Survey methodologists are searching for covariates to use in nonresponse adjustment models, ultimately hoping to find variables that are highly correlated with both the outcomes of interest and the propensity to respond. These covariates can come from auxiliary data that provide information on both respondents and nonrespondents. Two such types of auxiliary data are interviewer observations (a form of paradata) and commercially available data on small areas or households. Interviewer observations intended for use in nonresponse adjustment can be specifically designed to match the outcome variables of interest, while commercial data provide a broad set of small area descriptors that may be correlated with multiple outcomes. This analysis examines these two data sources to determine which is more predictive of the outcomes of interest for a particular survey, thereby fulfilling one of the criteria for a good adjustment variable. The outcomes of interest in this analysis are self-reports of household income and receipt of unemployment benefits from a survey of labor market participation. The findings suggest that at this point in time, compared to commercial data, interviewer observations are better at predicting these outcomes, particularly in the subpopulation that the survey targets. Therefore, the observations share more (accurate) information with the true value, making them better for adjustment on this dimension. The results will inform the work of both researchers wishing to improve their nonresponse adjustments and survey managers looking to make better use of their survey budget.
The use of personal names for screening is an increasingly popular sampling technique for migrant populations. Although this is often an effective sampling procedure, very little is known about the properties of this method. Based on a large German survey, this article compares characteristics of respondents whose names have correctly been classified as belonging to a migrant population with respondents, that are migrants and whose names have not been classified as belonging to a migrant population. Although significant differences with large effect sizes in some cases could be found, the overall bias introduced by name-based sampling seems to be small as long as procedures with a small false-negative rate are used.
While interviewer observations have good potential as auxiliary sources of information on key survey variables, questions about their quality temper enthusiasm for their use in survey estimation and responsive survey design. This study considers the utility of two interviewer observations (household income and household receipt of unemployment benefits) collected in a panel survey: the German Labor Market and Social Security (PASS) study. We find that the ability of the interviewer observations to accurately indicate these household features is not as high as that of prior-wave survey reports on these features, but that the observations do tend to capture accurate information for households with changing socio-economic status over time (where prior-wave reports may be inconsistent with current-wave reports). The observations add modest predictive power to models for key survey variables that also account for survey reports on related variables in prior waves, but this predictive power may be limited by relatively high error rates and variance in observation quality among interviewers. Finally, estimates based on panel households only improve slightly when including the observations in nonresponse adjustments, which is likely due to the inability of the observations to also predict response propensity (given a relatively low attrition rate for the panel households). Implications for practice and directions for future research in this area are discussed in conclusion.
The challenge of managing the relationship between a firm's business and IT in order to derive business value from IT is an important topic on researchers' and practitioners' agendas. The focus of most related research and management actions has been on the top management or project management levels. However, conflicts frequently arise within the line organization when applications are extended, enhanced, maintained, or otherwise changed operationally outside software development projects. This study focuses on the impact of relationships at the application-change level and strives to identify and explain favorable social structures for effective business/IT dialog at the operational level. We collected data in seven comprehensive case studies, including 88 interviews and corresponding surveys, and applied social network analysis to show that three social structures at the implementation level influence the degree to which IT applications are maintained and enhanced in line with business requirements: (1) interface actors connecting business and IT, (2) the relationships between interface actors and the corresponding unit, and (3) the relationships between interface actors and other employees in their unit. In three cases, less favorable structures are revealed that correspond to low application change effectiveness and software applications that do not meet business requirements. The other cases benefit from favorable social structures and thus enhance fulfillment of business requirements and result in higher IT business value. This paper contributes to IS research by helping to explain why companies may not provide favorable IT services despite favorable relationships at the top management level and successful application development projects.
It is widely acknowledged that IT and business resources need to be well aligned to achieve organizational goals. Yet, year after year, chief information officers (CIOs) still name business-IT alignment a key challenge for IT executives. While alignment research has matured, we still lack a sound theoretical foundation for alignment. Transcending the predominantly strategic executive level focus, we develop a model of 'operational alignment' and IT business value that combines a social perspective of IT and business linkage with a view of interaction between business and IT at non-strategic levels, such as in daily business operations involving regular staff. Drawing on social capital theory to explain how alignment affects organizational performance, we examine why common suggestions like "communicate more" are insufficient to strengthen alignment and disclose how social capital between IT and business units drives alignment and ultimately IT business value.
Empirical data from 136 firms confirms the profound impact of operational business-IT alignment, composed of social capital and business understanding of IT, on IT flexibility, IT utilization, and organizational performance. The results show that social capital theory is a useful theoretical foundation for understanding how business IT alignment works. The findings suggest that operational alignment is at least as important as strategic alignment for IT service quality, that managers need to focus on operational aspects of alignment beyond communication by fostering knowledge, trust and respect, and that IT utilization and flexibility are appropriate intermediate goals for business-IT alignment governance.