Marketing responsibility in an era of economic and climatic challenge

Purpose – The purpose of this paper is to illustrate Michael Thomas's concept of civic professionalism and social trusteeship as a future alternative to the current marketing profession's code of conduct and to put this in the context of climate change and ecological sustainability as a model for firms everywhere. Design/methodology/approach – Review of the marketing profession's responsibility towards society, communities and the ecology of the planet in the twenty-first century in the light of climate change. Findings – The hypothesis for the paper emerges as: whether it is possible for Chinese firms to embrace the needs of twenty-first century global ecological sustainability in meeting their own economic requirements for development and financial prosperity. Research limitations/implications – Limited secondary research and primary research that is also limited in terms of scope. Practical implications – As we move into an era of Chinese economic supremacy, we marketers must face up to the responsibility we have towards balancing the progression of global economic development (and selling goods and services in global market systems) with our responsibility towards our cultural systems and the global ecological system (the global ecosystem), the home of all our economic wealth. Social implications – To extrapolate lessons and opportunities for firms from developing economies as they move towards global domination of world economic markets and, suggest strategies for sustainability that they can, and should, adopt. Originality/value – The paper presents a theoretical framework for a global strategy for sustainability, and provides a vision of marketing responsibility that embraces civic professionalism, social trusteeship and a strategy for sustainability.

Regression with input-dependent noise: A Bayesian treatment

In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.

Dynamic variability in adaptive systems

One of the reasons for using variability in the software product line (SPL) approach (see Apel et al., 2006; Figueiredo et al., 2008; Kastner et al., 2007; Mezini & Ostermann, 2004) is to delay a design decision (Svahnberg et al., 2005). Instead of deciding on what system to develop in advance, with the SPL approach a set of components and a reference architecture are specified and implemented (during domain engineering, see Czarnecki & Eisenecker, 2000) out of which individual systems are composed at a later stage (during application engineering, see Czarnecki & Eisenecker, 2000). By postponing the design decisions in such a manner, it is possible to better fit the resultant system in its intended environment, for instance, to allow selection of the system interaction mode to be made after the customers have purchased particular hardware, such as a PDA vs. a laptop. Such variability is expressed through variation points which are locations in a software-based system where choices are available for defining a specific instance of a system (Svahnberg et al., 2005). Until recently it had sufficed to postpone committing to a specific system instance till before the system runtime. However, in the recent years the use and expectations of software systems in human society has undergone significant changes.Today's software systems need to be always available, highly interactive, and able to continuously adapt according to the varying environment conditions, user characteristics and characteristics of other systems that interact with them. Such systems, called adaptive systems, are expected to be long-lived and able to undertake adaptations with little or no human intervention (Cheng et al., 2009). Therefore, the variability now needs to be present also at system runtime, which leads to the emergence of a new type of system: adaptive systems with dynamic variability.