers,” Journal of Management Information Systems (12:4), Spring, pp 5-34.

Watson, H.J., and Haley, B.J. 1997. “Data Warehousing: A Framework and Survey of Current Practices,” Journal of Data Warehousing (2:1), pp 10-17.

Watson, H.J., Gerard, J.G., Gonzalez, L.E., Haywood, M.E., and Fenton, D. 1999. “Data Warehousing Failures: Case Studies and Findings,” Journal of Data Warehousing (4:1), pp. 44-55.

Wixom, B.H., and Watson, H.J. 2001. “An Empirical Investigation of The Factors Affecting Data Warehousing Success,” MIS Quarterly (25:1), March, pp 17-41.

05/04/2015

Easter lunar eclipse in Melbourne

20/03/201505/04/2015

As the night falls…

22/02/201505/04/2015

Melbourne White Night 2015

11/12/201411/12/2014

Online Copyright Infringement – What should we do?

Senjie Bao

The University of Melbourne

Keywords

Background

On 20 November 2008, the Australian Federation Against Copyright Theft (AFACT) representing 34 film companies including Universal Pictures, Sony Pictures Entertainment, Village Roadshow, Warner Bros Entertainment, the Seven Networks, Twentieth Century Fox Film Corporation, and others commenced a lawsuit against one of Australia’s largest internet service providers (ISPs), iiNet, in Federal Court of Australia. AFACT alleged that iiNet had authorized its users to download illegal movies through peer-to-peer network (BitTorrent) and thus should be liable for the copyright infringement.

AFAC had conducted investigations into copyright infringement and sent notices to iiNet from July 2008. The notices did not include AFACT’s detection methodology but requested iiNet to warn, suspend or terminate the internet service of its users to stop the copyright infringement. iiNet did not ast as requested and claiming that it is not iiNet’s responsibility to take actions based on AFACT’s allegations and the offence should have been proven in the courts before iiNet can disconnect its customer’s internet.

This case was finally taken to the High Court by AFACT after iiNet won the lawsuit in the Federal Court. However, the High Court made its decision on 20 April 2012 in favor of iiNet and dismissed AFACT’s appeal. The High Court held that the power that iiNet had to prevent its customer from copyright infringement was indirect as iiNet had no control of torrents file sharing. In addition, the High Court also considered the notice sent by AFACT which did not include its methodology was not sufficient for iiNet to take actions.

ISP’s Responsibility

As a consequence of iiNet’s winning, copyright holders will now hard to prove ISPs are liable for their customers’ copyright infringements which make it more difficult for copyright holders to protect their rights. This raises the issue of whether ISPs should be liable for their users’ copyright infringement.

Copyright Enforcement

From ISPs’ perspective, making service providers responsible for their actual users’ behavior is not fair for them and this argument is generally supported by courts (Kidman 2011). In iiNet’s case, the core factors contribute to iiNet’s winning is its indirect power to prevent the copyright infringement as well as AFACT’s insufficient information on the notices which made the High Court held that it was not unreasonable for iiNet to act as it did (Bushby & Webb 2012). The High Court made this decision based on subsection 101(1A) of the Copyright Act 1968 which determines the authorization liability (Swinn 2012). However, the Chief Justice French, Justices Crennan and Kiefel also noted that this provision was not ready to suit to BitTorrent copyright infringement and needs to be changed (Webb 2012).

Australian ISPs started their engagement in online copyright infringement prevention in 2011 when they were facing ongoing push from the filming industry (Kidman 2011). Five major Australian ISPs including Telstra, Optus, iiNet, Primus, and Internode proposed an online copyright enforcement scheme through Communication Alliance. Very similar to Europe’s “three strike” approach, the scheme was education-based that ISPs will send education and warning notice to their customer who downloads unauthorized files from internet and being detected by copyright owners. Australia’s ISPs agreed on this scheme and started an 18 month trial in 2011 (Colley 2011). However, under this scheme, ISPs will not impose any sanctions against their customers or disconnect their internet access.

Legislative Reform

A lawsuit against ISPs is not an effective way but expensive way for copyright owners to protect their rights (Muir 2012). In iiNet’s case, AFACT was ordered by the High Court to pay iiNet’s legal expenses which was said to be approximately 9 million (iiNet Press 2012). Traditional legal action against individuals is also inefficient and ineffective due to large number of infringers and technically sophisticated internet users.

Thus, copyright owners now turn to government to introduce new policy to force ISPs to be more active in online copyright infringement prevention. Some countries such as UK, New Zealand, and France, are in the process of implementing or have implemented a statutory approach through amending the law. Australian government has also recently released a “Online Copyright Infringement Discussion Paper” which outlines 3 proposals to amend the Copyright Act 1968 (Webb 2014).

Propose 1 – Extended Authorization Liability. This amendment is directly derived from iiNet’s case and proposing the court need also taking ISPs’ reasonable steps to prevent online copyright infringement into consideration as well as their power.
Propose 2 – Extended Injunctive Relief. This amendment will enable copyright owners to request ISPs to block infringement websites outside Australia.
Propose 3 – Extended Safe Harbor Scheme. This amendment extends the application of the safe harbor scheme from carriage provider to all service providers.

Balancing Intellectual Property and Other Rights

The government discussion paper shows policy-maker’s effort to support Australia’s copyright industries. It is reasonable for copyright owners to seek government to improve the situation in this digital world. However, a balance between protecting copyrights and basic human rights, such as privacy, should also be taken into consideration (Muir 2012). Muir (2012) points out that the copyright owner’s ability to enforce their rights and Internet user’s rights including privacy, access to Internet and freedom of speech, due process should be balanced.

In Telstra’s response to the government’s discussion paper, it is stated that the extension of authorization liability will significantly shift the balance between copyright holders, Internet users and ISPs’ competing interest which copyright law intend to strike thus is not appropriate (Telstra 2014).

In an Online Copyright Infringement Forum held by Communications Minister Malcolm Turnbull in Sydney also finds out that accessibility, timely, and price of the copyrighted in Australia are also factors contribute to online piracy (Law 2014).

Implications and Conclusion

Traditional remedies for online copyright infringement are hard to address the issue while the more recent approach such as “three strikes” and blocking may also breaking the balance of basic human rights as well as competing interests between all parties (Kiskis 2013). The challenge to implement copyright enforcement scheme is to balance the copyright industry, ISPs, and public interest. Any copyright enforcement measures that are not compatible with human rights may not be successful in the long term (Muir 2012).

(Words count: 1021)

References

Bushby, D., Webb, T. (2012). iiNet wins in high court. Retrieved from http://www.claytonutz.com/publications/videos/iinet_wins_in_high_court_tim_webb.page

Colley, A. (2011). ISPs agree to online copyright enforcement plan. The Australian. Retrieved from http://www.theaustralian.com.au/technology/isps-agree-to-copyright-online-enforcement-plan/story-fn4htb9o-1226206441917

iiNet Press. (2012). iiNet wins landmark copyright case. Retrieved from http://www.iinet.net.au/about/mediacentre/releases/20120420-iiNet-wins-landmark-copyright-case.html

Kidman, A. (2011). Aussie ISPs propose copyright enforcement scheme. Lifehacker. Retrieved from http://www.lifehacker.com.au/2011/11/aussie-isps-propose-copyright-enforcement-scheme/

Kiskis, M. (2013). Novel remedies for intellectual property rights infringement online. Jurisprudence 20(4), 1443-1456. doi: 10.13165/JUR-13-20-4-09.

Law, J. (2014). Online Copyright Infringement forum: can anything stop our nation of pirates. Retrieved from http://www.news.com.au/technology/online/online-copyright-infringement-forum-can-anything-stop-our-nation-of-pirates/story-fnjwneld-1227054171162

Muir, A. (2012). Online copyright enforcement by Internet Service Providers. Journal of Information Science 39 (2), 256-269. doi:10.1177/0165551512463992.

Swinn, M. (2012). Roadshow films v iiNet: the high court of Australia holds that an ISP is not liable for the online copyright infringement. [web log post]. Retrieved from http://www.corrs.com.au/publications/ip-preview/roadshow-films-v-iinet/

Telstra. (2014). Submission to the Attorney-General’s Department Discussion Paper ‘Online Copyright Infringement’. Retrieved from http://www.ag.gov.au/Consultations/Documents/OnlineCopyrightInfringement/OnlineCopyrightInfringement-TelstraResponse.pdf

Webb, T. (2012). iiNet’s High Court win means ISPs must still tread carefully on copyright infringement. [web log post]. Retrieved from http://www.claytonutz.com/publications/news/201204/20/iinets_high_court_win_means_isps_must_still_tread_carefully_on_copyright_infringement.page

Webb, T. (2014). Australian government seeks new ways to limit online copyright infringement in the new discussion paper. Retrieved from http://www.claytonutz.com/publications/edition/07_august_2014/20140807/australian_government_seeks_new_ways_to_limit_online_copyright_infringement_in_new_discussion_paper.page

11/12/201411/12/2014

Internet Privacy Concerns Literature Review

Senjie Bao

The University of Melbourne

Abstract

Internet has a huge impact on people’s life; however, internet also raises the issue of information privacy issue. This paper examines the impact of internet on its users’ information privacy and what are the major concerns as well as how internet users response to those concerns. As a conclusion, this paper believes internet has invaded users’ privacy and suggests few research areas to provide better understanding of internet privacy concerns and its relationship with user’s response, thus online companies can have better privacy practices.

Keywords

information privacy; internet privacy concerns; online privacy; privacy protection

Introduction

Information privacy is defined as individual’s ability to control his or her personal information (Westin 1967) and is one of the most discussed issues in the digital age (Culnan and Bies 2003). The advances in information technologies enable companies to provide personalized information and services to consumers based on the consumer’s personal information. On one hand, those personalized technologies can provide consumers much better user experience in using information systems and applications (Toch et al. 2012); however, on the other hand, consumers’ information privacy becomes more vulnerable (Hong and Thong 2013) and the need to collect consumers’ private information becomes a threat to consumer information privacy and may have negative impact on internet usage growth (Dinev & Hart, 2005).

Researchers have found that trust is one of the most important factors affecting consumers’ online purchasing behavior (Dinew & Hart, 2005) and information privacy is another greatest factor influencing the growth of electronic commerce (Son & Kim, 2008). However, internet users’ privacy has been seriously threatened according to several reports. A survey published on BusinessWeek (2000) shows a quarter Americans consider their information privacy was invaded. Another research shows more than three quarters Americans consider information collected by companies about them was actually very important information, but they do not have any form of control on that information which was collected and used by companies (Dinev & Hart 2005).

This paper examines several information systems papers on information privacy concerns and internet user’s privacy protective response to have a better understanding of internet technology’s impact on individuals’ information privacy. The first question this paper trying to answer is “what is privacy”. Given the concept of privacy has existed for more than 100 years; the conceptualization of privacy is still not consistent in academic literatures. Thus, the first question will explore the definitions of privacy and multi dimensions information privacy. The second question in this paper is “what are internet privacy concerns”. In this question, this paper trying to find out what is information privacy concerns in the context of internet environment and the dimensions if internet privacy concerns. The final question this paper trying to answer is “how internet users response to information privacy threats”. A better understanding of internet privacy concerns and how internet users respond to privacy threats can help online companies implement a better consumer privacy strategy which is critical to success in this information age.

Q1: What is privacy? – Multidimensional Definition

The concept of privacy has existed for more than a century in almost all disciplines of social science (Smith et al. 2011). However, academics have not reached a common definition of what is privacy even though numerous attempts have been made to combine all perspectives together. Smith et al. (2011) classified the definitions of privacy from different disciplines into two main categories namely valued-based and cognate-based. The value-based definition considers privacy as part of human rights and was the first definition of privacy. Under this category, privacy is also viewed as commodity when the privacy paradox was noted after applying this concept to consumer behavior. Privacy as commodity view considers privacy not purely as a human right but also an economic factor which can be calculated. The other category of definition of privacy, cognate-based conceptualization, was often used by psychologists focusing on individual’s perceptions and cognition instead of human rights. Under this category, privacy has been defined as a state by Westin (1967) with four sub states respectively intimacy, solitude, anonymity and reserve. Another cognate-based view considers privacy as control and has been developed by information systems researchers (Smith et al. 2011). In addition, Smith et al. (2011) points out that the general privacy concept includes both physical privacy and information privacy. Different from physical privacy which concerns about physical access to one’s surroundings and private places, information privacy concerns about access to one’s personal information.

The conceptualization and meaning of privacy are also dependent on the context. Acquisti (2004) argues that privacy should be defined as a category of multiple perspectives rather than a single concept and its value may different in different contexts. Basal et al. (2008) refers the context to the research discipline, when, where, who, with whom, why which may all affect the meaning of privacy. Based on this, Smith, Dinev and Xu (2011) summarize context types as (1) contextual sensitivity; (2) industry; (3) political context; and (4) technological applications.

In the context of information systems, Belanger and Crossler (2011) reviewed multiple definitions of information privacy in information systems research literature concluding that information privacy typically defined as control over one’s personal information especially the second use of the information.

Besides privacy’s multidimensional attributes, Smith et al. (2011) also argue that privacy is not anonymity, secrecy, confidentiality, security, and ethics. Refer to both Smith et al.’s research and Belanger and Crossler’s research, Pavlou (2011) points out that Belanger and Crossler base their research on a specific definition from information system discipline while Smith et al. focus on developing a cross disciplines definition of information privacy. Nonetheless, both papers are consistent with information systems literatures’ conceptualization of information privacy. However, Pavlou (2011) also noticed the conceptualization of information privacy was not a future research direction which implies there is little work can do to refine the definition of information privacy or the conceptualization of information may be difficult to reach a consensus.

Q2: What are the information privacy concerns about internet? – Internet privacy concerns

Information privacy concerns

Information privacy concerns are one of the most important research areas on privacy. Researchers usually try to explain different levels of information privacy concerns or to identify the impact of information concerns on several attributes such as the willingness to purchase online or provide personal information (Belanger & Crossler, 2011). Some academics define information privacy concerns as individuals’ concerns about organizations’ information privacy practices (Smith et al. 1996). Researchers have found that information privacy concerns can have influence on individuals’ decision making and preferences or willingness to provide personal identifiable information (Milberg et al. 2000) as well as individual’s willingness to adopt certain technologies such as using internet purchase.

Most researches on information privacy concerns focusing on two main streams: general information privacy concerns and internet privacy concerns (Belanger & Crossler, 2011). General information concerns were discovered before internet privacy concerns and have four dimensions, specifically, collection of data, errors in data, improper use of data, and unauthorized second use of personal information (Smith et al. 1996). The internet privacy concerns were developed few years later than general information privacy concerns and contain three dimensions namely data collection, information control, and privacy awareness (Malhotra et al. 2004). The later developed internet privacy concerns are more focusing on individuals’ willingness to transact but been less referenced in majority research as most researches are more related to initial information privacy issues rather than in the context of internet.

Internet Privacy Concerns

Internet privacy concerns (IPC) are a subclass of information privacy concerns and are representations of individuals’ perception on the personal information they provide through internet (Dinev & Hart 2006). Internet users now are having more knowledge on information technology and becoming more aware of their privacy on the internet. A report shows only 6 percent of Americans trust the websites could process their personal information and protect their data securely (Carroll 2002) while the internet is becoming a much more significant channel for companies to collect and transmit consumer personal information. Therefore, a better understanding of internet users’ information concerns is one of the fundamental factors to success in this information age.

There are many researchers attempted to conceptualize IPC; however, the conceptualizations of IPC are not consistent and the definitions and operationalization of the first-order factors are barely agreed (Hong & Thong 2013). For example, similar dimensions of IPCs can be defined and named differently from case to case. In addition, the measurement for IPC and information privacy concerns is significantly different (Hong & Thong 2013). The differences in measurement may bring difficulties to consolidate prior research findings. Thus, it is critical to develop a consistent perspective in measuring IPC in future research to resolve inconsistency and consolidate findings of prior research.

In a recent research, Hong and Thong (2013) conclude the dimensions of IPC as data collection, secondary use of personal information, information errors, unauthorized access to personal information, control over one’s personal information, and awareness of companies’ information privacy practices. Other relevant concerns but not directly related to IPC include individuals’ fear of being monitored or tracked when browsing the internet, identity issues, legal issues, application issues, and security issues.

Q3: How internet users respond to IPCs? – Information Privacy-Proactive Responses

Having analyzed internet privacy concerns, it is also critical to understand how internet users response to those IPCs. The concept of information privacy-proactive responses (IPPR) is recently been used by academics to define internet users’ response to internet information privacy threats and concerns which are caused by companies’ information practices. Internet users have IPCs when they are asked to provide personal identifiable information to companies through websites or applications. Under this situation, internet users may have several responses to IPC. Specifically, IPPR consists of three main categories of behavior that internet users may have which are: information provision, private action, and public action (Son & Kim, 2008). When internet users have some IPCs, their most possible way to response for protection of personal information is to decline to provide personal information and they can be classified into above categories based on how they respond to online companies’ mishandling their person information.

Information Provision

When having some online activities, most websites requires internet users to register and provide some personal information in the registration form. However, if the internet users are concerned about IPC, they usually refuse to provide personal information or provide incorrect information. Some personal information can also be collected by companies using analytic tools to analyze internet users’ online behavior without awareness of the users themselves. They may realize this only when they received targeted advertisements from online companies. Thus, internet users may reluctantly to provide personal identifiable information for online companies and two forms of response internet users may have are refusal and misrepresentation.

Private action

Research suggests that some consumers may often have some forms of private action including a boycott of a website or online company as well as share one’s negative experience with friends and relatives when they found their personal information was mishandled by the online company. Examples of this lost control of personal information various from receiving junk e-mail to online companies tracking users’ online activities and selling to other companies. Therefore, one form of private actions that internet users may take is removal from online companies’ database. Another form is to share negative experience which is expected to reduce the company’s sales and damage its reputation.

Public action

In addition to information provision and private action, internet users may also take public actions as a response to internet information threats. The main purpose of taking a public action is to find a way to remedy for information privacy threats. Two forms of public actions can be taken by internet users: direct complain to the online company and indirect complain to a third party. Internet users usually complain to third party only when they cannot get satisfactory redress from the online company.

Son and Kim (2008) developed a nomological model to understand how antecedents including IPC, perceived justice and social benefits from complaining affect IPPR. Their research result shows a different level of association between antecedents and IPPR which can provide a theoretical foundation for recommendations for online companies’ managers to take proactive actions. The study shows a general IPC association with six types of IPPR which confirms IPC can causing IPPR; however, the associations between each dimension of IPC and sic types of IPPR are not clear and need future research.

Conclusion and Future Research

Having answered the above three research questions, it can be concluded that the wide spread of internet technology has invaded internet users’ information privacy which causing internet user’s information privacy concerns. The definition and conceptualization of information privacy are various from discipline to discipline. In the information systems literature, information privacy often defined as individual’s control over his/her personal information. As a subclass of information privacy concerns, internet privacy concerns are also a multidimensional concept but research definitions were not consistent which may require future research. The third question answered how internet users may respond to information threats under certain circumstances. However, future research should also be performed to understand the relationships between dimensions of IPCs and types of IPPR to understand in a specific context, which type of IPPR might be adopted by internet users.

(Words count: 2120)

References

Acquisti, A. (2004). Privacy in electronic commerce and the economics of immediate gratification. Proceedings of the 5th ACM Electronic Commerce Conference, New York: ACM Press, 21-29.

Bansal, G., Zahedi, F. & Gefen, D. (2008). The moderating influence of privacy concern on the efficacy of privacy assurance mechanisms for building trust: a multiple-context investigation. Proceedings of 29th International Conference on Information Systems. Paris, France, December 14-17.

Belanger, F. & Crossler, R. E. (2011). Privacy in the digital age: a review of information privacy research in information systems. MIS Quarterly 35(4), 1017-1041.

BusinessWeek. (2000, March 20). Business week/Harrie Pool: A growing Threat. Retrieved from: http://businessweek.com/2000/00_12/b3673010.htm

Carroll, B. (2002). Price of privacy: selling consumer databases in bankruptcy. Journal of Interactive Marketing 16(3), 47-58.

Culnan, M. J. & Bies, R. J. (2003). Consumer privacy: balancing economic and justice consideration. Journal of Social Issues 59(2), 323-342.

Dinev, T. & Hart, P. (2005). Internet privacy concerns and social awareness as determinants of internet to transact. International Journal of Electronic Commerce 10(2), 7-29.

Dinev, T. & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research 17(1), 61-80.

Hong, W. & Thong, J. Y. L. (2013). Internet privacy concerns: an integrated conceptualization and four empirical studies. MIS Quarterly 37(1), 275-298.

Malhotra, N. K., Kim, S. S. & Agarwal, J. (2004). Internet users’ information privacy concerns (IUIPC): the construct, the scale, and a causal model. Information Systems Research 15(4), 336-355.

Milberg, S. J., Smith, H. J. & Burke, S. J. (2000). Information privacy:corporate management and national regulation. Organizational Science 11(1), 35-57.

Pavlou, P. A. (2011). State of the information privacy literature: where are we now and where should we go. MIS Quarterly 35(4), 977-988.

Smith, H. J., Dinec, T. & Xu, H. (2011). Information privacy research: an interdisciplinary review. MIS Quarterly 35(4), 898-1015.

Smith, H. J., Milberg, S. J. & Burke, S. J. (1996). Information privacy: measuring individuals’ concerns about organization organizational practices. MIS Quarterly 20(2), 167-196.

Son, J. Y. & Kim, S. S. (2008). Internet users’ information privacy-protective responses: a taxonomy and a nomological model. MIS Quarterly 32(3), 503-529.

Toch, E., Wang, Y. & Cranor, L. F. (2012). Personalization and privacy: a survey of privacy risks and remedies in personalization-based systems. User Model User-Adap Inter 22, 203-220. doi: 10.1001/s11257-011-9110-z.

Westin, A. F. (1967). Privacy and Freedom, New York: Atheneum.

30/04/2014