Relationships between daily affect and pro-environmental behavior at work: The moderating role of pro-environmental attitude

Research in organizational psychology has increasingly focused on understanding the determinants of "green" employee behavior. The present study used a daily diary design to investigate relationships between employees' daily affect, pro-environmental attitude, as well as daily task-related pro-environmental behavior (i.e., the extent to which employees complete required work tasks in environmentally friendly ways), and daily proactive pro-environmental behavior (i.e., the extent to which employees show personal initiative when acting in environmentally friendly ways at work). Fifty-six employees working in small businesses completed a baseline survey and two daily surveys over ten workdays. Daily unactivated positive affect and pro-environmental attitude positively predicted daily task-related pro-environmental behavior. In addition, daily activated positive affect positively predicted daily proactive pro-environmental behavior among employees with a less positive pro-environmental attitude but not among employees with a more positive pro-environmental attitude. These findings suggest that fostering pro-environmental attitudes and, to some extent, positive affect among employees could help organizations to promote pro-environmental behavior in the workplace.

Finding My Way: Protocol of a randomised controlled trial evaluating an internet self-help program for cancer-related distress

Background A cancer diagnosis elicits greater distress than any other medical diagnosis, and yet very few studies have evaluated the efficacy of structured online self-help therapeutic programs to alleviate this distress. This study aims to assess the efficacy over time of an internet Cognitive Behaviour Therapy (iCBT) intervention (‘Finding My Way’) in improving distress, coping and quality of life for individuals with a recent diagnosis of early stage cancer of any type. Methods/Design The study is a multi-site Randomised Controlled Trial (RCT) seeking to enrol 188 participants who will be randomised to either the Finding My Way Intervention or an attention-control condition. Both conditions are delivered online; with 6 modules released once per week, and an additional booster module released one month after program-completion. Participants complete online questionnaires on 4 occasions: at baseline (immediately prior to accessing the modules); post-treatment (immediately after program-completion); then three and six months later. Primary outcomes are general distress and cancer-specific distress, with secondary outcomes including Health-Related Quality of Life (HRQoL), coping, health service utilisation, intervention adherence, and user satisfaction. A range of baseline measures will be assessed as potential moderators of outcomes. Eligible participants are individuals recently diagnosed with any type of cancer, being treated with curative intent, aged over 18 years with sufficient English language literacy, internet access and an active email account and phone number. Participants are blinded to treatment group allocation. Randomisation is computer generated and stratified by gender. Discussion Compared to the few prior published studies, Finding My Way will be the first adequately powered trial to offer an iCBT intervention to curatively treated patients of heterogeneous cancer types in the immediate post-diagnosis/treatment period. If found efficacious, Finding My Way will assist with overcoming common barriers to face-to-face therapy in a cost-effective and accessible way, thus helping to reduce distress after cancer diagnosis and consequently decrease the cancer burden for individuals and the health system. Trial registration Australian New Zealand Clinical Trials Registry ACTRN12613000001​796 16.10.13

The Behavior of Hydroxyl Units of Synthetic Goethite and its Dehydroxylated Product Hematite

The behavior of the hydroxyl units of synthetic goethite and its dehydroxylated product hematite was characterized using a combination of Fourier transform infrared (FTIR) spectroscopy and X-ray diffraction (XRD) during the thermal transformation over a temperature range of 180-270 degrees C. Hematite was detected at temperatures above 200 degrees C by XRD while goethite was not observed above 230 degrees C. Five intense OH vibrations at 3212-3194, 1687-1674, 1643-1640, 888-884 and 800-798 cm(-1), and a H2O vibration at 3450-3445 cm(-1) were observed for goethite. The intensity of hydroxyl stretching and bending vibrations decreased with the extent of dehydroxylation of goethite. Infrared absorption bands clearly show the phase transformation between goethite and hematite: in particular. the migration of excess hydroxyl units from goethite to hematite. Two bands at 536-533 and 454-452 cm(-1) are the low wavenumber vibrations of Fe-O in the hematite structure. Band component analysis data of FTIR spectra support the fact that the hydroxyl units mainly affect the a plane in goethite and the equivalent c plane in hematite.

Let's Dance: A Language for Service Behavior Modeling

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements.

A natural model for architecture : the nature of the evolutionary model 1995

John Frazer's architectural work is inspired by living and generative processes. Both evolutionary and revolutionary, it explores informatin ecologies and the dynamics of the spaces between objects. Fuelled by an interest in the cybernetic work of Gordon Pask and Norbert Wiener, and the possibilities of the computer and the "new science" it has facilitated, Frazer and his team of collaborators have conducted a series of experiments that utilize genetic algorithms, cellular automata, emergent behaviour, complexity and feedback loops to create a truly dynamic architecture. Frazer studied at the Architectural Association (AA) in London from 1963 to 1969, and later became unit master of Diploma Unit 11 there. He was subsequently Director of Computer-Aided Design at the University of Ulter - a post he held while writing An Evolutionary Architecture in 1995 - and a lecturer at the University of Cambridge. In 1983 he co-founded Autographics Software Ltd, which pioneered microprocessor graphics. Frazer was awarded a person chair at the University of Ulster in 1984. In Frazer's hands, architecture becomes machine-readable, formally open-ended and responsive. His work as computer consultant to Cedric Price's Generator Project of 1976 (see P84)led to the development of a series of tools and processes; these have resulted in projects such as the Calbuild Kit (1985) and the Universal Constructor (1990). These subsequent computer-orientated architectural machines are makers of architectural form beyond the full control of the architect-programmer. Frazer makes much reference to the multi-celled relationships found in nature, and their ongoing morphosis in response to continually changing contextual criteria. He defines the elements that describe his evolutionary architectural model thus: "A genetic code script, rules for the development of the code, mapping of the code to a virtual model, the nature of the environment for the development of the model and, most importantly, the criteria for selection. In setting out these parameters for designing evolutionary architectures, Frazer goes beyond the usual notions of architectural beauty and aesthetics. Nevertheless his work is not without an aesthetic: some pieces are a frenzy of mad wire, while others have a modularity that is reminiscent of biological form. Algorithms form the basis of Frazer's designs. These algorithms determine a variety of formal results dependent on the nature of the information they are given. His work, therefore, is always dynamic, always evolving and always different. Designing with algorithms is also critical to other architects featured in this book, such as Marcos Novak (see p150). Frazer has made an unparalleled contribution to defining architectural possibilities for the twenty-first century, and remains an inspiration to architects seeking to create responsive environments. Architects were initially slow to pick up on the opportunities that the computer provides. These opportunities are both representational and spatial: computers can help architects draw buildings and, more importantly, they can help architects create varied spaces, both virtual and actual. Frazer's work was groundbreaking in this respect, and well before its time.

Life cycle modelling and design knowledge development in 3D virtual environments : final report

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This final report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Life Cycle Modelling and Design Knowledge Development in 3D Virtual Environments

Experience plays an important role in building management. “How often will this asset need repair?” or “How much time is this repair going to take?” are types of questions that project and facility managers face daily in planning activities. Failure or success in developing good schedules, budgets and other project management tasks depend on the project manager's ability to obtain reliable information to be able to answer these types of questions. Young practitioners tend to rely on information that is based on regional averages and provided by publishing companies. This is in contrast to experienced project managers who tend to rely heavily on personal experience. Another aspect of building management is that many practitioners are seeking to improve available scheduling algorithms, estimating spreadsheets and other project management tools. Such “micro-scale” levels of research are important in providing the required tools for the project manager's tasks. However, even with such tools, low quality input information will produce inaccurate schedules and budgets as output. Thus, it is also important to have a broad approach to research at a more “macro-scale.” Recent trends show that the Architectural, Engineering, Construction (AEC) industry is experiencing explosive growth in its capabilities to generate and collect data. There is a great deal of valuable knowledge that can be obtained from the appropriate use of this data and therefore the need has arisen to analyse this increasing amount of available data. Data Mining can be applied as a powerful tool to extract relevant and useful information from this sea of data. Knowledge Discovery in Databases (KDD) and Data Mining (DM) are tools that allow identification of valid, useful, and previously unknown patterns so large amounts of project data may be analysed. These technologies combine techniques from machine learning, artificial intelligence, pattern recognition, statistics, databases, and visualization to automatically extract concepts, interrelationships, and patterns of interest from large databases. The project involves the development of a prototype tool to support facility managers, building owners and designers. This Industry focused report presents the AIMMTM prototype system and documents how and what data mining techniques can be applied, the results of their application and the benefits gained from the system. The AIMMTM system is capable of searching for useful patterns of knowledge and correlations within the existing building maintenance data to support decision making about future maintenance operations. The application of the AIMMTM prototype system on building models and their maintenance data (supplied by industry partners) utilises various data mining algorithms and the maintenance data is analysed using interactive visual tools. The application of the AIMMTM prototype system to help in improving maintenance management and building life cycle includes: (i) data preparation and cleaning, (ii) integrating meaningful domain attributes, (iii) performing extensive data mining experiments in which visual analysis (using stacked histograms), classification and clustering techniques, associative rule mining algorithm such as “Apriori” and (iv) filtering and refining data mining results, including the potential implications of these results for improving maintenance management. Maintenance data of a variety of asset types were selected for demonstration with the aim of discovering meaningful patterns to assist facility managers in strategic planning and provide a knowledge base to help shape future requirements and design briefing. Utilising the prototype system developed here, positive and interesting results regarding patterns and structures of data have been obtained.

Multiple-choice versus short-response items: Differences in omit behavior

The overall rate of omission of items for 28,331 17 year old Australian students on a high stakes test of achievement in the common elements or cognitive skills of the senior school curriculum is reported for a subtest in multiple choice format and a subtest in short response format. For the former, the omit rates were minuscule and there was no significant difference by gender or by type of school attended. For the latter, where an item can be 'worth' up to five times that of a single multiple choice item, the omit rates were between 10 and 20 times that for multiple choice and the difference between male and female omit rate was significant as was the difference between students from government and non-government schools. For both formats, females from single sex schools omitted significantly fewer items than did females from co-educational schools. Some possible explanations of omit behaviour are alluded to.

Sharing knowledge at work : macro, social and relational individual influences on individual behavior

The impact of relations between an organization and its workers and the relations among workers on individual knowledge generation and sharing practices has not, to date, been addressed in an integrated way. This paper discusses the findings of a study analyzing issues at macro, locally-constructed and micro levels in a public sector organization, to identify and integrate the complex sets of mediators. Key factors were found to include (a) the contested nature of the process of knowledge construction, (b) the worker’s experience of the organization’s internal environment, (c) how the organization is understood to value knowledge sharing, (d) relations with colleagues, and (e) the perceived outcomes of knowledge sharing behaviors. Implications for practice are discussed.

The interactions of consumption characteristics on social norms

Purpose: An extended Theory of Planned Behavior (TPB) model tests how customer loyalty intentions may relate to subjective and descriptive norms. The study further determines whether consumption characteristics – product enjoyment and importance – moderate norms-loyalty relationships.----- Methodology: Using a two-study approach focusing on youth, an Australian study (n = 244) first augmented TPB with descriptive norm. A Singapore study (n = 415) followed up with how consumption characteristics might moderate norms-loyalty relationships. With both studies, linear regressions tested the relationships among the variables.----- Findings: Extending TPB with descriptive norm improved TPB’s predictive ability across studies. Further, product enjoyment and importance moderated the norms-loyalty relationships differently. Subjective norm related to loyalty intentions significantly with high enjoyment, whereas descriptive norm was significant with low enjoyment. Only subjective norm was significant with low importance.----- Research limitations: Single-item variables, self-reported questionnaires on intended rather than actual behavior, and not controlling for cultural differences between the two samples limit generalizablity.----- Practical implications: The significance of both norms suggests that mobile firms should reach youth through their peers. With youth, social pressure may be influential particularly with hedonic products. However, the different moderations of product enjoyment and importance imply that a blanket marketing strategy targeting youth may not work.----- Originality/Value: This study extends academic knowledge on the relationships between norms and customer loyalty, particularly with consumption characteristics as moderators. The findings highlight the importance of considering different norms with consumer behavior. The study should help mobile firms understand how social influences impact customer loyalty.

The role of sensation seeking and need for cognition on web-site evaluations : a resource-matching perspective

The Internet theoretically enables marketers to personalize a Website to an individual consumer. This article examines optimal Website design from the perspective of personality trait theory and resource-matching theory. The influence of two traits relevant to Internet Web-site processing—sensation seeking and need for cognition— were studied in the context of resource matching and different levels of Web-site complexity. Data were collected at two points of time: personality-trait data and a laboratory experiment using constructed Web sites. Results reveal that (a) subjects prefer Web sites of a medium level of complexity, rather than high or low complexity; (b)high sensation seekers prefer complex visual designs, and low sensation seekers simple visual designs, both in Web sites of medium complexity; and (c) high need-for-cognition subjects evaluated Web sites with high verbal and low visual complexity more favourably.