Technology valuation within the context of transfer collaborations with Chinese machine tool companies

The point of departure for this study was a recognition of the differences in suppliers' and acquirers' judgements of the value of technology when transferred between the two, and the significant impacts of technology valuation on the establishment of technology partnerships and effectiveness of technology collaborations. The perceptions, transfer strategies and objectives, perceived benefits and assessed technology contributions as well as associated costs and risks of both suppliers and acquirers were seen to be the core to these differences. This study hypothesised that the capability embodied in technology to yield future returns makes technology valuation distinct from the process of valuing manufacturing products. The study hence has gone beyond the dimensions of cost calculation and price determination that have been discussed in the existing literature, by taking a broader view of how to achieve and share future added value from transferred technology. The core of technology valuation was argued as the evaluation of the 'quality' of the capability (technology) in generating future value and the effectiveness of the transfer arrangement for best use of such a capability. A dynamic approach comprising future value generation and realisation within the context of specific forms of collaboration was therefore adopted. The research investigations focused on the UK and China machine tool industries, where there are many technology transfer activities and the value issue has already been recognised in practice. Data were gathered from three groups: machine tool manufacturing technology suppliers in the UK and acquirers in China, and machine tool users in China. Data collecting methods included questionnaire surveys and case studies within all the three groups. The study has focused on identifying and examining the major factors affecting value as well as their interactive effects on technology valuation from both the supplier's and acquirer's point of view. The survey results showed the perceptions and the assessments of the owner's value and transfer value from the supplier's and acquirer's point of view respectively. Benefits, costs and risks related to the technology transfer were the major factors affecting the value of technology. The impacts of transfer payment on the value of technology by the sharing of financial benefits, costs and risks between partners were assessed. The close relationship between technology valuation and transfer arrangements was established by which technical requirements and strategic implications were considered. The case studies reflected the research propositions and revealed that benefits, costs and risks in the financial, technical and strategic dimensions interacted in the process of technology valuation within the context of technology collaboration. Further to the assessment of factors affecting value, a technology valuation framework was developed which suggests that technology attributes for the enhancement of contributory factors and their contributions to the realisation of transfer objectives need to be measured and compared with the associated costs and risks. The study concluded that technology valuation is a dynamic process including the generation and sharing of future value and the interactions between financial, technical and strategic achievements.

The valuation of travel time savings in least developed countries:theoretical and empirical challenges and results from a field study

In developed countries travel time savings can account for as much as 80% of the overall benefits arising from transport infrastructure and service improvements. In developing countries they are generally ignored in transport project appraisals, notwithstanding their importance. One of the reasons for ignoring these benefits in the developing countries is that there is insufficient empirical evidence to support the conventional models for valuing travel time where work patterns, particularly of the poor, are diverse and it is difficult to distinguish between work and non-work activities. The exclusion of time saving benefits may lead to a bias against investment decisions that benefit the poor and understate the poverty reduction potential of transport investments in Least Developed Countries (LDCs). This is because the poor undertake most travel and transport by walking and headloading on local roads, tracks and paths and improvements of local infrastructure and services bring large time saving benefits for them through modal shifts. The paper reports on an empirical study to develop a methodology for valuing rural travel time savings in the LDCs. Apart from identifying the theoretical and empirical issues in valuing travel time savings in the LDCs, the paper presents and discusses the results of an analysis of data from Bangladesh. Some of the study findings challenge the conventional wisdom concerning the time saving values. The Bangladesh study suggests that the western concept of dividing travel time savings into working and non-working time savings is broadly valid in the developing country context. The study validates the use of preference methods in valuing non-working time saving values. However, stated preference (SP) method is more appropriate than revealed preference (RP) method.

On a distinction between casual and formative variable models:theoretical issues and practical implications for organizational researchers

Formative measurement has seen increasing acceptance in organizational research since the turn of the 21st Century. However, in more recent times, a number of criticisms of the formative approach have appeared. Such work argues that formatively-measured constructs are empirically ambiguous and thus flawed in a theory-testing context. The aim of the present paper is to examine the underpinnings of formative measurement theory in light of theories of causality and ontology in measurement in general. In doing so, a thesis is advanced which draws a distinction between reflective, formative, and causal theories of latent variables. This distinction is shown to be advantageous in that it clarifies the ontological status of each type of latent variable, and thus provides advice on appropriate conceptualization and application. The distinction also reconciles in part both recent supportive and critical perspectives on formative measurement. In light of this, advice is given on how most appropriately to model formative composites in theory-testing applications, placing the onus on the researcher to make clear their conceptualization and operationalisation.

Formative experience [postgraduate industrial experience]

Producing the graduates that industry wants is more complicated than simply cramming them full of the right knowledge; they must also develop the confidence and understanding to serve the needs of their future employers. This can only be gained from experience in a real manufacturing environment. The authors describe the pioneering approach to industrial experience given to postgraduates at Cranfield University.

Problems with formative and higher-order reflective variables

Cadogan and Lee (this issue) discuss the problems inherent in modeling formative latent variables as endogenous. In response to the commentaries by Rigdon (this issue) and Finn and Wang (this issue), the present article extends the discussion on formative measures. First, the article shows that regardless of whether statistical identification is achieved, researchers are unable to illuminate the nature of a formative latent variable. Second, the study clarifies issues regarding formative indicator weighting, highlighting that the weightings of formative components should be specified as part of the construct definition. Finally, the study shows that higher-order reflective constructs are invalid, highlights the damage their use can inflict on theory development and knowledge accumulation, and provides recommendations on a number of alternative models which should be used in their place (including the formative model). © 2012 Elsevier Inc.

How to manual:the valuation of rural travel time savings in least developed countries (LDCs)

The manual is designed to bring out issues that are relevant in the valuation of rural travel time savings in Least Developed Countries (LDCs). It should also be relevant for other developing countries which do not have LDC status but have rural economy features typical of low income developing countries. The manual elaborates step-by-step procedures on how to design and execute studies to estimate the value of time (VoT) savings of rural travellers.

Improper use of formative endogenous variables

Researchers often develop and test conceptual models containing formative variables. In many cases, these formative variables are specified as being endogenous. This article provides a clarification of formative variable theory, distinguishing between the formative latent variable and the formative composite variable. When an endogenous latent variable relies on formative indicators for measurement, empirical studies can say nothing about the relationship between exogenous variables and the endogenous formative latent variable: conclusions can only be drawn regarding the exogenous variables' relationships with a composite variable. The authors also show the dangers associated with developing theory about antecedents to endogenous formative variables at the (aggregate) formative latent variable level. Modeling relationships with endogenous formative variables at the (disaggregate) indicator level informs richer theory development, and encourages more precise empirical testing. When antecedents' relationships with endogenous formative variables are modeled at the formative latent variable level rather than the formative indicator level, theory construction can verge on the superficial, and empirical findings can be ambiguous in substantive meaning.

Technology transfer and competitive operations:the valuation and collaboration questions

To improve competitiveness and find new markets companies are extending their operations through collaborations involving technology transfer. However, such collaborations have often been based on ad hoc agreements resulting from negotiations in which each side has been inadequately equipped with information about the other’s motivations and expectations. As a result there has been a gap in the ‘value’ attached to the technology, leading to delays or even failure in reaching an agreement. To address this problem a technology valuation and collaboration model has been developed using empirical data gathered from various points along the UK-China value chain for machine tool technology.

Material and efficient cause interpretations of the formative model:resolving misunderstandings and clarifying conceptual language

This paper presents a causal explanation of formative variables that unpacks and clarifies the generally accepted idea that formative indicators are ‘causes’ of the focal formative variable. In doing this, we explore the recent paper by Diamantopoulos and Temme (AMS Review, 3(3), 160-171, 2013) and show that the latter misunderstand the stance of Lee, Cadogan, and Chamberlain (AMS Review, 3(1), 3-17, 2013; see also Cadogan, Lee, and Chamberlain, AMS Review, 3(1), 38-49, 2013). By drawing on the multiple ways that one can interpret the idea of causality within the MIMIC model, we then demonstrate how the continued defense of the MIMIC model as a tool to validate formative indicators and to identify formative variables in structural models is misguided. We also present unambiguous recommendations on how formative variables can be modelled in lieu of the formative MIMIC model.

Formative variables and unreal variables:why the formative MIMIC model is invalid

In this rejoinder, we provide a response to the three commentaries written by Diamantopoulos, Howell, and Rigdon (all this issue) on our paper The MIMIC Model and Formative Variables: Problems and Solutions (also this issue). We contrast the approach taken in the latter paper (where we focus on clarifying the assumptions required to reject the formative MIMIC model) by spending time discussing what assumptions would be necessary to accept the use of the formative MIMIC model as a viable approach. Importantly, we clarify the implications of entity realism and show how it is entirely logical that some theoretical constructs can be considered to have real existence independent of their indicators, and some cannot. We show how the formative model only logically holds when considering these ‘unreal’ entities. In doing so, we provide important counter-arguments for much of the criticisms made in Diamantopoulos’ commentary, and the distinction also helps clarify a number of issues in the commentaries of Howell and Rigdon (both of which in general agree with our original paper). We draw together these various threads to provide a set of conceptual tools researchers can use when thinking about the entities in their theoretical models.

The MIMIC model and formative variables:problems and solutions

The use of the multiple indicators, multiple causes model to operationalize formative variables (the formative MIMIC model) is advocated in the methodological literature. Yet, contrary to popular belief, the formative MIMIC model does not provide a valid method of integrating formative variables into empirical studies and we recommend discarding it from formative models. Our arguments rest on the following observations. First, much formative variable literature appears to conceptualize a causal structure between the formative variable and its indicators which can be tested or estimated. We demonstrate that this assumption is illogical, that a formative variable is simply a researcher-defined composite of sub-dimensions, and that such tests and estimates are unnecessary. Second, despite this, researchers often use the formative MIMIC model as a means to include formative variables in their models and to estimate the magnitude of linkages between formative variables and their indicators. However, the formative MIMIC model cannot provide this information since it is simply a model in which a common factor is predicted by some exogenous variables—the model does not integrate within it a formative variable. Empirical results from such studies need reassessing, since their interpretation may lead to inaccurate theoretical insights and the development of untested recommendations to managers. Finally, the use of the formative MIMIC model can foster fuzzy conceptualizations of variables, particularly since it can erroneously encourage the view that a single focal variable is measured with formative and reflective indicators. We explain these interlinked arguments in more detail and provide a set of recommendations for researchers to consider when dealing with formative variables.