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Today, most of the car manufacturers world-wide have embraced the principles of lean manufacturing on strategic and operational level. On strategic level car companies like Toyota (Womack et al., 1990) shifted 63 per cent of the value of the car towards the first, second and third tier suppliers for the co-production and co-development of cars as an effect of lean implementation. However, lean implementation was also followed by for instance Ford and GM in the USA, the latter company faced a sudden disruption in 2009 due to the break-out of the financial crisis in 2008, while Ford survived. Could this be foreseen? The exclusive use of (classic) financial performance indicators may give a false image of a company’s current and future performance. There is a need for a model to identify “the stars and the laggards’ regarding car companies by taking into account non-financial and intangible dimensions as advocated by Neely et al. (2003) regarding the third generation of business performance measurement systems. The purpose of this paper is therefor to propose a method to measure and benchmark car company performance which includes the non-financial R&D dimension as well as supply chain, value creating and employee dimensions. These dimensions are present in the value leverage model (van Blokland et al., 2012a, 2012b) which can serve as a basis for this method. The aim is to contribute to the third generation business performance measurement systems by further development of the value leverage model towards a maturity model for benchmarking car company performance. The proposed method can provide a big picture and give insight regarding company performance and direction of the performance.

Value leverage can be measured by a correlation analysis regarding three dimensions, namely, supply chain, R&D and value creation, all relative to the employee or capita which results in the average value leverage (AVL) factor. This AVL factor can be used to compose a combined relative and absolute ranking. The score regarding the AVL results in a relative ranking expressing the level of stability regarding the car companies value chain and system. For the absolute ranking the car companies receive per variable parameter a score according to their absolute performance relative to the other car companies. The relative and absolute ranking are presented on the vertical and horizontal axes forming a matrix. The matrix is the basis for the stability-value leverage maturity model for measuring and benchmarking company performance. With the proposed method, the following main research question can be answered: “How can company performance be measured and benchmarked from a stability-value leverage perspective?”.

With the proposed method, stability-value leverage performance can be measured. The relative ranking on the vertical axis and the absolute ranking form together a matrix which is presented by a scatterplot. A matrix with four maturity levels emerged from the analysis by introducing the average score of all the car companies together in the data set crossing the matrix vertical and horizontal. The four levels are as follows: Level I, low stability – low value leverage; Level II, low stability – high value leverage; Level III, high stability – low value leverage; and Level IV, high stability – high value leverage. Stability-value leverage performance of car companies can be measured over time which makes it possible to observe to which direction the car company migrates for instance from Level I to Level III, before and after the financial crises in 2008. The car companies BMW, Daimler, Audi, Ford and Honda are the best performing companies in stability-value leverage over the period 2000-2014, as they are situated at Level IV. With the findings, the main research question can be answered. The value leverage indicators can be used for measuring and benchmarking company performance regarding four maturity levels of stability and value leverage. The direction of performance can be observed as well.

This research is limited to the car industry. Further research is devised to test the indicators for instance on the truck manufacturing industry. Further research towards new variables is part of the ongoing research.

With the value leverage maturity model, it is possible to inform stakeholders about stability, value leverage and value creation capability of car companies. Weak performing companies can be identified in an early stage with this method to anticipate for instance on possible discontinuation of a car company effecting in merger an acquisition processes.

With the method stakeholders such as employees, users of cars and investors can be informed about how and why car companies perform in an unstable or stable manner.

This research towards ranking and classification of car companies aligns with theories regarding lean manufacturing and maturity models, as these models are used to compare companies on their level of perfection or excellence.

The purpose of this paper is to examine how independent social and commercial activities have developed in response to the perceived decline in the UK High Street and in response to the challenges of increasing digital retailing opportunities. This examination is undertaken through the lens of the social supply chain as a means to understanding, suggesting and expanding on current research regarding retailing and the UK High Street. The authors reveal some of the challenges being posed by the changing patterns of growth and consumption in cities and couple these with shifting supply chain trends.

A case study approach is used to explore the rapid advances and influence of digital technologies on businesses operating on the primary business street of suburban centre, towns or cities (described in the UK collectively as the “high street”). The research is conducted through the analytical lens of the social supply chain.

Theoretically extending the “social” in the social supply chain, the authors illustrate the usefulness of the nuanced concept of the “social supply chain” with two related strategies concerning delivery and balance. These strategies are themselves interlinked with the actions of co-creation, co-production and co-consumption. Examples of social supply chain strategies presented include retail businesses giving away something as an incentive, where the underlying requirement from the customer is that they will bring their own specialist product, skill or social network to a specified location (real or virtual).

For the purpose of this paper, the authors use two distinct strategies relating to delivery and balancing and in relation to the actions of co-creation, co-production and co-consumption to emphasise and analyse changes currently occurring in the UK High Street. The authors take a social supply chain management (SCM) perspective to undertake a systematic critical review of the various recent efforts undertaken by local governments, communities and trader groups to revitalise the High Street.

The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives.

Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error.

The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018.

This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns.

Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices.

Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective.

The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs.

A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

The purpose of this paper is to be limited to provide an overall economic structure linked with a particular hospitality industry by identifying the economic structure of relations between the two hotel industries and other industries within a particular state in the USA.

The analyses of output, labor income and employment multipliers from the input–output system were performed using the IMPLAN 3.0 software. The study attempted to compare the hotel/motel industry (industry code 411) and the accommodations industry (industry code 412) with the top ten industries and averages of each set of multipliers to estimate the relative importance and contribution of the two hotel industries to the economy of Texas. After this comparison, the aggregated input–output tables and multipliers were prepared to determine the economic inter-relationship between the two combined hotel industries (industry code 411 plus industry code 412) and the non-hotel industries, using the criteria of the NAICS (North American Industry Classification System).

The three findings of this study are summarized as follows. First, the two hotel industries impacted the state economy due to a high induced effect from output and a considerable direct, indirect and induced effect from labor income and employment, despite their relatively lower multipliers and the economic downturn in the state. Second, the hotel-related industry had a strong inter-dependent relationship with the finance and insurance-related industries. Finally, while the hotel industry generated more labor income and employment than did the other accommodations industry, it is interesting that the other accommodations industry created more output than did the hotel industry.

Other than limitations pertaining to assumptions of input-output model, an input-output analysis alone cannot become the best analytical method for decision-making. The study was a cross-sectional study with 2009 data and did not incorporate a time-series flow of the state economic structure over several decades. A study of the inter-relationship among varied states bordering the state could be worthwhile to identify the flow of inputs and outputs.

Despite a considerable number of research in measuring the economic impacts, this paper was of great significance, in that the economic impact of the hotel industry that has never been performed in a particular state of the USA was analyzed. Additionally, these quantified economic data and results should be helpful to future plans and policies associated with the hotel industry.