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Purpose – This chapter reports on a rapidly growing trend in the analysis of data about emerging market (EM) economies – the use of baseline models as comparisons for explanatory models. Baseline models estimate expected values for the dependent variable in the absence of a hypothesized causal effect but set higher standards than do traditional null hypotheses tests that expect no effect.

Design/methodology/approach – Although the use of baseline models research originated in the 1960s, it has not been widely discussed, or even acknowledged, in the EM literature. We surveyed published EM studies to determine trends in the use of baseline models.

Findings – We categorize and describe the different types of baseline models that scholars have used in EM studies, and draw inferences about the differences between more effective and less effective uses of baseline models.

Value – We believe that comparisons with baseline models offer distinct methodological advantages for the iterative development of better explanatory models and a deeper understanding of empirical phenomena.

This chapter reports on a rapidly growing trend in data analysis – analytic comparisons between baseline models and explanatory models. Baseline models estimate values for the dependent variable in the absence of hypothesized causal effects. Thus, the baseline models discussed in this chapter differ from the baseline models commonly used in sequential regression analyses.Baseline modelling entails iteration: (1) Researchers develop baseline models to capture key patterns in the empirical data that are independent of the hypothesized effects. (2) They compare these patterns with the patterns implied by their explanatory models. (3) They use the derived insights to improve their explanatory models. (4) They iterate by comparing their improved explanatory models with modified baseline models.The chapter draws on methodological literature in economics, applied psychology, and the philosophy of science to point out fundamental features of baseline modelling. Examples come from research in international business and management, emerging market economies and developing countries.Baseline modelling offers substantial advantages for theory development. Although analytic comparisons with baseline models originated in some research fields as early as the 1960s, they have not been widely discussed or applied in international management. Baseline modelling takes a more inductive and iterative approach to modelling and theory development. Because baseline modelling holds substantial potential, international-management scholars should explore its opportunities for advancing scientific progress.

Using data from the Survey of Income and Program Participation (SIPP) matched to administrative records, we examine mortality risk and participation in the Disability Insurance (DI) and Supplemental Security Income (SSI) disability programs from a long-term perspective. Over a period of 14 years, we analyze the effect of self-reported health and disability on the probability of death and disability program entry among individuals aged 18–48 in 1984. We also assess DI and SSI programs from a life-cycle perspective. Self-reported poor health and severe disability at baseline are strongly correlated with death over the 14-year follow-up period. These variables also are strong predictors of disability program participation over the follow-up period among non-participants at baseline or before, with increasing marginal probabilities in the out-years. Our cross-sectional models are consistent with recent studies that find that the work-prevented measure is useful in modeling DI entry. However, once self-reported health and functional limitations are accounted for, the longitudinal entry models provide conflicting DI results for the work-prevented measure, suggesting that, contrary to claims based on cross-sectional or short-time horizon application models, the work-prevented measure is an unreliable indicator of severity. The risk of SSI and DI participation is significantly greater for individuals who die, suggesting that future mortality captures the effect of case severity and deterioration of health during the follow-up period. From a life-cycle perspective, a substantially greater proportion of individuals participate in SSI or DI at some point in their lives compared to typical cross-sectional estimates of participation, especially among minorities, people with less than a high school education, and those with early onset of poor health and/or disabilities. Cross-sectional estimates for the Social Security area population indicate SSI and DI participation rates of no more than 5% combined in 2000. In contrast, for individuals aged 43–48 in 1984, we observe a cumulative lifetime SSI and/or DI participation rate of 14%. The corresponding figure is 32% for individuals in that age group who did not graduate from high school, suggesting the need for human capital investments and/or improved work incentives.

The inclusion of heritage dwellings in the UK decarbonization policies can contribute to cut operational carbon emissions from the building stock; this needs to be made a priority if net zero carbon targets are to be achieved. However, the energy and carbon savings potential of suitable retrofit interventions on this part of the stock is extremely variable and strictly intertwined with the range of baseline conditions of such dwellings. This study aims to propose a framework for interventions in traditional listed dwellings (TLDs) to improve their energy performance utilizing dynamic energy simulation (DES) of selected case studies (CSs) in the city of Brighton and Hove (South-East England).

To achieve this aim, the study established a baseline scenario which provides a basis for the assessment of energy performance and thermo-hygrometric behaviour pre- and post-interventions and allows for comparison between different CSs under comparable conditions.

Presenting a brief overview of the methodology adopted in this study, the paper describes the approach devised to generate such baseline scenario. The paper then compares the results obtained from simulation of normalized and baseline models with the status-quo energy consumption of the dwellings investigated (based on meter readings).

This analysis finally allows to highlight some key physical determinants of the baseline HEC which, in the following stage of research, proved to have a considerable effect also on the amount of energy and carbon savings achievable post retrofit interventions.

The purpose of this paper is to address a possible interaction of cognitive distortions associated with substance dependency and intimate partner violence (IPV), and the effects on subsequent behavior. The primary focus was to investigate the relationship between offender perception (i.e. perception of family problems (FP) and perception of need for treatment for family problems (FPTx)) and treatment outcome (i.e. substance use and violence), among a unique sample of substance dependent male offenders of IPV. An additional investigation included the change in perception from baseline to the end of treatment.

In total, 63 participants were randomly assigned to one of two treatment conditions and assessed across 12 weeks of treatment.

Participants in the (FP+) (i.e. those who perceived family problems at baseline) and (FPTx+) (i.e. those who perceived a need for treatment for family problems at baseline) conditions reported a significantly greater change in the number of days of violence from baseline to the end of treatment, compared to participants in the (FP−) (i.e. those who did not perceive family problems at baseline) and (FPTx−) (i.e. participants who perceived no need for treatment at baseline) conditions. (FP+) and (FPTx+) participants had significant decreases in any violent behavior from pre- to post-treatment.

The results of this study highlight the importance of techniques aimed at improving clients’ ability to recognize and admit to problem behaviors, a critical component of cognitive-behavioral therapy, in an effort to increase their motivation for treatment, thus leading to greater treatment success.

Socioeconomic aims and impacts are an explicit part of the UK City of Culture (UKCoC) application, bidding, delivery and evaluation stages. This article engages with existing debates on evaluating cities of culture and introduces perspectives from critical data studies to examine the collection and analysis of different data for the purposes of the CoC application and evaluation processes.

The meta-methodological concept of accompanying researcher is used to analyse the experiences of researchers based within a city bidding for UKCoC 2025 in dialogue with the evaluation reports from past UKCoC host cities.

Findings are analysed under three themes: defining data morsels; local histories and infrastructures of data generation and sharing; and resources, capacities and expertise for data generation and evaluation. The discussion examines data still to be generated and/or brought into relation; tensions around data and measurement; and how constructing an evaluation baseline is generative—creating new organisations, relationships and practices.

The conceptual and methodological approach and empirical findings will be relevant for academic, policymakers and practitioners engaging with cultural evaluation.

In focussing on the bidding stage in real time through the accompanying researcher position, this article presents original empirical insights into the process of creating a baseline for cities of culture evaluation. The conceptual originality of this article is in using critical data studies to explain strategies of data generation and analyse data relations and frictions.

This paper seeks to describe a protocol to estimate annual community energy consumption baselines for single‐family detached homes in the Gainesville Regional Utility service area of Alachua County, Florida, USA. Further, it details methods using these baselines to make direct comparisons of individual households' energy consumption and evaluate the energy impacts of three prescriptive demand side management (DSM) programs.

To improve estimates of energy savings, the paper proposes using a “micro” scale multivariate regression methodology based on a census of utility and property appraiser household data.

Results suggest that traditional analysis approaches are likely to overestimate savings significantly and that the annual community baseline technique provides more consistent estimates of energy savings than most commonly used methods.

This type of analysis could provide a tool that utilities can use to more accurately and cost effectively measure DSM savings. This could result in reduced energy demand related to streamlined program setup and management.

The proposed methodology is unique in that it defines a new household‐level energy consumption baseline measure that we think is a more appropriate performance measure, uses a census of publicly available data for the population of interest, merging metered utility data with property appraiser data, and works upward to construct a simple model for evaluating household‐level energy consumption. The critical element that distinguishes our proposed energy performance measures is that they are calculated and interpreted using annual, population‐level, comparison‐group baselines that effectively normalize for community energy consumption patterns in any given year.

This study aims to provide the first longitudinal examination of the relationship between affective, calculative, normative commitment and customer loyalty by using longitudinal panel survey data.

Repeated measures for 269 customers of a large financial services provider are employed. Two types of segmentation methods are compared: predefined classes and latent class models and predictive power of different models contrasted.

The results reveal that the impact that different dimensions of commitment have on share development varies across segments. A two-segment latent class model and a managerially relevant predefined two-segment customer model are identified. In addition, the results demonstrate the benefits of using panel survey data in models that are designed to study how loyalty develops over time.

This study illustrates the benefits of including both baseline level information and changes in the dimensions of commitment in models that try to understand how loyalty unfolds over time. It also demonstrates how managers can be misled by assuming that everyone will react to commitment improvement efforts similarly. This study also shows how different segmentation schemes can be employed and reveals that the most sophisticated ones are not necessarily the best.

This research provides the first examination of models for change in customer loyalty by employing survey panel data on the three-component model of customer commitment (affective, calculative, and normative) and considers alternative segmentation methods.