Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.

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Common method biases in behavioral research: a critical review of the literature and recommended remedies.

Basics of qualitative research: Grounded theory procedures and techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This study examines the relationship between use of Facebook, a popular online social network site, and the formation and maintenance of social capital. In addition to assessing bonding and bridging social capital, we explore a dimension of social capital that assesses one’s ability to stay connected with members of a previously inhabited community, which we call maintained social capital. Regression analyses conducted on results from a survey of undergraduate students (N = 286) suggest a strong association between use of Facebook and the three types of social capital, with the strongest relationship being to bridging social capital. In addition, Facebook usage was found to interact with measures of psychological well-being, suggesting that it might provide greater benefits for users experiencing low self-esteem and low life satisfaction.

/pdf/the-benefits-of-facebook-friends-social-capital-and-college-2j90l44gqu.pdf

The Benefits of Facebook “Friends:” Social Capital and College Students’ Use of Online Social Network Sites

This paper differs from previous examinations of organizational learning in that it is broader in scope and more evaluative of the literatures. Four constructs related to organizational learning knowledge acquisition, information distribution, information interpretation, and organizational memory are articulated, and the literatures related to each are described and critiqued.

The literature on knowledge acquisition is voluminous and multi-faceted, and so the knowledge acquisition construct is portrayed here as consisting of five subconstructs or subprocesses: 1 drawing on knowledge available at the organization's birth, 2 learning from experience, 3 learning by observing other organizations, 4 grafting on to itself components that possess knowledge needed but not possessed by the organization, and 5 noticing or searching for information about the organization's environment and performance. Examination of the related literatures indicates that much has been learned about learning from experience, but also that there is a lack of cumulative work and a lack of integration of work from different research groups. Similarly, much has been learned about organizational search, but there is a lack of conceptual work, and there is a lack of both cumulative work and syntheses with which to create a more mature literature. Congenital learning, vicarious learning, and grafting are information acquisition subprocesses about which relatively little has been learned.

The literature concerning information distribution is rich and mature, but an aspect of information distribution that is central to an organization's benefitting from its learning, namely how units that possess information and units that need this information can find each other quickly and with a high likelihood, is unexplored. Information interpretation, as an organizational process, rather than an individual process, requires empirical work for further advancement. Organizational memory is much in need of systematic investigation, particularly by those whose special concerns are improving organizational learning and decision making.

https://www.jstor.org/stable/pdfplus/2634941.pdf

Organizational Learning: The Contributing Processes and the Literatures

Cell phone conversations distract drivers. This research explores the possibility of reducing distracting by providing callers with remote information about the driver's traffic. We asked whether providing such contextual information would change the caller's conversation such that drivers would be less distracted. In Experiment 1 we examined this question in a low-fidelity driving simulator; in Experiment 2 we examined this question in a higher fidelity simulator. In both experiments, remote callers and passengers were distracting. Providing traffic information to the remote caller significantly reduced crashes in the low fidelity tests and significantly reduced passing in the high fidelity tests, compared with the control conditions. We consider the implications for development of remote displays or signals to promote driving safety.

Calling while driving: effects of providing remote traffic context

The research enterprise increasingly involves multidisciplinary collaboration, sometimes over geographic distance Technological advances have made these collaborations possible, and the history of past innovations suggests these collaborations are desirable Yet multidisciplinary projects can carry high coordination costs This study investigated how collaborations address disciplinary differences and geographic dispersion to coordinate people and tasks to achieve success We conducted an inductive study of 62 scientific collaborations supported by a program of the US National Science Foundation in 1998 and 1999 Projects with principal investigators in more disciplines did not appear to suffer more coordination losses and reported as many positive outcomes as did projects involving fewer disciplines By contrast, geographic dispersion, rather than multidisciplinarity, was most problematic Dispersed projects, with principal investigators from more universities, were significantly less well coordinated and reported fewer positive outcomes than collocated projects Coordination mechanisms that brought researchers together physically somewhat reduced the negative impact of dispersion We discuss several implications for theory, practice, and policy

https://aisel.aisnet.org/cgi/viewcontent.cgi?article=1130&context=icis2003

Coordination and Success in Multidisciplinary Scientific Collaborations.

Dramatic changes in the practice of scientific research over the past half century, including trends towards working in teams and on large projects, as well as geographically distributed and interdisciplinary collaboration, have created opportunities and challenges for scientists. Some of the newer ways of doing science create opportunities and challenges for organization theory. We describe how applying organization theory to science can enhance our knowledge of research organizations and raise questions for theories of coordination, social identity, the knowledge-based view of the firm, social networks, organizational learning, and absorptive capacity. We argue that an organizational perspective on science is critical to understanding the sources of technological innovation, making national policy on R&D investment, and designing successful 21st-century research organizations.

Organization Theory and the Changing Nature of Science

P sychologists have long studied how people’s communication context changes social relationships. Classic studies by Festinger, Schachter and Back (1950) and Newcomb (1961) examined how people’s physical proximity influenced their communication patterns and friendships. The Internet opens new options for communication that may challenge our understanding of how communication shapes social relationships. We have been studying the influence of Internet use on social relationships since 1995 (Kraut, Scherlis, Mukhopadhyay, Manning, & Kiesler, 1996).

The Social Impact of Internet Use

Organizational computing tools are often developed and managed with an eye toward increasing efficiency. Yet today's most widespread organizational computing tool, electronic mail, has an impact that goes well beyond efficiency effects. This article summarizes a two‐level perspective on organizational computing and reviews research results demonstrating strong organizational effects of electronic mail. From these results, we draw some lessons for the next generation of organizational computing.

Sara Kiesler

Papers

Calling while driving: effects of providing remote traffic context

Coordination and Success in Multidisciplinary Scientific Collaborations.

Organization Theory and the Changing Nature of Science

The Social Impact of Internet Use

Two‐level perspective on electronic mail in organizations