Building dynamic capabilities of adaptation and innovation: a study of micro-foundations in a transition economy

What are the microfoundations of dynamic capabilities that sustain competitive advantage in a highly volatile environment, such as a transition economy? We explore the detailed nature of these dynamic capabilities along with their antecedents by tracing the sequence of their development based on a longitudinal case study of an organization subject to an external context of radical transition — the Russian oil company, Yukos. Our rich qualitative data indicate two distinct types of dynamic capabilities that are pivotal for organizational transformation. Adaptation dynamic capabilities relate to routines of resource exploitation and deployment, which are supported by acquisition, internalization and dissemination of extant knowledge, as well as resource reconfiguration, divestment and integration. Innovation dynamic capabilities relate to the creation of completely new capabilities via exploration and path-creation processes, which are supported by search, experimentation and risk taking, as well as project selection, funding and implementation. Second, we find that sequencing the two types of dynamic capabilities, helped the organization both to secure short-term competitive advantage, and to create the basis for long-term competitive advantage. These dynamic capability constructs advance theoretical understanding of what dynamic capabilities are, whilst their sequencing explains how firms create, leverage and enhance them over time.

A dynamic mechanistic model of lactic acid metabolism in the rumen

Current feed evaluation systems for ruminants are too imprecise to describe diets in terms of their acidosis risk. The dynamic mechanistic model described herein arises from the integration of a lactic acid (La) metabolism module into an extant model of whole-rumen function. The model was evaluated using published data from cows and sheep fed a range of diets or infused with various doses of La. The model performed well in simulating peak rumen La concentrations (coefficient of determination = 0.96; root mean square prediction error = 16.96% of observed mean), although frequency of sampling for the published data prevented a comprehensive comparison of prediction of time to peak La accumulation. The model showed a tendency for increased La accumulation following feeding of diets rich in nonstructural carbohydrates, although less-soluble starch sources such as corn tended to limit rumen La concentration. Simulated La absorption from the rumen remained low throughout the feeding cycle. The competition between bacteria and protozoa for rumen La suggests a variable contribution of protozoa to total La utilization. However, the model was unable to simulate the effects of defaunation on rumen La metabolism, indicating a need for a more detailed description of protozoal metabolism. The model could form the basis of a feed evaluation system with regard to rumen La metabolism.

Anticipating future risk in social-ecological systems using fuzzy cognitive mapping: the case of wildfire in the Chiquitania, Bolivia

Understanding complex social-ecological systems, and anticipating how they may respond to rapid change, requires an approach that incorporates environmental, social, economic, and policy factors, usually in a context of fragmented data availability. We employed fuzzy cognitive mapping (FCM) to integrate these factors in the assessment of future wildfire risk in the Chiquitania region, Bolivia. In this region, dealing with wildfires is becoming increasingly challenging due to reinforcing feedbacks between multiple drivers. We conducted semi-structured interviews and constructed different FCMs in focus groups to understand the regional dynamics of wildfire from diverse perspectives. We used FCM modelling to evaluate possible adaptation scenarios in the context of future drier climatic conditions. Scenarios also considered possible failure to respond in time to the emergent risk. This approach proved of great potential to support decision-making for risk management. It helped identify key forcing variables and generate insights into potential risks and trade-offs of different strategies. All scenarios showed increased wildfire risk in the event of more droughts. The ‘Hands-off’ scenario resulted in amplified impacts driven by intensifying trends, affecting particularly the agricultural production. The ‘Fire management’ scenario, which adopted a bottom-up approach to improve controlled burning, showed less trade-offs between wildfire risk reduction and production compared to the ‘Fire suppression’ scenario. Findings highlighted the importance of considering strategies that involve all actors who use fire, and the need to nest these strategies for a more systemic approach to manage wildfire risk. The FCM model could be used as a decision-support tool and serve as a ‘boundary object’ to facilitate collaboration and integration of different forms of knowledge and perceptions of fire in the region. This approach has also the potential to support decisions in other dynamic frontier landscapes around the world that are facing increased risk of large wildfires.

Review of the literature on credit risk modeling : development of the past 10 years

This paper traces the developments of credit risk modeling in the past 10 years. Our work can be divided into two parts: selecting articles and summarizing results. On the one hand, by constructing an ordered logit model on historical Journal of Economic Literature (JEL) codes of articles about credit risk modeling, we sort out articles which are the most related to our topic. The result indicates that the JEL codes have become the standard to classify researches in credit risk modeling. On the other hand, comparing with the classical review Altman and Saunders(1998), we observe some important changes of research methods of credit risk. The main finding is that current focuses on credit risk modeling have moved from static individual-level models to dynamic portfolio models.

Integrating insurance services, trust and risk mechanisms into multi-agent systems

In multi-agent systems, there is often the need for an agent to cooperate with others so as to ensure that a given task is achieved timely and cost effectively. Currently multi-agent systems maximize this through mechanisms such as coalition formation, trust and risk assessments, etc. In this paper, we incorporate the concept of insurance with trust and risk mechanisms in multi-agent systems. The novelty of this proposal is that it ensures continuous sharing of resources while encouraging expected utility to be maximized in a dynamic environment. Our experimental results confirm the feasibility of our approach.

Trading in open marketplace using trust and risk

In this paper, we propose buying and selling models for agents to trade in the open multi-agent marketplace. Unlike auctions, we take into account of the fact that agents trading in such open environments has to maximize their profits and at the same time, protect themselves from fraud and deception. We attempt to address this issue by incorporating the element of trust and risk management into our proposed buying and selling model. During buying, agents learn to select their partners based on the trustworthiness of the potential partner as well as its personal risk attitude. During selling, agents learn to increase the chances of winning a deal by adjusting their profit rate, which is a measure that considers both trust and risk. The novelty of this proposal is that it ensures agents continuing to seek maximum expected utility in a dynamic trading environment. Our experimental results confirm the feasibility of our approach.

Fire fighters' assessments of fireground risk

Fire fighters are often required to work in dynamic and hazardous environments involving a high level of uncertainty. The present study investigated 110 volunteer fire fighters’ assessments of levels of risk associated with a photographic depiction of a typical grassland fire situation. The fire fighters used a standard fire agency risk-rating matrix procedure requiring them to specify the severity of the hazards depicted and the probability of a mishap in order to rate overall level of risk (1 = Low; 4 = Extreme). The risk ratings made by the fire fighters varied greatly. The overall rate of agreement with the risk level rating of the situation made by a panel of expert fire officers (=1, Low) was only 27%. It seems that use of a standard risk-rating matrix procedure by fire fighters at incidents, as recommended currently by many fire agencies, is likely to result in unreliable risk assessments, at least in the absence of effective training in the risk assessment procedure. The 110 volunteers were also asked to identify the total number of potential hazards apparent in five photographs depicting different kinds of emergency incidents. Identifying more hazards was found to be associated with (a) previous personal experience of a ‘near-miss’; and (b) higher levels of education. The findings imply that when faced with identical fire ground situations, individual fire fighters are likely to differ in their situational awareness of hazards and consequent risk assessments.

Mental health-related risk factors for violence : using the evidence to guide mental health triage decision making

Mental health clinicians working in emergency crisis assessment teams or mental health triage roles are required to make rapid and accurate risk assessments. The assessment of violence risk at triage is particularly pertinent to the early identification and prevention of patient violence, and to enhancing the safety of clinical staff and the general public. To date, the evidence base for mental health triage violence risk assessment has been minimal. This study aimed to address this evidence gap by identifying best available evidence for mental health-related risk factors for patientinitiated violence.We conducted a systematic review based on the National Health and Medical Research Council of Australia’s methodology for systematic reviews. A total of 6847 studies were retrieved, of which 326 studies met the study inclusion criteria. Of these studies, 277 met inclusion criteria but failed the quality appraisal process, thus a total of 49 studies were included in the final review. The risk factors that achieved the highest evidence grading were predominantly related to dynamic clinical factors immediately observable in the patient’s general appearance, behaviour and speech. These factors included hostility/anger, agitation, thought disturbance, positive symptoms of schizophrenia, suspiciousness and irritability.

He did what? Well that wasn't handed over! Communicating risk in mental health

Mental health inpatient units are dynamic, complex environments that provide care for patients with heterogeneous ages, diagnoses and levels of acuity. These environments commonly expose clinicians and patients to many potential risks. Despite extensive research into risk assessment, prediction and management, no study has investigated how risk information is communicated at handover in acute mental health settings. Given the pivotal role handover plays in informing risk management, this evidence gap is significant. This paper reports on a study that investigated the practices of communicating risk at handover in an Australian acute mental health inpatient unit. The aim of this research was to identify the frequency and type of risk information communicated between nursing shifts, and the methods by which this communication was performed. A secondary aim was to identify effective and ineffective risk communication practices. This study involved an observational design method using a 14-item Clinical Audit Tool derived from handover principles outlined by World Health Organization. Five hundred occasions of patient handover were observed. Few risk information items were observed to be communicated in any method. Risk communication practice was inconsistent, and a key recommendation from the study is the use of standardized handover tools that ensures risk information is adequately reported.

Dynamic currency hedging for international stock portfolios

The paper studies dynamic currency risk hedging of international stock portfolios using a currency overlay. A dynamic conditional correlation (DCC) multivariate GARCH model is employed to estimate time-varying covariance among stock market returns and currency returns. The conditional covariance is then used in the estimation of risk-minimizing conditional hedge ratios. The study considers seven developed economies over the period January 2002 to April 2010 and estimates daily conditional hedge ratios for portfolios of various stock market combinations. Conditional hedging is shown to dominate traditional static hedging and unconditional hedging in terms of risk reduction both in-sample and out-of-sample, especially during the recent global financial crisis. Conditional hedging also proves to consistently reduce portfolio risk for various levels of foreign investments.

Analysing stillbirth data using dynamic self organizing maps

Even with the presence of modern obstetric care, stillbirth rate seems to stay stagnant or has even risen slightly in countries such as England and has become a significant public health concern [1]. In the light of current medical research, maternal risk factors such as diabetes and hypertensive disease were identified as possible risk factors and are taken into consideration in antenatal care. However, medical practitioners and researchers suspect possible relationships between trends in maternal demographics, antenatal care and pregnancy information of current stillbirth in consideration [2]. Although medical data and knowledge is available appropriate computing techniques to analyze the data may lead to identification of high risk groups. In this paper we use an unsupervised clustering technique called Growing Self organizing Map (GSOM) to analyse the stillbirth data and present patterns which can be important to medical researchers.

A systematic evaluation of field-based screening methods for the assessment of anterior cruciate ligament (ACL) injury risk

BACKGROUND: Laboratory-based measures provide an accurate method to identify risk factors for anterior cruciate ligament (ACL) injury; however, these methods are generally prohibitive to the wider community. Screening methods that can be completed in a field or clinical setting may be more applicable for wider community use. Examination of field-based screening methods for ACL injury risk can aid in identifying the most applicable method(s) for use in these settings. OBJECTIVE: The objective of this systematic review was to evaluate and compare field-based screening methods for ACL injury risk to determine their efficacy of use in wider community settings. DATA SOURCES: An electronic database search was conducted on the SPORTDiscus™, MEDLINE, AMED and CINAHL databases (January 1990-July 2015) using a combination of relevant keywords. A secondary search of the same databases, using relevant keywords from identified screening methods, was also undertaken. STUDY SELECTION: Studies identified as potentially relevant were independently examined by two reviewers for inclusion. Where consensus could not be reached, a third reviewer was consulted. Original research articles that examined screening methods for ACL injury risk that could be undertaken outside of a laboratory setting were included for review. STUDY APPRAISAL AND SYNTHESIS METHODS: Two reviewers independently assessed the quality of included studies. Included studies were categorized according to the screening method they examined. A description of each screening method, and data pertaining to the ability to prospectively identify ACL injuries, validity and reliability, recommendations for identifying 'at-risk' athletes, equipment and training required to complete screening, time taken to screen athletes, and applicability of the screening method across sports and athletes were extracted from relevant studies. RESULTS: Of 1077 citations from the initial search, a total of 25 articles were identified as potentially relevant, with 12 meeting all inclusion/exclusion criteria. From the secondary search, eight further studies met all criteria, resulting in 20 studies being included for review. Five ACL-screening methods-the Landing Error Scoring System (LESS), Clinic-Based Algorithm, Observational Screening of Dynamic Knee Valgus (OSDKV), 2D-Cam Method, and Tuck Jump Assessment-were identified. There was limited evidence supporting the use of field-based screening methods in predicting ACL injuries across a range of populations. Differences relating to the equipment and time required to complete screening methods were identified. LIMITATIONS: Only screening methods for ACL injury risk were included for review. Field-based screening methods developed for lower-limb injury risk in general may also incorporate, and be useful in, screening for ACL injury risk. CONCLUSIONS: Limited studies were available relating to the OSDKV and 2D-Cam Method. The LESS showed predictive validity in identifying ACL injuries, however only in a youth athlete population. The LESS also appears practical for community-wide use due to the minimal equipment and set-up/analysis time required. The Clinic-Based Algorithm may have predictive value for ACL injury risk as it identifies athletes who exhibit high frontal plane knee loads during a landing task, but requires extensive additional equipment and time, which may limit its application to wider community settings.

DeepCare: a deep dynamic memory model for predictive medicine

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, recorded in electronic medical records, are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors in space, models patient health state trajectories through explicit memory of historical records. Built on Long Short-Term Memory (LSTM), DeepCare introduces time parameterizations to handle irregular timed events by moderating the forgetting and consolidation of memory cells. DeepCare also incorporates medical interventions that change the course of illness and shape future medical risk. Moving up to the health state level, historical and present health states are then aggregated through multiscale temporal pooling, before passing through a neural network that estimates future outcomes. We demonstrate the efficacy of DeepCare for disease progression modeling, intervention recommendation, and future risk prediction. On two important cohorts with heavy social and economic burden -- diabetes and mental health -- the results show improved modeling and risk prediction accuracy.

Essays in macroeconomy and dynamic term-structure models

This thesis is composed of three articles with the subjects of macroeconomics and - nance. Each article corresponds to a chapter and is done in paper format. In the rst article, which was done with Axel Simonsen, we model and estimate a small open economy for the Canadian economy in a two country General Equilibrium (DSGE) framework. We show that it is important to account for the correlation between Domestic and Foreign shocks and for the Incomplete Pass-Through. In the second chapter-paper, which was done with Hedibert Freitas Lopes, we estimate a Regime-switching Macro-Finance model for the term-structure of interest rates to study the US post-World War II (WWII) joint behavior of macro-variables and the yield-curve. We show that our model tracks well the US NBER cycles, the addition of changes of regime are important to explain the Expectation Theory of the term structure, and macro-variables have increasing importance in recessions to explain the variability of the yield curve. We also present a novel sequential Monte-Carlo algorithm to learn about the parameters and the latent states of the Economy. In the third chapter, I present a Gaussian A ne Term Structure Model (ATSM) with latent jumps in order to address two questions: (1) what are the implications of incorporating jumps in an ATSM for Asian option pricing, in the particular case of the Brazilian DI Index (IDI) option, and (2) how jumps and options a ect the bond risk-premia dynamics. I show that jump risk-premia is negative in a scenario of decreasing interest rates (my sample period) and is important to explain the level of yields, and that gaussian models without jumps and with constant intensity jumps are good to price Asian options.