Energy Conservation—A Case Study in Campaign Evaluation

This study demonstrates the considerations involved in evaluation research into the effectiveness of campaigns designed to influence social change. It is an examination of the results of applying aspects of the attitude change studies to the subject of environmental issues. A case study was made of the Smallternatives energy conservation campaign which originated in 1976 in Brisbane, at a time of little recognition of an energy 'crisis' for Australia or the need for energy conservation. Results indicated that levels of concern for environmental issues were present throughout the sample; that one group (who requested Smallternatives information) were more concerned about energy and environmental issues than groups who had not requested it; and that the booklet, when issued without request, had little or no effect on desired attitudes towards energy conservation. These results are discussed with respect to the dilemma of attitude-behaviour consistency, and the impact of information input when it is sent to, rather than requested by, the individual.     

Relations Between Alphabetized Name Order and Nomination Counts in Peer Nomination Measures

Peer nominations, a central method for measuring peer relationships in developmental research, typically involve asking children or adolescents to choose peers who fit various criteria from an alphabetized roster of classmates or grade‐mates. Although such measures have been used for decades, very little research has investigated the effects of alphabetical name order on the number of nominations received by peers. This study collected peer nominations for 20 items among 607 eighth grade participants in two schools. Regression analyses showed that earlier name order significantly predicted higher nomination counts for eight of the items, and explained over 5 percent of the variance in four affective variables (friendship, acceptance, acquaintanceship, and received liking). Across variables, name order effects were negatively correlated with internal reliability of nominations, implying that order effects may be related to the consensus of the peer group. Name order also had a minimal effect on inter‐correlations among a subset of variables. Implications and concrete recommendations for controlling and reducing name order effects in future research are discussed.     

Manufacturing Proactiveness and Performance*

Since Skinner's [40] landmark article depicting the manufacturing function as the “missing link” in corporate strategic processes, a portion of the blame for inferior performance in many firms has been attributed to the subordinate strategic position of manufacturing. It has been argued that part of the solution to misalignments between the capabilities possessed by manufacturing and the requirements dictated by customers is for manufacturing to take a more proactive stance. However, little research has been reported which examines manufacturing proactiveness empirically. In this paper, we address this gap by developing an operational definition of manufacturing proactiveness and testing empirically whether a link exists between proactiveness and performance based on data collected from a sample of manufacturers. Based on the manufacturing strategy literature, we identify two major dimensions of manufacturing proactiveness: (1) the degree of manufacturing's involvement in the strategic processes of the business unit; and (2) the degree of commitment to a long-term program of investments in manufacturing structure and infrastructure aimed at building capabilities in anticipation of their need. We develop reliable scales for measuring each of the dimensions of proactiveness and use the data to provide evidence of a clear link between manufacturing proactiveness and business performance. We show that investments in structural programs coupled with either high levels of manufacturing involvement in strategic processes or planned investments in infrastructural programs correlate with higher than average performance.     

Probabilistic Analysis of Dam Accidents Worldwide: Risk Assessment for Dams of Different Purposes in OECD and Non-OECD Countries with Focus on Time Trend Analysis

This study presents probabilistic analysis of dam accidents worldwide in the period 1911–2016. The accidents are classified by the dam purpose and by the country cluster, where they occurred, distinguishing between the countries of the Organization for Economic Cooperation and Development (OECD) and nonmember countries (non-OECD without China). A Bayesian hierarchical approach is used to model distributions of frequency and severity for accidents. This approach treats accident data as a multilevel system with subsets sharing specific characteristics. To model accident probabilities for a particular dam characteristic, this approach samples data from the entire data set, borrowing the strength across data set and enabling to model distributions even for subsets with scarce data. The modelled frequencies and severities are combined in frequency-consequence curves, showing that accidents for all dam purposes are more frequent in non-OECD (without China) and their maximum consequences are larger than in OECD countries. Multipurpose dams also have higher frequencies and maximum consequences than single-purpose dams. In addition, the developed methodology explicitly models time dependence to identify trends in accident frequencies over the analyzed period. Downward trends are found for almost all dam purposes confirming that technological development and implementation of safety measures are likely to have a positive impact on dam safety. The results of the analysis provide insights for dam risk management and decision-making processes by identifying key risk factors related to country groups and dam purposes as well as changes over time.     

Promoting Children's Welfare by Inter‐professional Practice and Learning in Social Work and Primary Care

Social work in the United Kingdom is an activity that benefits from, and often requires, co‐operation between different staff and across different professions. How this is to be achieved has been a central dilemma for practice and policy for many years. Primary care health services often play a key role. New policy and practice developments are designed to promote inter‐professional working with children, where research has shown significant problems in the community‐based health care of looked after children, children leaving care, and children at serious risk. There is, however, little evidence from research that supports current developments in inter‐professional practice; analysis of the particular nature of the inter‐professional problems is lacking, as are therefore the relevant inter‐professional solutions. A framework for better analysis, linked to developments in practice‐based research, one that would yield significant improvements for children's welfare, is presented here.     

Goffman,Growing Up,and Experienced Relationality

This article adopts a Goffmanian framework to grapple with the emotional‐relational experiences associated with teenagers growing up. Drawing upon focus group interviews with 14–15 year olds in East and West Belfast, the lack of full personhood attributed to nonadults is discussed in relation to spatial exclusion and stigmatizing service transactions, along with some emotional consequences. However, instances where teenagers embrace as well as reject this ostensibly negative social positioning are also elaborated. The overall thrust of the article seeks to demonstrate how the hierarchical adult‐child complex is built and maintained through everyday face‐to‐face interaction and the ritualistic expression of standards of worth assigned to participants.     

Dynamic reasoning and time pressure: Transition from analytical operations to experiential responses

Based upon the Decision Field Theory (Busemeyer and Townsend 1993), we tested a model of dynamic reasoning to predict the effect of time pressure on analytical and experiential processing during decision-making. Forty-six participants were required to make investment decisions under four levels of time pressure. In each decision, participants were presented with experiential cues which were either congruent or incongruent with the analytical information. The congruent/incongruent conditions allowed us to examine how many decisions were based upon the experiential versus the analytical information, and to see if this was affected by the varying degrees of time pressure. As expected, the overall accuracy was reduced with greater time pressure and accuracy was higher when the experiential and analytical cues were congruent than when they were incongruent. Of great interest was the data showing that under high time pressure participants used more experiential cues than at other time pressures. We suggest that the dynamic reasoning paradigm has some future potential for predicting the effects of experiential biases in general, and specifically under time pressure.     

Identifying Latent Structures in Panel Data

This paper provides a novel mechanism for identifying and estimating latent group structures in panel data using penalized techniques. We consider both linear and nonlinear models where the regression coefficients are heterogeneous across groups but homogeneous within a group and the group membership is unknown. Two approaches are considered—penalized profile likelihood (PPL) estimation for the general nonlinear models without endogenous regressors, and penalized GMM (PGMM) estimation for linear models with endogeneity. In both cases, we develop a new variant of Lasso called classifier‐Lasso (C‐Lasso) that serves to shrink individual coefficients to the unknown group‐specific coefficients. C‐Lasso achieves simultaneous classification and consistent estimation in a single step and the classification exhibits the desirable property of uniform consistency. For PPL estimation, C‐Lasso also achieves the oracle property so that group‐specific parameter estimators are asymptotically equivalent to infeasible estimators that use individual group identity information. For PGMM estimation, the oracle property of C‐Lasso is preserved in some special cases. Simulations demonstrate good finite‐sample performance of the approach in both classification and estimation. Empirical applications to both linear and nonlinear models are presented.     

A Business Model Innovation Typology

An effective business model is the core enabler of any company's performance. Business model innovation is not only becoming more and more important due to increasing and globalizing competition, but also an enormous challenge, both theoretically and practically. Although many managers are eager to consider more disruptive changes to their business model, they often do not know how to articulate their existing or desired business model and, even less so, understand the possibilities for innovating it. One of the steps toward developing more theoretical insight and practical guidelines is the identification of types and the development of a typology of business model innovations. Ten retrospective case studies of business model innovations undertaken by two industrial companies provide the empirical basis for this article. We analyzed the characteristics of these innovations as well as their success rates. The findings suggest that there are indeed various business model innovation types, each with its own characteristics and challenges.