Reference points for measuring social performance: Case study of a social business venture

We present a longitudinal qualitative case study to elaborate on how a social venture forms reference points for social performance. Although organizations increasingly use various social performance targets to direct their operations, the scholarly knowledge on social performance reference points remains limited. We make use of the prior accounting literature and draw on the idea of compromising accounts to discuss how provisional and performative metrics can have a significant role in how organizations develop new ways to evaluate their social performance. Given that the social performance reference point criteria are ambiguous and the corresponding referents malleable, performative accounts are helpful as they can intervene in the organizational life by making particular things visible, providing space for interpretations, and facilitating discussion, thus creating temporary settlements and enabling opportunities for productive compromises between different organizational groups and evaluative principles. The recursive feedback loops between reference point referents, criteria and accounting artefacts help the organization to make sense of its own social performance and interpret the associated performance feedback, and thereby provide ground for organizational decisions on further action. Moreover, we discuss how imperfect accounts can be useful for social businesses in their pursuit of developing their activities and achieving social impact.     

Does educational diversity of managers matter for the performance of team‐managed funds?

We study the effect of the educational diversity of managers on the performance of team‐managed mutual funds using a large sample of U.S. equity funds from 1994 to 2013. We consider diversity in terms of both final educational degree and field of educational specialisation. We find that, in general, both types of diversity have a positive impact on fund performance, and our results are robust over a wide range of performance metrics and changes in market conditions.     

Introduction to symposium: Smallholders in communist and postcommunist societies

The primary contributions of smallholders during the communist and early postcommunist periods have been food production and labour for large farms. Those conditions are changing, however, as modern farms require less labour and food supply may be imported. For most smallholders in Central and Eastern European and former Soviet Union countries, the postcommunist neoliberal environment has not brought significant improvement, and strong arguments can be made that land grabbing, social and economic exclusion, and rural poverty are worse than regime bias during the communist period. Cooperatives, which have empowered smallholders in other parts of the world, have not been as well developed in postcommunist nations.     

Multi-level and mixed-level <Emphasis Type="Italic">k</Emphasis>-circulant supersaturated designs

Supersaturated designs (SSDs) constitute an important class of fractional factorial designs that could be extremely useful in factor screening experiments. Most of the existing studies have focused on balanced designs. This paper provides a new lower bound for the \(E(f_{NOD})\)-optimality measure of SSDs with general run sizes. This bound is a generalization of existing bounds since it is applicable to both balanced and unbalanced designs. Optimal multi and mixed-level, balanced and nearly balanced SSDs are constructed by applying a k-circulant type methodology. Necessary and sufficient conditions are introduced for the generator vectors, in order to pre-ensure the optimality of the constructed k-circulant SSDs. The provided lower bounds were used to measure the efficiency of the generated designs. The presented methodology leads to a number of new families of improved SSDs, providing tools for directly constructing optimal or nearly-optimal k-circulant designs by just checking the corresponding generator vector.     

Machine learning for financial transaction classification across companies using character-level word embeddings of text fields

An important initial step in accounting is mapping financial transfers to the corresponding accounts. We devised machine-learning-based systems that automate this process. They use word embeddings with character-level features to process transaction texts. When considering 473 companies independently, our approach achieved an average top-1 accuracy of 80.50%, outperforming baselines that exclude the transaction texts or rely on a lexical bag-of-words text representation. We extended the approach to generalizes across companies and even across different corporate sectors. After standardization of the account structures and careful feature engineering, a single classifier trained on 44 companies from 28 sectors achieved a test accuracy of more than 80%. When trained on 43 companies and tested on the remaining one, the system achieved an average performance of 64.62%. This rate increased to nearly 70% when considering only the largest sector.     

Testing the Empirical Relevance of the ‘Saving for a Rainy Day’ Hypothesis in US Metro Areas

The joint implication of the consumption Euler equation and cointegration between income and consumption is that savings predict future income declines, the ‘saving for a rainy day’ hypothesis. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers, and it holds under the null that the permanent income hypothesis is true. We find little support for this hypothesis using time series data for the 100 largest US Metropolitan Statistical Areas for the period 1980q1–2015q4. Our approach is to test for cointegration and weak exogeneity between income and consumption, and by exploring the direction of Granger causality between the two time series. We find that income more often predicts consumption and saving than the converse. We also give evidence that house price changes played a role in US income and consumption dynamics, before, during and after the Great Recession.     

Revisiting the expatriate failure concept: A qualitative study of Scandinavian expatriates in Hong Kong

This article advances the understanding of expatriate failure, which remains a contested social phenomenon in international work life as well as scholarly research. The study challenges the definition of expatriate failure and its inherent biases, i.e., the epistemological primacy of the firm level and the failure/success binary. We argue that this qualitative study of 51 Scandinavian expatriates in Hong Kong can contribute to advancing theory on the expatriate failure concept by asking individual expatriates what constitutes failure to them. By applying social constructionist and social anthropological ideas to the expatriate failure concept debate, we develop the internationality thesis which demonstrates a discrepancy between the expatriates’ perceptions of successful international assignments and the actual nature of their lived lives; many expatriates desire to enrich their lives through experiencing an international/intercultural and adventurous lifestyle, but, in fact, living lives with limited intercultural exposure and interaction. We conclude by proposing a reconceptualisation of expatriate failure in terms of offering both a new definition and approach to researching expatriate failure in which time/duration, context, and geographical location need to be taken into account. We believe the new approach can overcome some of the empirical unsoundness of mainstream definitions.     

Machine Learning and Expert Judgement: Analyzing Emerging Topics in Accounting and Finance Research in the Asia–Pacific

In this paper, we focus on the question to what extent machine learning (ML) tools can be used to support systematic literature reviews. We apply a ML approach for topic detection to analyze emerging topics in the literature—our context is accounting and finance research in the Asia–Pacific region. To evaluate the robustness of the approach, we compare findings from the automated ML approach with the results from a manual analysis of the literature. The automated approach uses a keyword algorithm detection mechanism whereby the manual analysis uses common techniques for qualitative data analysis, that is, triangulation between researchers (expert judgement). From our paper, we conclude that both methods have strengths and weaknesses. The automated analysis works well for large corpora of text and provides a very standardized and non-biased way of analyzing the literature. However, the human researcher is potentially better equipped to evaluate current issues and future trends in the literature. Overall, the best results might be achieved when a variety of tools are used together.     

Predicting Household Poverty: A Methodological Note with a Kenyan Example

Household surveys provide data that is used for identifying and measuring the poverty status of households and individuals. However, carrying out such surveys is expensive, especially in poor developing countries. Thus it is important to make maximum use of the available survey data in developing countries, especially in sub‐Saharan Africa, where such data are expensive to collect and analyse. This paper develops a simple method for using poverty indices derived from survey data for a given year, to predict poverty rates for subsequent periods without having to conduct a new household survey. We illustrate the workings of the method with data from Kenyan household surveys for 1994 and 1997.