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Replication of Internet Privacy Concerns in the Mobile Banking Context
Abstract
This study is a conceptual replication of the work of Hong and Thong (2013), who developed the Internet Privacy Concerns scale to measure individuals’ concerns regarding how personal information is handled by websites. We adapt the wording of the original survey items to the context of mobile banking and follow the same procedures to assess the scale. The replication results reinforce the stability and applicability of the scale over the years and in different scenarios. In contrast with the original study, however, we detect a high correlation between the Control and Awareness dimensions, suggesting the design of an additional second-order dimension that we label “exposure management” (individuals’ consciousness about existing controls that mitigate the risks of personal data loss).
... Aboobucker and Bao (2018) conducted research in Sri Lanka and concluded that privacy concerns were not significant. A study of US adults found that privacy concerns were important to mobile banking users (Terlizzi et al., 2019). Given the lack of research on demographic linkages, this study assumes that education and income are likely to be important. ...
Most studies on the effects of privacy concerns examined social media behaviours. This research focuses on other privacy-protecting actions that people can take to reduce privacy risks. Responses to two surveys mailed to US adults living in four states were analysed to learn if privacy concerns were associated with seven actions that could lower privacy threats. Regressions showed that privacy concerns were not significantly related to most of the privacy-protecting behaviours. This suggests that privacy attitudes do not match privacy behaviours (i.e., a privacy paradox exists) and that self-reported privacy attitudes may overstate true feelings. Stricter regulations may not be justified by the privacy concerns that consumers describe in surveys. Most of the demographic relationships with the privacy-protecting behaviours from prior studies were not supported in the regressions, suggesting that some structural changes may have occurred in the profiles of the individuals who use these actions to protect their privacy.
Valid and reliable measurement instruments are crucial for human factors in privacy research. We expect them to measure what they purport to measure, yielding validity, and to measure this consistently, offering us reliability. While there is a range of privacy concern instruments available in the field and their investigation continues unabated, we shall focus on a brief form of the scale Internet users? Information privacy concerns (IUIPC-8) as an example. We not only present IUIPC-8 itself, but also consider methods for the evaluation of valid and reliable measurement instruments. In this, confirmatory factor analysis (CFA) serves us as a valuable tool. Our inquiry takes into account the ordinal and non-normal data yielded by the IUIPC questionnaire, compares multiple models to confirm the three-dimensionality is a fixed term, typically written with hyphen of the scale, examines global and local fit, and, finally, estimates construct validity and internal consistency reliability metrics. We offer a comparison between IUIPC-10 and IUIPC-8 drawing on two independent samples. In conclusion, we highlight properties of the scale and considerations for its use in practice.
Valid and reliable measurement instruments are crucial for human factors in privacy research. We expect them to measure what they purport to measure, yielding validity, and to measure this consistently, offering us reliability. While there is a range of privacy concern instruments available in the field and their investigation continues unabated, we shall focus on a brief form of the scale Internet Users? Information Privacy Concerns (IUIPC-8) as an example. We not only present IUIPC-8 itself, but also consider methods for the evaluation of valid and reliable measurement instruments. In this, confirmatory factor analysis (CFA) serves us as a valuable tool. Our inquiry takes into account the ordinal and non-normal data yielded by the IUIPC questionnaire, compares multiple models to confirm the three-dimensionality of the scale, examines global and local fit and, finally, estimates construct validity and internal consistency reliability metrics. We offer a comparison between IUIPC-10 and IUIPC-8 drawing on two independent samples. In conclusion, we highlight properties of the scale and considerations for its use in practice.
Controlling the coronavirus pandemic is triggering a cross-border strategy by which national governments attempt to control the spread of the COVID-19 pandemic. A response based on sharing facts about millions of private movements and a call to study information behavior during the global health crisis has been advised worldwide. The present study aims to identify the technologies to control the COVID-19 and future pandemics with massive data collection from users’ mobile devices. This research undertakes a Systematic Literature Review (SLR) of the studies about the currently available methods, strategies, and actions to collect and analyze data from users’ mobile devices. In a total of 76 relevant studies, 13 technologies that are classified based on the following aspect of data and data management have been identified: (1) security; (2) destruction; (3) voluntary access; (4) time span; and (5) storage. In addition, in order to understand how these technologies can affect user privacy, 25 data points that these technologies could have access to if installed through mobile applications have been detected. The paper concludes with a discussion of important theoretical and practical implications of preserving user privacy and curbing COVID-19 infections in the global public health emergency situation.
Cross-sectional studies of attitude-behavior relationships are vulnerable to the inflation of correlations by common method variance (CMV). Here, a model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV. This method also suggests procedures for designing questionnaires to increase the precision of this adjustment.
Drawing on expectation confirmation research, we develop hypotheses regarding the effect of compensation on key customer outcomes following a major data breach and consequent service recovery effort. Data were collected in a longitudinal field study of Sony customers during their data breach in 2011. A total of 144 customers participated in the two-phase data collection that began when the breach was announced and concluded after reparations were made. Using polynomial modeling and response surface analysis, we demonstrate that a modified assimilation-contrast model explained perceptions of service quality and continuance intention and a generalized negativity model explained repurchase intention. The results of our work contribute to research on data breaches and service failure by demonstrating the impacts of compensation on customer outcomes. We discuss theoretical and practical implications.
The financial service industry (FSI) has been the victim of sophisticated cyber attacks that take advantage of vulnerabilities created by employee misconduct. An experiment was conducted on Facebook ® with 500 employees from the top five largest banks in Brazil and another 100 randomly selected individuals. It was observed that bank employees are more prepared to avoid social engineering than typical Facebook ® users; however, more training is still needed because an anonymous individual using social engineering techniques successfully infiltrated an online social network (OSN) used by bank employees and gained access to sensitive data. Moreover, by analyzing the banking reports and their policies, it was possible to identify the five main mechanisms of control and governance implemented by the FSI to protect data: (a) incorporate the National Institute of Standards and Technology framework into its model of cybersecurity governance, (b) establish policies that regulate the use of information assets, (c) establish a code of conduct for its employees, (d) develop a corporate security culture, and (e) maintain a corporate security department .
Provides a nontechnical introduction to the partial least squares (PLS) approach. As a logical base for comparison, the PLS approach for structural path estimation is contrasted to the covariance-based approach. In so doing, a set of considerations are then provided with the goal of helping the reader understand the conditions under which it might be reasonable or even more appropriate to employ this technique. This chapter builds up from various simple 2 latent variable models to a more complex one. The formal PLS model is provided along with a discussion of the properties of its estimates. An empirical example is provided as a basis for highlighting the various analytic considerations when using PLS and the set of tests that one can employ is assessing the validity of a PLS-based model. (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Internet privacy concerns (IPC) is an area of study that is receiving increased attention due to the huge amount of personal information being gathered, stored, transmitted, and published on the Internet. While there is an emerging literature on IPC, there is limited agreement about its conceptualization in terms of its key dimensions and its factor structure. Based on the multidimensional developmental theory and a review of the prior literature, we identify alternative conceptualizations of IPC. We examine the various conceptualizations of IPC with four online surveys involving nearly 4,000 Internet users. As a baseline, study 1 compares the integrated conceptualization of IPC to two existing conceptualizations in the literature. While the results provide support for the integrated conceptualization, the second-order factor model does not outperform the correlated first-order factor model. Study 2 replicates the study on a different sample and confirms the results of study 1. We also investigate whether the prior results are affected by the different perspectives adopted in the wording of items in the original instruments. In study 3, we find that focusing on one's concern for website behavior (rather than one's expectation of website behavior) and adopting a consistent perspective in the wording of the items help to improve the validity of the factor structure. We then examine the hypothesized third-order conceptualizations of IPC through a number of alternative higher-order models. The empirical results confirm that, in general, the third-order conceptualizations of IPC outperform their lower-order alternatives. In addition, the conceptualization of IPC that has the best fit with the data contains a third-order general IPC factor, two second-order factors of interaction management and information management, and six first-order factors (i.e., collection, secondary usage, errors, improper access, control, and awareness). Study 4 cross-validates the results with another data set and examines IPC within the context of a nomological network. The results confirm that the third-order conceptualization of IPC has nomological validity, and it is a significant determinant of both trusting beliefs and risk beliefs. Our research helps to resolve inconsistencies in the key underlying dimensions of IPC, the factor structure of IPC, and the wording of the original items in prior instruments of IPC. Finally, we discuss the implications of this research.
Mechanical Turk (MTurk), an online labor market created by Amazon, has recently become popular among social scientists as a source of survey and experimental data. The workers who populate this market have been assessed on dimensions that are universally relevant to understanding whether, why, and when they should be recruited as research participants. We discuss the characteristics of MTurk as a participant pool for psychology and other social sciences, highlighting the traits of the MTurk samples, why people become MTurk workers and research participants, and how data quality on MTurk compares to that from other pools and depends on controllable and uncontrollable factors.
This study seeks to clarify the nature of control in the context of information privacy to generate insights into the effects of different privacy assurance approaches on context-specific concerns for information privacy. We theorize that such effects are exhibited through mediation by perceived control over personal information and develop arguments in support of the interaction effects involving different privacy assurance approaches (individual self-protection, industry self-regulation, and government legislation). We test the research model in the context of location-based services using data obtained from 178 individuals in Singapore. In general, the results support our core assertion that perceived control over personal information is a key factor affecting context-specific concerns for information privacy. In addition to enhancing our theoretical understanding of the link between control and privacy concerns, these findings have important implications for service providers and consumers as well as for regulatory bodies and technology developers.
Data quality is one of the major concerns of using crowdsourcing websites such as Amazon Mechanical Turk (MTurk) to recruit participants for online behavioral studies. We compared two methods for ensuring data quality on MTurk: attention check questions (ACQs) and restricting participation to MTurk workers with high reputation (above 95% approval ratings). In Experiment 1, we found that high-reputation workers rarely failed ACQs and provided higher-quality data than did low-reputation workers; ACQs improved data quality only for low-reputation workers, and only in some cases. Experiment 2 corroborated these findings and also showed that more productive high-reputation workers produce the highest-quality data. We concluded that sampling high-reputation workers can ensure high-quality data without having to resort to using ACQs, which may lead to selection bias if participants who fail ACQs are excluded post-hoc.
Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compen-sation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample's attitudes but similar to students' attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are pres-ent biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results.
In this study of employees in five multinational corporations, assessment was made of (a) employees' beliefs regarding the types of personal information stored their companies, (b) the accuracy of those perceptions, (c) reactions to various internal and external uses of this personal information, and (d) evaluations of the companies' information handling policies and practices.
Amazon Mechanical Turk (AMT) is an online crowdsourcing service where anonymous online workers complete web-based tasks for small sums of money. The service has attracted attention from experimental psychologists interested in gathering human subject data more efficiently. However, relative to traditional laboratory studies, many aspects of the testing environment are not under the experimenter's control. In this paper, we attempt to empirically evaluate the fidelity of the AMT system for use in cognitive behavioral experiments. These types of experiment differ from simple surveys in that they require multiple trials, sustained attention from participants, comprehension of complex instructions, and millisecond accuracy for response recording and stimulus presentation. We replicate a diverse body of tasks from experimental psychology including the Stroop, Switching, Flanker, Simon, Posner Cuing, attentional blink, subliminal priming, and category learning tasks using participants recruited using AMT. While most of replications were qualitatively successful and validated the approach of collecting data anonymously online using a web-browser, others revealed disparity between laboratory results and online results. A number of important lessons were encountered in the process of conducting these replications that should be of value to other researchers.
193 randomly selected adults completed a structured interview concerning their values, beliefs, attitudes, experiences, and intentions concerning information-privacy issues involving 6 types of organizations such as employers, law enforcement agencies, and the Internal Revenue Service. Results indicate that higher valuations of informational privacy were related to lower feelings of control over personal information, less satisfaction with their perceived degree of control, and less willingness to participate in a subsequent interview. Information-privacy beliefs and attitudes varied with the type of organization under consideration. Implications are discussed in terms of organizational and societal policy concerning the treatment of information about individuals. (16 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Despite the fact that validating the measures of constructs is critical to building cumulative knowledge in MIS and the behavioral sciences, the process of scale development and validation continues to be a challenging activity. Undoubtedly, part of the problem is that many of the scale development procedures advocated in the literature are limited by the fact that they (1) fail to adequately discuss how to develop appropriate conceptual definitions of the focal construct, (2) often fail to properly specify the measurement model that relates the latent construct to its indicators, and (3) underutilize techniques that provide evidence that the set of items used to represent the focal construct actually measures what it purports to measure. Therefore, the purpose of the present paper is to integrate new and existing techniques into a comprehensive set of recommendations that can be used to give researchers in MIS and the behavioral sciences a framework for developing valid measures. First, we briefly elaborate upon some of the limitations of current scale development practices. Following this, we discuss each of the steps in the scale development process while paying particular attention to the differences that are required when one is attempting to develop scales for constructs with formative indicators as opposed to constructs with reflective indicators. Finally, we discuss several things that should be done after the initial development of a scale to examine its generalizability and to enhance its usefulness.
The lack of consumer confidence in information privacy has been identified as a major problem hampering the growth of e-commerce. Despite the importance of understanding the nature of online consumers' concerns for information privacy, this topic has received little attention in the information systems community. To fill the gap in the literature, this article focuses on three distinct, yet closely related, issues. First, drawing on social contract theory, we offer a theoretical framework on the dimensionality of Internet users' information privacy concerns (IUIPC). Second, we attempt to operationalize the multidimensional notion of IUIPC using a second-order construct, and we develop a scale for it. Third, we propose and test a causal model on the relationship between IUIPC and behavioral intention toward releasing personal information at the request of a marketer. We conducted two separate field surveys and collected data from 742 household respondents in one-on-one, face-to-face interviews. The results of this study indicate that the second-order IUIPC factor, which consists of three first-order dimensions--namely, collection, control, and awareness--exhibited desirable psychometric properties in the context of online privacy. In addition, we found that the causal model centering on IUIPC fits the data satisfactorily and explains a large amount of variance in behavioral intention, suggesting that the proposed model will serve as a useful tool for analyzing online consumers' reactions to various privacy threats on the Internet.
The arrival of the "information age" holds great promise in terms of providing organizations with access to a wealth of information stores. However, the free exchange of electronic information also brings the threat of providing easy, and many times unwanted, access to personal information. Given the potential backlash of consumers, it is imperative that both researchers and practitioners understand the nature of consumers' concern for information privacy and accurately model the construct within evolving research and business contexts. Drawing upon a sample of 355 consumers and working within the framework of confirmatory factor analysis, this study examines the factor structure of the concern for information privacy (CFIP) instrument posited by Smith et al. (1996). Consistent with prior findings, the results suggest that each dimension of this instrument is reliable and distinct. However, the results also suggest that CFIP may be more parsimoniously represented as a higher-order factor structure rather than a correlated set of first-order factors. The implication of these results is that each dimension of CFIP as well as the supra dimension derived from the associations among dimensions are important in capturing CFIP and associating the construct to other important antecedents and consequences.
Amazon's Mechanical Turk (AMT) is a Web application that provides instant access to thousands of potential participants for survey-based psychology experiments, such as the acceptability judgment task used extensively in syntactic theory. Because AMT is a Web-based system, syntacticians may worry that the move out of the experimenter-controlled environment of the laboratory and onto the user-controlled environment of AMT could adversely affect the quality of the judgment data collected. This article reports a quantitative comparison of two identical acceptability judgment experiments, each with 176 participants (352 total): one conducted in the laboratory, and one conducted on AMT. Crucial indicators of data quality--such as participant rejection rates, statistical power, and the shape of the distributions of the judgments for each sentence type--are compared between the two samples. The results suggest that aside from slightly higher participant rejection rates, AMT data are almost indistinguishable from laboratory data.
Internet privacy policies describe an organization's practices on data collection, use, and disclosure. These privacy policies both protect the organization and signal integrity commitment to site visitors. Consumers use the stated website policies to guide browsing and transaction decisions. This paper compares the classes of privacy protection goals (which express desired protection of consumer privacy rights) and vulnerabilities (which potentially threaten consumer privacy) with consumer privacy values. For this study, we looked at privacy policies from nearly 50 websites and surveyed over 1000 Internet users. We examined Internet users' major expectations about website privacy and revealed a notable discrepancy between what privacy policies are currently stating and what users deem most significant. Our findings suggest several implications to privacy managers and software project managers. Results from this study can help managers determine the kinds of policies needed to both satisfy user values and ensure privacy-aware website development efforts.
The increasing social emphasis on wellness empowers individuals to track and manage their personal health using mobile health applications and wearable tracking devices. However, these devices require the disclosure and generation of copious amounts of sensitive health data and thus often foster concerns regarding the privacy of individuals’ health data. Notwithstanding that fact, the few studies that have examined citizens’ information privacy concerns in the health context focus either on a specific technology or utilize a one-dimensional measure of concern. In order to develop a deeper understanding of citizens’ health privacy concerns, this replication study adapts the six-dimensional Internet Privacy Concerns instrument to empirically test citizens’ health privacy concerns in the United States and Ireland. The paper advances understanding by illustrating the applicability of this measure in the complex health context, and elucidating how citizens’ health privacy concerns reduce trust and heighten risk perceptions associated with health technology vendors.
Each scale is prefaced by the same information. Details are provided of construct, description, development, samples, validity, scores, sources, references, and other evidence. The book includes a number of measures that have been used in several studies. The volume serves as a guide to the literature and may spur further refinement of existing measures in terms of item reduction, dimensionality, reliability, and validity. This Handbook also aims to help identify areas where measures are needed, thus encouraging further development of valid measures of consumer behavior and marketing constructs.
Online crowdsourcing markets (OCM) are becoming more popular as a source for data collection. In this paper, we examine the consistency of survey results across student samples, consumer panels, and online crowdsourcing markets (specifically Amazon's Mechanical Turk) both within the United States and outside. We conduct two studies examining the technology acceptance model (TAM) and the expectation-disconfirmation theory (EDT) to explore potential differences in demographics, psychometrics, structural model estimates, and measurement invariances. Our findings indicate that (1) U.S.-based OCM samples provide demographics much more similar to our student and consumer panel samples than the non-U.S.-based OCM samples; (2) both U.S. and non-U.S. OCM samples provide initial psychometric properties (reliability, convergent, and divergent validity) that are similar to those of both student and consumer panels; (3) non-U.S. OCM samples generally provide differences in scale means compared to those of our students, consumer panels, and U.S. OCM samples; and (4) one of the non-U.S. OCM samples refuted the highly replicated and validated TAM model in the relationship of perceived usefulness to behavioral intentions. Although our post hoc analyses isolated some cultural and demographic effects with regard to the non-U.S. samples in Study 1, they did not address the model differences found in Study 2. Specifically, the inclusion of non-U.S. OCM respondents led to statistically significant differences in parameter estimates, and hence to different statistical conclusions. Due to these unexplained differences that exist within the non-U.S. OCM samples, we caution that the inclusion of non-U.S. OCM participants may lead to different conclusions than studies with only U.S. OCM participants. We are unable to conclude whether this is due to of cultural differences, differences in the demographic profiles of non-U.S. OCM participants, or some unexplored factors within the models. Therefore, until further research is conducted to explore these differences in detail, we urge researchers utilizing OCMs with the intention to generalize to U.S. populations focus on U.S.-based participants and exercise caution in using non-U.S. participants. We further recommend that researchers should clearly describe their OCM usage and design (e.g., demographics, participant filters, etc.) procedures. Overall, we find that U.S. OCM samples produced models that lead to similar statistical conclusions as both U.S. students and U.S. consumer panels at a considerably reduced cost.
This Review summarizes and draws connections between diverse streams of empirical research on privacy behavior. We use three themes to connect insights from social and behavioral sciences: people's uncertainty about the consequences of privacy-related behaviors and their own preferences over those consequences; the context-dependence of people's concern, or lack thereof, about privacy; and the degree to which privacy concerns are malleable—manipulable by commercial and governmental interests. Organizing our discussion by these themes, we offer observations concerning the role of public policy in the protection of privacy in the information age.
Copyright © 2015, American Association for the Advancement of Science.
In this study, we examined data quality among Amazon Mechanical Turk (MTurk) workers based in India, and the effect of monetary compensation on their data quality. Recent studies have shown that work quality is independent of compensation rates, and that compensation primarily affects the quantity but not the quality of work. However, the results of these studies were generally based on compensation rates below the minimum wage, and far below a level that was likely to play a practical role in the lives of workers. In this study, compensation rates were set around the minimum wage in India. To examine data quality, we developed the squared discrepancy procedure, which is a task-based quality assurance approach for survey tasks whose goal is to identify inattentive participants. We showed that data quality is directly affected by compensation rates for India-based participants. We also found that data were of a lesser quality among India-based than among US participants, even when optimal payment strategies were utilized. We additionally showed that the motivation of MTurk users has shifted, and that monetary compensation is now reported to be the primary reason for working on MTurk, among both US- and India-based workers. Overall, MTurk is a constantly evolving marketplace where multiple factors can contribute to data quality. High-quality survey data can be acquired on MTurk among India-based participants when an appropriate pay rate is provided and task-specific quality assurance procedures are utilized.
The debate about online privacy gives testimony of Web users' concerns. Privacy concerns make consumers adopt data protection features, guide their appreciation for existing features, and can steer their consumption choices amongst competing businesses. However, approaches to measure privacy concern are fragmented and often ad-hoc, at the detriment of reliable results.
The need for measurement instruments for privacy concern is twofold. First, attitudes and opinions about data protection cannot be established and compared without reliable mechanisms. Second, behavioural studies, notably in technology acceptance and the behavioural economics of privacy require measures for concern as a moderating factor.
In its first part, this paper provides a comprehensive review of existing survey instruments for measuring privacy concerns. The second part focuses on revealed preferences that can be used for opportunistically measuring privacy concerns in the wild or for scale validation. Recommendations for scale selection and reuse are provided.
Crowdsourcing systems like Amazon's Mechanical Turk (AMT) allow data to be collected from a large sample of people in a short amount of time. This use has garnered considerable interest from behavioral scientists. So far, most experiments conducted on AMT have focused on survey-type instruments because of difficulties inherent in running many experimental paradigms over the Internet. This study investigated the viability of presenting stimuli and collecting response times using Adobe Flash to run ActionScript 3 code in conjunction with AMT. First, the timing properties of Adobe Flash were investigated using a phototransistor and two desktop computers running under several conditions mimicking those that may be present in research using AMT. This experiment revealed some strengths and weaknesses of the timing capabilities of this method. Next, a flanker task and a lexical decision task implemented in Adobe Flash were administered to participants recruited with AMT. The expected effects in these tasks were replicated. Power analyses were conducted to describe the number of participants needed to replicate these effects. A questionnaire was used to investigate previously undescribed computer use habits of 100 participants on AMT. We conclude that a Flash program in conjunction with AMT can be successfully used for running many experimental paradigms that rely on response times, although experimenters must understand the limitations of the method.
Based on findings from previous interviews with 900 5–17 yr old children and adolescents and other research, a situational perspective of privacy is developed. Situations affecting an individual's perception and experience of privacy and invasions of privacy are described in terms of 3 dimensions: (a) self-ego (i.e., a developmental process that focuses on individuation and personal dignity); (b) environmental, which includes cultural, sociophysical, and life cycle elements; and (c) interpersonal, which involves interaction management and information management elements. The issue of control/choice is conceptualized as a mediating variable in the privacy system. (23 ref) (PsycINFO Database Record (c) 2012 APA, all rights reserved)
Amazon's Mechanical Turk (MTurk) is a relatively new website that contains the major elements required to conduct research: an integrated participant compensation system; a large participant pool; and a streamlined process of study design, participant recruitment, and data collection. In this article, we describe and evaluate the potential contributions of MTurk to psychology and other social sciences. Findings indicate that (a) MTurk participants are slightly more demographically diverse than are standard Internet samples and are significantly more diverse than typical American college samples; (b) participation is affected by compensation rate and task length, but participants can still be recruited rapidly and inexpensively; (c) realistic compensation rates do not affect data quality; and (d) the data obtained are at least as reliable as those obtained via traditional methods. Overall, MTurk can be used to obtain high-quality data inexpensively and rapidly.
© The Author(s) 2011.
Despite recurring concerns about common method variance (CMV) in survey research, the information systems (IS) community remains largely uncertain of the extent of such potential biases. To address this uncertainty, this paper attempts to systematically examine the impact of CMV on the inferences drawn from survey research in the IS area. First, we describe the available approaches for assessing CMV and conduct an empirical study to compare them. From an actual survey involving 227 respondents, we find that although CMV is present in the research areas examined, such biases are not substantial. The results also suggest that few differences exist between the relatively new marker-variable technique and other well-established conventional tools in terms of their ability to detect CMV. Accordingly, the marker-variable technique was employed to infer the effect of CMV on correlations from previously published studies. Our findings, based on the reanalysis of 216 correlations, suggest that the inflated correlation caused by CMV may be expected to be on the order of 0.10 or less, and most of the originally significant correlations remain significant even after controlling for CMV. Finally, by extending the marker-variable technique, we examined the effect of CMV on structural relationships in past literature. Our reanalysis reveals that contrary to the concerns of some skeptics, CMV-adjusted structural relationships not only remain largely significant but also are not statistically differentiable from uncorrected estimates. In summary, this comprehensive and systematic analysis offers initial evidence that (1) the marker-variable technique can serve as a convenient, yet effective, tool for accounting for CMV, and (2) common method biases in the IS domain are not as serious as those found in other disciplines.
Strategic uses of information technology based on personal information may raise privacy concerns among consumers if these applications do not reflect a common set of values. This study addresses what differentiates consumers who object to certain uses of personal information from those who do not object. Data collected by questionnaire from young consumers are used to identify a research approach for investigating attitudes toward the secondary use of personal information for direct marketing. Secondary information use occurs when personal information collected for one purpose Is subsequently used fora different purpose. While secondary information use is both widespread and legal, it may be viewed as an invasion of privacy when it occurs without the knowledge or consent of the consumer. The setting for the study is the use of point-of-sale data from a supermarket frequent shopper program to generate direct mail solicitations. Control emerges as a clear theme in differentiating individuals with positive overall attitudes toward secondary information use from those with negative attitudes. Study participants with positive attitudes are less concerned about privacy (measured as control over personal information), perceive shopping by mall as beneficial, and have coping strategies for dealing with unwanted mail. The results also suggest that theory related to categorization of strategic issues as positive-negative with outcomes that are controllable/uncontrollable provides a basis for understanding differences in the ways individuals perceive practices involving personal information. Future research should focus on the specific characteristics of secondary use practices, including the sensitivity of the information, its source, its perceived relevance to the original transaction, and whether disclosure reflects informed consent or results in a potentially harmful outcome to the individual.
Information privacy has been called one of the most important ethical issues of the informa-tion age. Public opinion polls show rising levels of concern about privacy among Americans. Against this backdrop, research into issues associated with information privacy is increasing. Based on a number of preliminary studies, it has become apparent that organizational practices, individuals' perceptions of these practices, and societal responses are inextricably linked in many ways. Theories regarding these relationships are slowly emerging. Unfortunately, researchers attempting to examine such relationships through confirmatory empirical approaches may be impeded by the lack of validated instruments for measuring individuals' concerns about organizational information privacy practices. To enable future studies in the information privacy research stream, we developed and validated an instrument that identifies and measures the primary dimensions of individuals' concerns about organizational information privacy practices. The development process included examinations of privacy literature; experience surveys and focus groups; and the use of expert judges. The result was a parsimonious 15-item instrument with four sub-scales tapping into dimensions of individuals' concerns about organizational information privacy practices. The instrument was rigorously tested and validated across several heterogenous populations, providing a high degree of confidence in the scales' validity, reliability, and generalizability.
Information privacy refers to the desire of individuals to control or have some influence over data about themselves. Advances in information technology have raised concerns about information privacy and its impacts, and have motivated Information Systems researchers to explore information privacy issues, including technical solutions to address these concerns. In this paper, we inform researchers about the current state of information privacy research in IS through a critical analysis of the IS literature that considers information privacy as a key construct. The review of the literature reveals that information privacy is a multilevel concept, but rarely studied as such. We also find that information privacy research has been heavily reliant on studentbased and USA-centric samples, which results in findings of limited generalizability. Information privacy research focuses on explaining and predicting theoretical contributions, with few studies in journal articles focusing on design and action contributions. We recommend that future research should consider different levels of analysis as well as multilevel effects of information privacy. We illustrate this with a multilevel framework for information privacy concerns. We call for research on information privacy to use a broader diversity of sampling populations, and for more design and action information privacy research to be published in journal articles that can result in IT artifacts for protection or control of information privacy.
We examine three possible explanations for differences in Internet privacy concerns revealed by national regulation: (1) These differences reflect and are related to differences in cultural values described by other research; (2) these differences reflect differences in Internet experience; or (3) they reflect differences in the desires of political institutions without reflecting underlying differences in privacy preferences. Using a sample of Internet users from 38 countries matched against the Internet population of the United States, we find support for (1) and (2), suggesting the need for localized privacy policies. Privacy concerns decline with Internet experience. Controlling for experience, cultural values were associated with differences in privacy concerns. These cultural differences are mediated by regulatory differences, although new cultural differences emerge when differences in regulation are harmonized. Differences in regulation reflect but also shape country differences. Consumers in countries with sectoral regulation have less desire for more privacy regulation.
The nation's median age continues to rise
- Census
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The big data breach suffered by Equifax has alarming implications. The Economist
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