State of multilevel research perspective in the information systems discipline

Existing distinctions among macro and micro approaches have been jeopardising the advances of Information Systems (IS) research. Both approaches have been criticized for explaining one level while neglecting the other; thereby, the current situation necessitates the application of multilevel research for revealing the deficiencies. Instead of studying single level (macro or micro), multilevel research entails more than one level of conceptualization and analysis, simultaneously. As the notion of multilevel is borrowed from reference disciplines, there tends to be confusions and inconsistencies within the IS discipline, which hinders the adoption of multilevel research. This paper speaks for the potential value of multilevel research, by investigating the current application status of multilevel research within the IS domain. A content analysis of multilevel research articles from major IS conferences and journals is presented. Analysis results suggest that IS scholars have applied multilevel research to produce high quality work ranging from a variety of topics. However, researchers have not yet been consistently defining “multilevel”, leading to idiosyncratic meanings of multilevel research, most often, in authors’ own interpretations. We argue that a rigorous definition of “multilevel research” needs to be explicated for consistencies in research community.

Multilevel model of trade union membership

Presents a new theory of why people join or leave trade unions. Shows that a major influence on this decision is the quality of social relationships in the workplace. In particular, workplaces with a workforce of diverse occupational groups are not conducive to trade unionism.

A multilevel social network perspective on IT adoption

Adoption of technologies has long been a key area of research in the information systems (IS) discipline, and researchers have thus been interested in the attributes, beliefs, intentions, and behaviors of individuals and organisations that could explain information and communication technology (ICT) adoption. The focal unit of adoption has mainly been individuals and organisations, however, research at group or social network level as well as the inter-organizational level have recently gained increased interest from IS researchers. This recent focus supports the view of the world as being the sum of all relations. Various social network theories exist that seek to emphasize different proficiencies of social networks and explain theoretical mechanisms for behavior in social networks. The core idea of these theories is that social networks are valuable, and the relations among actors affect the behavior of individuals, groups, organizations, industries, and societies. IS researchers have also found that social network theory can help explain technology adoption. Some researchers, in addition, acknowledge that most adoption situations involve phenomena occuring at multiple levels, yet most technology adoption research applies a single level of analysis. Multilevel research can address the levels of theory, measurement, and analysis required to fully examining research questions. This paper therfore adapts the Coleman diagram into the Multi-level Framework of Technology. Adoption in order to explain how social network theory, at the individual and social network level, can help explain adoption of ICT. As Coleman (1990) attempts to create a link between the micro and macro level in a holistic manner, his approach is applicable in explaining ICT adoption

A Bayesian nonparametric approach to multilevel regression

Regression is at the cornerstone of statistical analysis. Multilevel regression, on the other hand, receives little research attention, though it is prevalent in economics, biostatistics and healthcare to name a few. We present a Bayesian nonparametric framework for multilevel regression where individuals including observations and outcomes are organized into groups. Furthermore, our approach exploits additional group-specific context observations, we use Dirichlet Process with product-space base measure in a nested structure to model group-level context distribution and the regression distribution to accommodate the multilevel structure of the data. The proposed model simultaneously partitions groups into cluster and perform regression. We provide collapsed Gibbs sampler for posterior inference. We perform extensive experiments on econometric panel data and healthcare longitudinal data to demonstrate the effectiveness of the proposed model