Development and implementation of customer solutions: A study of process dynamics and market shaping

A broad, dynamic network perspective on solution processes remains scarce. This article presents the process of developing and implementing customer solutions and its effects on the wider business environment by investigating customers and suppliers in the global mining industry (Australia, Chile, and Sweden), analyzing the deployment of a new customer solution, and assessing the changes to the competitive environment and focal firms' relationships with other customers and suppliers. It shows that the forces that drive customer and supplier interests and motivation to co-develop customer solutions may change over time, thus redefining the aim and scope of solutions and creating failure risks. Customers present problems; suppliers respond, on the basis of not only the feasibility of the customer-specific solution but also of their evaluation of future solutions in a broader market; then suppliers aim to standardize successful solutions across markets. Customers want close supplier relationships and unique solutions but also like standardized and repeatable solutions, so they can share development costs with competitors and expose the supplier to competition to avoid lock-in effects. From a network perspective, a novel solution can have a market-shaping effect and evoke reactions from other actors who want to enhance their market position. However, these changes are not necessarily deliberate, and the dynamics that market introductions of solutions trigger may be difficult to predict.     

Business model innovation and decision making: uncovering mechanisms for coping with uncertainty

As a research subject, business model innovation spans the strategy, innovation, and entrepreneurship fields. Yet, despite the importance of the concept, prior work has paid little attention to how decision-makers cope with uncertainty and gain understanding about interdependencies in new business model configurations. To address this gap, we combine top-down theorising and evidence-based exploration and seek to unpack some of the coping mechanisms that operate in the evolutionary view of business model innovation. Using in-depth interviewing to collect data, our study reveals five strategies – customer centricity, value co-creation, capability evolution, ecosystem growth, and adaptive pricing – that decision-makers apply to cope with uncertainty in business model innovation. We find that coping mechanisms support decision making during the development of new business models. Furthermore, we find that the five coping strategies delineate decision making for value proposition, value creation, and value capture configurations in more detail than existing literature has described. Our findings have important implications for decision making in business model innovation.     

Regulations for the Eighth International Congress of Actuaries

Abstract

The present paper proposes and investigates a procedure for numerical evaluation of the transition probabilities for a time-inhomogeneous Markov process when the intensities are known (estimated). The procedure is based on Taylor-expansion of the transition probabilities linked with the Chapman-Kolmogorov equations.     

Asymptotic results for the risk process based on marked point processes

Abstract

In this paper asymptotic properties for the risk process will be studied when the number of risk units tends to infinity. The paper extends asymptotic properties for the classical risk process to more general processes. In the classical risk process the claim amounts are assumed independent and identically distributed, and the claim number process is a homogeneous Poisson process.

The key tool is point process theory with associated martingale theory. The results are illustrated by examples.     

Intraday data and hedging efficiency in interest spread trading

Three government bond futures contracts and their respective 3-month interest rate futures contracts traded on LIFFE are examined. The data period covers three years of observations, January 1994-December 1996, sampled at half-hourly intervals. Borrowing from the calculation of minimum variance hedge ratios, half-hourly minimum variance spread ratios (the ratio of one contract to another, which provides the minimum variance) are estimated for the above contracts. The hypothesis under examination is whether there is any value-added in estimating minimum spread ratios based on intraday data. Three spread ratios are defined: two ratios calculated from daily data and a third one based on intraday data. Evidence tends to indicate that spread ratios calculated from intraday data exhibit a substantially lower variance than the other two spread ratio speciications. Thus, it is shown that intraday data, in comparison with daily data, allow for lower hedging costs. Moreover, the use of intraday-based spread ratios might be a contributing factor to reducing the maximum cumulative loss potentially incurred while holding a spread position.     

Simulation-based pricing of convertible bonds

We propose and empirically investigate a pricing model for convertible bonds based on Monte Carlo simulation. The method uses parametric representations of the early exercise decisions and consists of two stages. Pricing convertible bonds with the proposed Monte Carlo approach allows us to better capture both the dynamics of the underlying state variables and the rich set of real-world convertible bond specifications. Furthermore, using the simulation model proposed, we present an empirical pricing study of the US market, using 32 convertible bonds and 69 months of daily market prices. Our results do not confirm the evidence of previous studies that market prices of convertible bonds are on average lower than prices generated by a theoretical model. Similarly, our study is not supportive of a strong positive relationship between moneyness and mean pricing error, as argued in the literature.     

Economic and financial crises and the predictability of U.S. stock returns

We argue that the use of publicly available and easily accessible information on economic and financial crises to detect structural breaks in the link between stock returns and macroeconomic predictor variables improves the performance of simple trading rules in real time. In particular, our results suggest that accounting for structural breaks and regime shifts in forecasting regressions caused by economic and financial crises has the potential to increase the out-of-sample predictability of stock returns, the performance of simple trading rules, and the market-timing ability of an investor trading in the U.S. stock market.     

Task‐based structures in open source software: revisiting the onion model

Studies on Open Source Software (OSS) developer communities have long stated that there is a relationship between community structure and tasks carried out by project members. This relationship has been exemplified by the onion model, which has been instrumental in understanding self‐coordination in OSS projects. Despite its ubiquity, there is a lack of empirical evidence to validate the relative position of each task cluster within the onion model. In this study, we map out the community structure of a large open source project and observe its bug‐fixing patterns to explore the relationship between tasks and structure. Our study makes three significant contributions. First, we find no empirical evidence to support the structural location of bug‐fixing tasks in the onion structure. Second, we find empirical evidence to support the core‐periphery continuum model linking an actor’s coreness to problem‐solving ability. Third, our results suggest that the importance and location of each task within the core‐periphery structure evolve over time. These findings add clarity to the community structure and their implications for the management and coordination of collaborative innovation projects.