Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Technologies in caregiving: professionals’ strategies for engaging with new technology

This article explores the adoption of new technology in organisations that provide senior citizen care. Inspired by Niklas Luhmann’s systems theory, we study how technology reduces complexity by identifying client needs and ensuring predictability in service delivery. However, how technologies are adopted in practice is not determined by technology since it is also structured by care-workers' continuous decision-making. Against this backdrop, we explore how technologies alter the conditions for decision-making in two settings of elderly care, and we describe how care workers seek to adapt technologies to their practical needs as well as conception of care ethics. Developing a systems theory approach, the article eschews a priori assumptions of technological constraint on care-workers’ professional autonomy, offering a more open-ended exploration of diversified strategies for coping with new technology. Our case studies show that employees develop diversified strategies for technology adoption, including both non-usage, heated resistance, excessive embrace, and creative adaption.     

Coordination in a skeptical two-group population

Journal of Economic Interaction and Coordination - This paper explores a situation in which a population split into two groups attempts to achieve the socially efficient outcome of a coordination...     

Change within purchasing and supply management organisations – Assessing the claims from maturity models

It is a wide-held assumption that professional development and change within purchasing and supply management (PSM) organisations can be explained and guided by a maturity model. In this paper the guidance which the maturity model concept offers to understand a PSM organisation's performance is assessed. The methodology is based on the outcomes of a literature review of PSM maturity models, development of an organisational change framework and the learning from three qualitative case studies. An alternative understanding of the development of the PSM organisation is offered through an organisational change framework, composing 1) movement transitions, 2) scalability of change, 3) acceptability of change, and 4) the substantive element of change. The research found that extant PSM maturity models are too rigid for PSM managers to apply, and although maturity models are commonly accepted in PSM literature, in practice, they may produce the opposite effect of what is promised. The PSM maturity models suggest that their application will lead to increased status and influence of PSM within the organisation; expectations that may not be met. PSM organisations’ change processes are subjected to a range of situational and contextual power relations which must be considered in order to advance the specific PSM organisation roles and responsibilities.     

From private label to co-branded credit card: an assessment of the impact of a change in value proposition over customer value perception and company value capture

Journal of Financial Services Marketing - Purpose: The aim of this paper is to evaluate the success of a value proposition over time, considering two aspects: customer’s perceived value and...     

Climate for conflict management,exposure to workplace bullying and work engagement: a moderated mediation analysis

The present study investigates a potential preventive factor in relation to workplace bullying. Specifically, we examine how climate for conflict management (CCM) may be related to less bullying, increased work engagement, as well as whether CCM is a moderator in the bullying engagement relationship. The study was based on a cross-sectional survey among employees in a transport company (N = 312). Hypotheses were tested simultaneously in a moderated mediation analysis which showed that bullying and job engagement were related (H1), CCM was related to less reports of bullying (H2), CCM was related to work engagement (H3) and that CCM was indirectly related to job engagement through bullying (H4), but only when CCM was weak (H5). That is, CCM moderated the relationship between bullying and work engagement in that this relationship only existed when CCM was low. The present study contributes to theory within this research field by showing that organizational measures may not only prevent bullying, but may also affect how employees react when subjected to bullying. Furthermore, the effect of climate in relation to bullying may be down to the narrow bandwidth facet of CCM. The study informs employers how they may act to prevent bullying while also reducing the potential negative outcomes of those cases of bullying that inevitably will show up from time to time.     

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.     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

Transition towards and of sustainability—Understanding sustainability as performative

Our purpose is to explore the concept of “sustainability” when understood from a performative perspective, i.e. as a concept that is filled with meaning across time. Drawing on a 10 year-long study of the digital footprint of Stockholm Royal Seaport, claimed to be northern Europe's largest sustainable urban development district, we show that “sustainability” emerged as the project became associated with particular places, projects, histories, and technologies. This means that “sustainability” was local in that it was situated in the particular spatial context of the project; temporal in that it was situated in a particular time; and political in that it expressed particular values and perspectives. The study contributes to explaining why “sustainability” remains—and always will remain—a contested concept, which is why sustainability transitions are complex. Consequently, we suggest that the transition towards sustainability always involves the transition of sustainability, something that needs to be acknowledged in order for a transition to actually become sustainable.