The research concerned semi-dyadic relations in SMEs and large companies that managed innovative suppliers in New Zealand construction supply chains. It explored effects of (independent) company variables on (mediating) procurement management variables, and also the effects of these variable types on (dependent) procurement performance variables when managing innovative suppliers.Exploratory interviews (N=5) revealed that innovation procurement seemed professional and logical within their contexts.Survey I (N=112) revealed that most case companies followed a product leadership strategy, and were equally entrepreneurial to innovative customers and innovative suppliers. They were innovative and gave innovative suppliers a dominant innovation role. They seemed to prefer radical innovations less than incremental innovations, but still somewhat more than New Zealand averages. Companies had slight preferences for new, small, or foreign suppliers for radical innovations. Innovations with supplier interactions were more beneficial to the company and the natural environment, than innovations without supplier interactions. Higher company innovation-benefits could equal higher environmental innovation-benefits. This profile differed from the profile of average companies in the construction supply chain.Survey I found weak correlations among output performance variables and process or proxy performance variables.Dependent (procurement and performance) variables were affected differently. Conversely, independent (company and procurement) variables had different effects.Different from extant literature, Survey I found limited statistically-significant effects of company variables on procurement management variables, and of these two variable types on performance. A minority (41%) of company variables affected procurement variables; only two company variables (13%) affected performance; a minority (40%) of procurement variables affected performance.Product leadership and NPD/innovation experience affected performance. Moreover, trust, lifestyle strategies and survival strategies affected procurement variables. Conversely, 27% of performance variables (satisfaction on marketing & sales; benefits for the natural environment) and 30% of procurement variables (entrepreneurial orientation with innovative suppliers, relation intensity with manufacturers, and small vs large suppliers for radical innovations) responded stronger on some company variables. Company size (<99 versus >250 staff) had little effects.Innovating, opportunity-seeking and trust towards innovative suppliers, and relation intensity with innovative service providers had highest effects on performance. Conversely, 46% of the performance variables (satisfaction with innovative suppliers, benefits for natural environment and company) responded stronger on innovating, opportunities-seeking and trust variables.Survey II (N=33) identified 12 procurement best-practices that respondents used for specific supplier or innovation types.Causality should be treated cautiously. Findings reflected the inconclusive results from extant literature. The research provided a nuanced and varied understanding on management of innovative suppliers, on the effects of entrepreneurial orientation to innovative suppliers, on the limited effects of company size, on the complex relations between various performance measures, and on entrepreneurship as a theoretical lens in innovation procurement. Companies had several options on how they managed their innovative suppliers. Additionally, the company characteristics and context of in this nascent research domain could be more important than commonly assumed from extant research.
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The promotor was Prof. Erik Jan Hultink and copromotors Dr Ellis van den Hende en Dr R. van der Lugt. The title of this dissertation is Armchair travelling the innovation journey. ‘Armchair travelling’ is an expression for travelling to another place, in the comfort of one’s own place. ‘The innovation journey’ is the metaphor Van de Ven and colleagues (1999) have used for travelling the uncharted river of innovation, the highly unpredictable and uncontrollable process of innovation. This research study began with a brief remark from an innovation project leader who sighed after a long and rough journey: ‘had I known this ahead of time…’. From wondering ‘what could he have known ahead of time?’ the immediate question arose: how do such innovation journeys develop? How do other innovation project leaders lead the innovation journey? And could I find examples of studies about these experiences from an innovation project leader’s perspective that could have helped the sighing innovation project leader to have known at least some of the challenges ahead of time? This dissertation is the result of that quest, as we do know relatively little how this process of the innovation project leader unfolds over time. The aim of this study is to increase our understanding of how innovation project leaders lead their innovation journeys over time, and to capture those experiences that could be a source for others to learn from and to be better prepared. This research project takes a process approach. Such an approach is different from a variance study. Process thinking takes into account how and why things – people, organizations, strategies, environments – change, act and evolve over time, expressed by Andrew Pettigrew (1992, p.10) as catching “reality in flight”.
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Small medium enterprises represent the largest share of revenues in the apparel and fashion business, compared to larger integrated companies. Small companies however often have to rely on larger suppliers in order to efficiently produce their products. These larger suppliers however will often prioritize companies that place larger orders. In the impossibility to deliver larger orders, how can these buyers make sure that a producer complies with their requests? In this paper we try to answer this question by applying concepts from the marketing channels literature to the specific context of fashion buying. We conclude that despite that the peculiarities of the fashion business do not foster the formation of long-term commitment, dependent companies can develop sources of power based on knowledge and reputation, but also based on applying non-mediated ways to manage the buyer-seller relationship. Examples of possible power sources for a buyer are: establishing a strong brand that a business partner will use in promoting its proposition, forcing suppliers to make transaction specific investments (which are not redeploy able), specializing in new products and technologies that a supplier will want to understand and use, developing knowledge that can be exchanged in the form of specialized personnel, investing in standardization of communication before the relationship starts in order to reduce administrative costs for both parties. Power sources are most effective when non-mediated, i.e. informal and based on reciprocity; ‘hard’ contracts with punishments (coercive power sources) in case of non-compliance will diminish the willingness to collaborate.
The denim industry faces many complex sustainability challenges and has been especially criticized for its polluting and hazardous production practices. Reducing resource use of water, chemicals and energy and changing denim production practices calls for collaboration between various stakeholders, including competing denim brands. There is great benefit in combining denim brands’ resources and knowledge so that commonly defined standards and benchmarks are developed and realized on a scale that matters. Collaboration however, and especially between competitors, is highly complex and prone to fail. This project brings leading denim brands together to collectively take initial steps towards improving the ecological sustainability impact of denim production, particularly by establishing measurements, benchmarks and standards for resource use (e.g. chemicals, water, energy) and creating best practices for effective collaboration. The central research question of our project is: How do denim brands effectively collaborate together to create common, industry standards on resource use and benchmarks for improved ecological sustainability in denim production? To answer this question, we will use a mixed-method, action research approach. The project’s research setting is the Amsterdam Metropolitan Area (MRA), which has a strong denim cluster and is home to many international denim brands and start-ups.
Cross-Re-Tour supports European tourism SME while implementing digital and circular economy innovations. The three year project promotes uptake and replication by tourism SMEs of tools and solutions developed in other sectors, to mainstream green and circular tourism business operations.At the start of the project existing knowledge-gaps of tourism SMEs will be researched through online dialogues. This will be followed by a market scan, an overview of existing state of the art solutions to digital and green constraints in other economic sectors, which may be applied to tourism SME business operations: water, energy, food, plastic, transport and furniture /equipment. The scan identifies best practices from other sectors related to nudging of clients towards sustainable behaviour and nudging of staff on how to best engage with new tourism market segments.The next stage of the project relates to two design processes: an online diagnostic tool that allows for measuring and assessing (160) SME’s potential to adapt existing solutions in digital and green challenges, developed in other economic sectors. Next to this, a knowledge hub, addresses knowledge constraints and proposes solutions, business advisory services, training activities to SMEs participating. The hub acts as a matchmaker, bringing together 160 tourism SMEs searching for solutions, with suppliers of existing solutions developed in other sectors. The next key activity is a cross-domain open innovation programme, that will provide 80 tourism SMEs with financial support (up to EUR 30K). Examples of partnerships could be: a hotel and a supplier of refurbished matrasses for hospitals; a restaurant and a supplier of food rejected by supermarkets, a dance event organiser and a supplier of refurbished water bottles operating in the cruise industry, etc.The 80 cross-domain partnerships will be supported through the knowledge hub and their business innovation advisors. The goal is to develop a variety of innovative partnerships to assure that examples in all operational levels of tourism SMEs.The innovation projects shall be presented during a show-and-share event, combined with an investors’ pitch. The diagnostic tool, market scan, knowledge hub, as well as the show and share offer excellent opportunities to communicate results and possible impact of open innovation processes to a wider international audience of destination stakeholders and non-tourism partners. Societal issueSupporting the implementation of digital and circular economy solutions in tourism SMEs is key for its transition towards sustainable low-impact industry and society. Benefit for societySolutions are already developed in other sectors but the cross-over towards tourism is not happening. The project bridges this gap.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
