A Staged Service Innovation Model*

Drawing from the new product development (NPD) literature, service quality literature (SERVQUAL), and empirically grounded research with 53 service innovation decision makers, we develop a staged service innovation model (SIM) for decision makers. We tested the model using empirical data from 329 firms across five industries. The empirical results show that integrating prelaunch service quality training into new service development process leads to successful service innovation. The model developed in this article can be used as a decision support tool and diagnostic model for assessing service innovation ideas, evaluating performance of ongoing service innovations, allocating resources, and improving success rate of service innovations. Decision makers can use the measures developed in this study as a checklist to identify their strengths in delivering service quality to their own customers as well as areas of improvement. This article extends service innovation research by combining NPD and service quality development into a single study and opens the door to further work that could help improve the success rate of service innovations. The model can serve as a base model for future research extensions in service innovation research. A major takeaway for the academic reader is that the SIM demonstrates the value of using the SERVQUAL literature to understand how best to provide excellent quality that results in more fully satisfied customers and, ultimately, improved service performance.     

Information technology, unions, and the new organization: Challenges and opportunities for union survival

The rapid transformation of the U.S. business environment has radically changed the character of organizations where unions seek to represent workers. Many of the structural elements that have provided critical support to union representation in collective bargaining have been significantly altered, or lost altogether. We have argued that information technologies have been a pivotal force of change in business organizations and have identified a set of key structural elements of these transformed organizations. It is our belief that while the new organization creates significant challenges for private sector unions, it also creates an opportunity for unions to develop new roles and new opportunities to prosper in the next millennium.     

Violent pasts: collective memory and football hooliganism

Since the 1970s, the sociological analysis of football hooliganism has focused on the processes which lead to violence between fans. This has been a reasonable research strategy since the incidence of violence is a striking phenomenon and violent fans themselves look upon violence as the objective. However, this focus on the causes of violence has cast other important aspects of football violence into the shade. In particular, there has been a lack of consideration of the way in which violence has been used as a resource by violent fans for the creation and re‐creation of their hooligan gangs. In particular, there has been no discussion of the way in which the collective memory of violence, established in discussions between group members, affirms the solidarity of these groups. In exploring the way that shared memory is employed by violent fans to sustain their relations with each other, this article widens the focus of the sociological study of hooliganism but also makes a contribution to the understanding of how social groups are constituted more generally through the empirical example of hooliganism.     

Social Acceleration Theory and the Self

In recent years, there has been a rapidly growing body of work in the social sciences that underscores the prevalence of the phenomenon of ‘social acceleration'—the speeding up of social life— in many parts of the Western world. Although research on social acceleration has tended to analyze the phenomenon on a social‐structural level, there is also a need to investigate how social acceleration has ‘ramifications for the socially dominant forms of self‐relation’. One way to gain a more in‐depth understanding of this facet of social acceleration is to investigate the speeding up of social life through the prism of the self. The central argument of this article is that there are at least five images of the self which can be associated with the social acceleration phenomenon: (1) the ‘detached’ self, (2) the ‘reflexive’ self, (3) the ‘reinventive’ self, (4) the ‘stationary’ self and (5) the ‘decelerating’ self. By explaining how these kinds of self relate to the speeding up process, we seek to advance a more nuanced and sophisticated theory of social acceleration, which captures some of the complexities and paradoxes that the phenomenon involves.     

What's Wrong with Risk Matrices?

Risk matrices—tables mapping "frequency" and "severity" ratings to corresponding risk priority levels—are popular in applications as diverse as terrorism risk analysis, highway construction project management, office building risk analysis, climate change risk management, and enterprise risk management (ERM). National and international standards (e.g., Military Standard 882C and AS/NZS 4360:1999) have stimulated adoption of risk matrices by many organizations and risk consultants. However, little research rigorously validates their performance in actually improving risk management decisions. This article examines some mathematical properties of risk matrices and shows that they have the following limitations. (a) Poor Resolution . Typical risk matrices can correctly and unambiguously compare only a small fraction (e.g., less than 10%) of randomly selected pairs of hazards. They can assign identical ratings to quantitatively very different risks ("range compression"). (b) Errors . Risk matrices can mistakenly assign higher qualitative ratings to quantitatively smaller risks. For risks with negatively correlated frequencies and severities, they can be "worse than useless," leading to worse-than-random decisions. (c) Suboptimal Resource Allocation . Effective allocation of resources to risk-reducing countermeasures cannot be based on the categories provided by risk matrices. (d) Ambiguous Inputs and Outputs . Categorizations of severity cannot be made objectively for uncertain consequences. Inputs to risk matrices (e.g., frequency and severity categorizations) and resulting outputs (i.e., risk ratings) require subjective interpretation, and different users may obtain opposite ratings of the same quantitative risks. These limitations suggest that risk matrices should be used with caution, and only with careful explanations of embedded judgments.     

A nonlinear optimization methodology for VLSI fixed-outline floorplanning

Floorplanning is a critical step in the physical design of VLSI circuits. The floorplanning optimization problem can be formulated as a global optimization problem minimizing wire length, with the area of each rectangular module fixed while the module’s height and width are allowed to vary subject to aspect ratio constraints. While classical floorplanning seeks to simultaneously minimize the wire length and the area of the floorplan without being constrained by a fixed outline for the floorplan, state-of-the-art technologies such as System-On-Chip require the solution of fixed-outline floorplanning. Fixing the outline of the floorplan makes the problem significantly more difficult. In this paper, we propose a two-stage nonlinear-optimization-based methodology specifically designed to perform fixed-outline floorplanning by minimizing wire length while simultaneously enforcing aspect ratio constraints on soft modules and handling a zero deadspace situation. In the first stage, a convex optimization globally minimizes an approximate measure of wire length. Using the solution of the first stage as a starting point, the second stage minimizes the wire length by sizing the modules subject to the prescribed aspect ratios, and ensuring no overlap. Computational results on standard benchmarks demonstrate that the model is competitive with other floorplanning approaches in the literature. Research supported by a Postgraduate Award, Discovery Grants 312125 and 15296, and RTI Grant 314668 from the Natural Sciences and Engineering Research Council of Canada, by an Ontario Graduate Scholarship, and by a MITACS grant.     

Symmetry, Identifiability, and Prediction Uncertainties in Multistage Clonal Expansion (MSCE) Models of Carcinogenesis

Many models of exposure-related carcinogenesis, including traditional linearized multistage models and more recent two-stage clonal expansion (TSCE) models, belong to a family of models in which cells progress between successive stages-possibly undergoing proliferation at some stages-at rates that may depend (usually linearly) on biologically effective doses. Biologically effective doses, in turn, may depend nonlinearly on administered doses, due to PBPK nonlinearities. This article provides an exact mathematical analysis of the expected number of cells in the last ("malignant") stage of such a "multistage clonal expansion" (MSCE) model as a function of dose rate and age. The solution displays symmetries such that several distinct sets of parameter values provide identical fits to all epidemiological data, make identical predictions about the effects on risk of changes in exposure levels or timing, and yet make significantly different predictions about the effects on risk of changes in the composition of exposure that affect the pharmacodynamic dose-response relation. Several different predictions for the effects of such an intervention (such as reducing carcinogenic constituents of an exposure) that acts on only one or a few stages of the carcinogenic process may be equally consistent with all preintervention epidemiological data. This is an example of nonunique identifiability of model parameters and predictions from data. The new results on nonunique model identifiability presented here show that the effects of an intervention on changing age-specific cancer risks in an MSCE model can be either large or small, but that which is the case cannot be predicted from preintervention epidemiological data and knowledge of biological effects of the intervention alone. Rather, biological data that identify which rate parameters hold for which specific stages are required to obtain unambiguous predictions. From epidemiological data alone, only a set of equally likely alternative predictions can be made for the effects on risk of such interventions.     

Risk-Based Approaches to Deal with Uncertainty in a Data-Poor System: Stakeholder Involvement in Hazard Identification for Marine National Parks and Marine Sanctuaries in Victoria, Australia

Management responsibilities for the system of marine national parks and sanctuaries declared in Victoria, Australia in 2002 have created imperatives for robust, scientifically defendable approaches to identifying threats to valued ecological attributes of the parks, setting management priorities, and developing monitoring systems. We are developing a protocol for ecological risk assessment in the parks that has due regard for the perception of risks by individuals, and ensures that stakeholder values are an intrinsic part of decision making. The inclusive and transparent protocol provides an opportunity for stakeholder involvement in the identification of valued attributes, as well as in the assessment of associated risks. Our approach brings together ideas about how science enters the community engagement domain in ways that promote collaboration and transparency in decision making. A series of stakeholder workshops across the state drew on the expertise of agency staff, community groups, fishers, industry representatives, academics, and knowledgeable park neighbors to identify hazards of major concern in the parks. Many hazards involved predictable, tangible threats like pollution and exotic species, but the approach also identified a number of less obvious threats related to governance issues and the knowledge-base for the parks. Importantly, the workshops with their broad range of stakeholders identified threats not previously considered by the management agency in its internal assessments, and several of these "new" threats are already the subject of action by the agency. The deliberate incorporation of local knowledge and local networks in the risk assessment process also provided opportunities for greater engagement of stakeholders with the management agency.     

On proportional hazards assumption under the random effects models

The proportional hazards mixed-effects model (PHMM) was proposed to handle dependent survival data. Motivated by its application in genetic epidemiology, we study the interpretation of its parameter estimates under violations of the proportional hazards assumption. The estimated fixed effect turns out to be an averaged regression effect over time, while the estimated variance component could be unaffected, inflated or attenuated depending on whether the random effect is on the baseline hazard, and whether the non-proportional regression effect decreases or increases over time. Using the conditional distribution of the covariates we define the standardized covariate residuals, which can be used to check the proportional hazards assumption. The model checking technique is illustrated on a multi-center lung cancer trial.