ACE Research Vignette 047: What is the hard evidence on innovation and business productivity

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Professor Beth Webster at Swinburne University of Technology, examines how innovation in small and medium size businesses affect their productivity.

Complex Symbolic Sequence Encodings for Predictive Monitoring of Business Processes

This paper addresses the problem of predicting the outcome of an ongoing case of a business process based on event logs. In this setting, the outcome of a case may refer for example to the achievement of a performance objective or the fulfillment of a compliance rule upon completion of the case. Given a log consisting of traces of completed cases, given a trace of an ongoing case, and given two or more possible out- comes (e.g., a positive and a negative outcome), the paper addresses the problem of determining the most likely outcome for the case in question. Previous approaches to this problem are largely based on simple symbolic sequence classification, meaning that they extract features from traces seen as sequences of event labels, and use these features to construct a classifier for runtime prediction. In doing so, these approaches ignore the data payload associated to each event. This paper approaches the problem from a different angle by treating traces as complex symbolic sequences, that is, sequences of events each carrying a data payload. In this context, the paper outlines different feature encodings of complex symbolic sequences and compares their predictive accuracy on real-life business process event logs.

The role of institutional pressures and top management support in the intention to adopt cloud computing solutions

Purpose – While many studies have predominantly looked at the benefits and risks of cloud computing, little is known whether and to what extent institutional forces play a role in cloud computing adoption. The purpose of this paper is to explore the role of institutional factors in top management team’s (TMT’s) decision to adopt cloud computing services. Design/methodology/approach – A model is developed and tested with data from an Australian survey using the partial least squares modeling technique. Findings – The results suggest that mimetic and coercive pressures influence TMT’s beliefs in the benefits of cloud computing. The results also show that TMT’s beliefs drive TMT’s participation, which in turn affects the intention to increase the adoption of cloud computing solutions. Research limitations/implications – Future studies could incorporate the influences of local actors who might also press for innovation. Practical implications – Given the influence of institutional forces and the plethora of cloud-based solutions on the market, it is recommended that TMTs exercise a high degree of caution when deciding for the types of applications to be outsourced as organizational requirements in terms of performance and security will differ. Originality/value – The paper contributes to the growing empirical literature on cloud computing adoption and offers the institutional framework as an alternative lens with which to interpret cloud-based information technology outsourcing.

A framework for freeway incident modeling and traffic re-routing

This paper proposes an analytical Incident Traffic Management framework for freeway incident modeling and traffic re-routing. The proposed framework incorporates an econometric incident duration model and a traffic re-routing optimization module. The incident duration model is used to estimate the expected duration of the incident and thus determine the planning horizon for the re-routing module. The re-routing module is a CTM-based Single Destination System Optimal Dynamic Traffic Assignment model that generates optimal real-time strategies of re-routing freeway traffic to its adjacent arterial network during incidents. The proposed framework has been applied to a case study network including a freeway and its adjacent arterial network in South East Queensland, Australia. The results from different scenarios of freeway demand and incident blockage extent have been analyzed and advantages of the proposed framework are demonstrated.

The role of emotion in product, service and business model design

Designers have become aware of the importance of creating strong emotional experiences intertwined with new tangible products for the past decade, however an increased interest from firms has emerged in developing new service and business models as complimentary forms of emotion-driven innovation. This interdisciplinary study draws from the psychological sciences – theory of emotion – and the management sciences – business model literature to introduce this new innovation agenda. The term visceral hedonic rhetoric (VHR) is defined as the properties of a product, (and in this paper service and business model extensions) that persuasively induce the pursuit of pleasure at an instinctual level of cognition. This research paper lays the foundation for VHR beyond a product setting, presenting the results from an empirical study where organizations explored the possibilities for VHR in the context of their business. The results found that firms currently believe VHR is perceived in either their product and/or services they provide. Implications suggest shifting perspective surrounding the use of VHR across a firm’s business model design in order to influence the outcomes of their product and/or service design, resulting in an overall stronger emotional connection with the customer.

Conformational epitopes on the diabetes autoantigen GAD65 identified by peptide phage display and molecular modeling

The major diabetes autoantigen, glutamic acid decarboxylase (GAD65), contains a region of sequence similarity, including six identical residues PEVKEK, to the P2C protein of coxsackie B virus, suggesting that cross-reactivity between coxsackie B virus and GAD65 can initiate autoimmune diabetes. We used the human islet cell mAbs MICA3 and MICA4 to identify the Ab epitopes of GAD65 by screening phage-displayed random peptide libraries. The identified peptide sequences could be mapped to a homology model of the pyridoxal phosphate (PLP) binding domain of GAD65. For MICA3, a surface loop containing the sequence PEVKEK and two adjacent exposed helixes were identified in the PLP binding domain as well as a region of the C terminus of GAD65 that has previously been identified as critical for MICA3 binding. To confirm that the loop containing tile PEVKEK sequence contributes to the MICA3 epitope, this loop was deleted by mutagenesis. This reduced binding of MICA3 by 70%. Peptide sequences selected using MICA4 were rich in basic or hydroxyl-containing amino acids, and the surface of the GAD65 PLP-binding domain surrounding Lys358, which is known to be critical for MICA4 binding, was likewise rich in these amino acids. Also, the two phage most reactive width MICA4 encoded the motif VALxG, and the reverse of this sequence, LAV, was located in this same region. Thus, we have defined the MICA3 and MICA4 epitopes on GAD65 using the combination of phage display, molecular modeling, and mutagenesis and have provided compelling evidence for the involvement of the PEVKEK loop in the MICA3 epitope.

The application of building information modeling to enhance organisational learning

The construction industry is a knowledge-based industry where various actors with diverse expertise create unique information within different phases of a project. The industry has been criticized by researchers and practitioners as being unable to apply newly created knowledge effectively to innovate. The fragmented nature of the construction industry reduces the opportunity of project participants to learn from each other and absorb knowledge. Building Information Modelling (BIM), referring to digital representations of constructed facilities, is a promising technological advance that has been proposed to assist in the sharing of knowledge and creation of linkages between firms. Previous studies have mainly focused on the technical attributes of BIM and there is little evidence on its capability to enhance learning in construction firms. This conceptual paper identifies six ‘functional attributes’ of BIM that act as triggers to stimulate learning: (1) comprehensibility; (2) predictability; (3) accuracy; (4) transparency; (5) mutual understanding and; (6) integration.

Social capital or ethnic enclave location? A multilevel explanation of immigrant business growth

The rapid increase in migration into host countries and the growth of immigrant-owned business enterprises has revitalized research on ethnic business. Does micro (individual)-level social capital, or meso (group)-level location within the ethnic enclave lead to immigrant business growth? Or do you need both? We analyze quantitative data collected from 110 Chinese restaurants in Australia, a major host country. At the micro level we find that coethnic (same ethnic group) networks are critical to the growth of an immigrant entrepreneur's business, particularly in the early years. But non-coethnic (different ethnic group) social capital only has a positive impact on business growth for immigrant businesses outside the ethnic enclave. Our findings are relevant, not only to host-country policymakers, but also for future immigrant business owners and ethnic community leaders trying to better understand how to promote healthy communities and sustainable economic growth.

On the mathematical modeling of wound healing angiogenesis in skin as a reaction-transport process

Over the last 30 years, numerous research groups have attempted to provide mathematical descriptions of the skin wound healing process. The development of theoretical models of the interlinked processes that underlie the healing mechanism has yielded considerable insight into aspects of this critical phenomenon that remain difficult to investigate empirically. In particular, the mathematical modeling of angiogenesis, i.e., capillary sprout growth, has offered new paradigms for the understanding of this highly complex and crucial step in the healing pathway. With the recent advances in imaging and cell tracking, the time is now ripe for an appraisal of the utility and importance of mathematical modeling in wound healing angiogenesis research. The purpose of this review is to pedagogically elucidate the conceptual principles that have underpinned the development of mathematical descriptions of wound healing angiogenesis, specifically those that have utilized a continuum reaction-transport framework, and highlight the contribution that such models have made toward the advancement of research in this field. We aim to draw attention to the common assumptions made when developing models of this nature, thereby bringing into focus the advantages and limitations of this approach. A deeper integration of mathematical modeling techniques into the practice of wound healing angiogenesis research promises new perspectives for advancing our knowledge in this area. To this end we detail several open problems related to the understanding of wound healing angiogenesis, and outline how these issues could be addressed through closer cross-disciplinary collaboration.

Mathematical modeling for improved greenhouse gas balances, agro-ecosystems, and policy development: Lessons from the Australian experience

If the land sector is to make significant contributions to mitigating anthropogenic greenhouse gas (GHG) emissions in coming decades, it must do so while concurrently expanding production of food and fiber. In our view, mathematical modeling will be required to provide scientific guidance to meet this challenge. In order to be useful in GHG mitigation policy measures, models must simultaneously meet scientific, software engineering, and human capacity requirements. They can be used to understand GHG fluxes, to evaluate proposed GHG mitigation actions, and to predict and monitor the effects of specific actions; the latter applications require a change in mindset that has parallels with the shift from research modeling to decision support. We compare and contrast 6 agro-ecosystem models (FullCAM, DayCent, DNDC, APSIM, WNMM, and AgMod), chosen because they are used in Australian agriculture and forestry. Underlying structural similarities in the representations of carbon flows though plants and soils in these models are complemented by a diverse range of emphases and approaches to the subprocesses within the agro-ecosystem. None of these agro-ecosystem models handles all land sector GHG fluxes, and considerable model-based uncertainty exists for soil C fluxes and enteric methane emissions. The models also show diverse approaches to the initialisation of model simulations, software implementation, distribution, licensing, and software quality assurance; each of these will differentially affect their usefulness for policy-driven GHG mitigation prediction and monitoring. Specific requirements imposed on the use of models by Australian mitigation policy settings are discussed, and areas for further scientific development of agro-ecosystem models for use in GHG mitigation policy are proposed.

In vitro modeling of the prostate cancer microenvironment

Prostate cancer is the most commonly diagnosed malignancy in men and advanced disease is incurable. Model systems are a fundamental tool for research and many in vitro models of prostate cancer use cancer cell lines in monoculture. Although these have yielded significant insight they are inherently limited by virtue of their two-dimensional (2D) growth and inability to include the influence of tumour microenvironment. These major limitations can be overcome with the development of newer systems that more faithfully recreate and mimic the complex in vivo multi-cellular, three-dimensional (3D) microenvironment. This article presents the current state of in vitro models for prostate cancer, with particular emphasis on 3D systems and the challenges that remain before their potential to advance our understanding of prostate disease and aid in the development and testing of new therapeutic agents can be realised.

Towards simple rules heuristics for IT business value

The business value of IT (BVIT) has been a prominent and central research topic in the IS discipline. Due to continuous and unpredictable technology and business changes, a more dynamic perspective on IT business value that includes organizational learning is required. We suggest that simple rules heuristics can address this challenge. The simple rules heuristics approach has been introduced by Eisenhardt and co-authors (Bingham & Eisenhardt, 2011; Bingham, Eisenhardt, & Furr, 2007; Eisenhardt & Sull, 2001) to better understand strategic decision making for capturing superabundant, heterogeneous, fastmoving opportunities. They argue that explicit organizational learning can translate accumulated experience into increasingly effective heuristics for strategic processes in highvelocity environments. We make three main contributions by exploring the suitability of a simple rules heuristics approach for the creation of IT business value: (1) we propose six types of simple rules heuristics for capturing IT-based opportunities in dynamic environments, including synergy heuristics as specifically relevant in an IT context, (2) we show how a simple rules heuristics approach can advance our understanding of dynamics and organizational learning for BVIT, and; (3) we introduce the strategic logic of opportunity to BVIT.

Data driven modeling for power transformer lifespan evaluation

Large sized power transformers are important parts of the power supply chain. These very critical networks of engineering assets are an essential base of a nation’s energy resource infrastructure. This research identifies the key factors influencing transformer normal operating conditions and predicts the asset management lifespan. Engineering asset research has developed few lifespan forecasting methods combining real-time monitoring solutions for transformer maintenance and replacement. Utilizing the rich data source from a remote terminal unit (RTU) system for sensor-data driven analysis, this research develops an innovative real-time lifespan forecasting approach applying logistic regression based on the Weibull distribution. The methodology and the implementation prototype are verified using a data series from 161 kV transformers to evaluate the efficiency and accuracy for energy sector applications. The asset stakeholders and suppliers significantly benefit from the real-time power transformer lifespan evaluation for maintenance and replacement decision support.

Development and validation of a multilevel model for predicting workload under routine and nonroutine conditions in an air traffic management center

Objective: The aim of this study was to develop a model capable of predicting variability in the mental workload experienced by frontline operators under routine and nonroutine conditions. Background: Excess workload is a risk that needs to be managed in safety-critical industries. Predictive models are needed to manage this risk effectively yet are difficult to develop. Much of the difficulty stems from the fact that workload prediction is a multilevel problem. Method: A multilevel workload model was developed in Study 1 with data collected from an en route air traffic management center. Dynamic density metrics were used to predict variability in workload within and between work units while controlling for variability among raters. The model was cross-validated in Studies 2 and 3 with the use of a high-fidelity simulator. Results: Reported workload generally remained within the bounds of the 90% prediction interval in Studies 2 and 3. Workload crossed the upper bound of the prediction interval only under nonroutine conditions. Qualitative analyses suggest that nonroutine events caused workload to cross the upper bound of the prediction interval because the controllers could not manage their workload strategically. Conclusion: The model performed well under both routine and nonroutine conditions and over different patterns of workload variation. Application: Workload prediction models can be used to support both strategic and tactical workload management. Strategic uses include the analysis of historical and projected workflows and the assessment of staffing needs. Tactical uses include the dynamic reallocation of resources to meet changes in demand.

Spatially explicit structural equation modeling

Structural equation modeling (SEM) is a powerful statistical approach for the testing of networks of direct and indirect theoretical causal relationships in complex data sets with intercorrelated dependent and independent variables. SEM is commonly applied in ecology, but the spatial information commonly found in ecological data remains difficult to model in a SEM framework. Here we propose a simple method for spatially explicit SEM (SE-SEM) based on the analysis of variance/covariance matrices calculated across a range of lag distances. This method provides readily interpretable plots of the change in path coefficients across scale and can be implemented using any standard SEM software package. We demonstrate the application of this method using three studies examining the relationships between environmental factors, plant community structure, nitrogen fixation, and plant competition. By design, these data sets had a spatial component, but were previously analyzed using standard SEM models. Using these data sets, we demonstrate the application of SE-SEM to regularly spaced, irregularly spaced, and ad hoc spatial sampling designs and discuss the increased inferential capability of this approach compared with standard SEM. We provide an R package, sesem, to easily implement spatial structural equation modeling.