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The purpose of this paper is to deal with the practical challenge faced by modern logistics enterprises to accurately evaluate driving performance with high computational efficiency under the disturbance of road smoothness and to identify significantly associated performance influence factors.

The authors cooperate with a logistics server (G7) and establish a driving grading system by constructing real-time inertial navigation data-enabled indicators for both driving behaviour (times of aggressive speed change and times of lane change) and road smoothness (average speed and average vibration times of the vehicle body).

The developed driving grading system demonstrates highly accurate evaluations in practical use. Data analytics on the constructed indicators prove the significances of both driving behaviour heterogeneity and the road smoothness effect on objective driving grading. The methodologies are validated with real-life tests on different types of vehicles, and are confirmed to be quite effective in practical tests with 95% accuracy according to prior benchmarks. Data analytics based on the grading system validate the hypotheses of the driving fatigue effect, daily traffic periods impact and transition effect. In addition, the authors empirically distinguish the impact strength of external factors (driving time, rainfall and humidity, wind speed, and air quality) on driving performance.

This study has good potential for providing objective driving grading as required by the modern logistics industry to improve transparent management efficiency with real-time vehicle data.

This study contributes to the existing research by comprehensively measuring both road smoothness and driving performance in the driving grading system in the modern logistics industry.

Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information.

This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates.

The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss.

This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.

Road freight carbon dioxide (CO₂) emissions are determined by a complex interaction between shippers and hauliers within the boundaries set by regulations and economic factors. It is necessary to gain understanding about the various driving forces and trends affecting these to promote low carbon future. The purpose of this paper is to find out what factors affect the long-term future development of road freight CO₂ emissions and whether the long-term emission targets will be achieved.

An international comparison of similar Delphi surveys is carried out in Finland, Norway, and Sweden.

The Delphi surveys indicate that the structural change of the economy, changes of consumer habits, concerns of energy and environment and changes in logistics practices and technology are the overarching trends shaping the future of the energy efficiency and CO₂ emissions of road freight transport. The expert forecasts for Finland and Sweden highlight that reaching the carbon emission target of 30 per cent reduction for the year 2030 is possible. However, the CO₂ emissions may also increase significantly even though the CO₂ intensity would decrease, as the Norwegian forecast shows.

This study combined quantitative and qualitative analysis. The results confirmed that similar factors are seen to affect the future in all three countries, but with some national differences in the likely effects of the factors. Future research using the same methodology would enable wider analysis of the global significance of these driving forces.

The paper aims to review existing performance indicators in technology business incubators (TBIs) and propose some new indicators with a focus on incubation activities in higher educational institutes (HEIs) of India.

Performance indicators of various types of incubators were identified from research papers followed by interview, consultation and suggestion from experts of the subject. Nature of interrelationship between the identified indicators has been established with the help of Interpretive Structural Modelling methodology and Matrice d’impacts croisés multiplication appliquée á un classment analysis.

Number of ideas came for screening and number of ideas converted to start-ups, survival rate of incubatees is the indicators which have the highest driving power followed by time taken in screening an idea and number of failed or rejected ideas returned back into incubation. Few indicators (driving indicators) are affecting performance of other indicators as well.

Some performance indicators are proposed which can be used for measuring performance of technology incubators in India. The actual implications will be known when these findings are used to assess performance of some technology incubator. This also is the limitation of the study that some cases can be included to validate the findings of this research.

A total of 15 performance indicators for measuring performance of TBIs in Indian HEIs have been proposed. The proposed indicators will help incubator management to prioritize the efforts and resource allocation.

TBIs are looked upon as mechanism for promoting entrepreneurial culture in Indian HEIs. Their success is well linked to growth of society. This research will help technology incubators to identify the most important factors in incubation process. Performance improvement will directly affect society in whole. Culture of IEE (Innovation, Entrepreneurship and Employment ) can be achieved through technology incubators

Identification of new indicators for performance measurement of incubators in Indian HEIs is the novelty of this research. This has a lot of value due to multilevel hierarchy model.

Implementation of a sustainability paradigm demands new choices and innovative ways of thinking. The main objective of this paper is to provide a meaningful sustainability assessment tool for make informed decisions, which is applied to higher education institutions (HEIs).

The objective is achieved by developing a quantitative tool for sustainability assessment using a driving force‐pressure‐state‐exposure‐effect‐action (DPSEEA) framework. The DPSEEA framework considers environmental, social, economic, and educational performance as main dimensions of sustainability. The proposed model is called DPSEEA‐Sustainability index Model (D‐SiM). The D‐SiM is a causality‐based model in which the sustainability index (SI) is an outcome of nonlinear effects of sustainability indicators in various stages of DPSEEA. To have an improved understanding of input factors (driving forces) and their impact on sustainability, a simplified empirical model is developed and applied to HEIs to determine the percent contribution of various driving forces on sustainability.

The study reveals that economic development, social equity, and education in sustainability are the major drivers for achieving sustainability in HEI, while health and safety issues, energy requirements, institutional enhancement, and international research and development trends are the less significant driving forces.

The indicators connected in DPSEEA framework through causal relationships lead to the quantitative assessment of sustainability, which provides a unique approach for informed decision making.

This study aims to propose a comprehensive framework among Key Performance Indicators (KPIs) for analyzing the Information Facilitated Product Recovery System (IFPRS) on the basis of feedback captured from the industry experts and researchers.

Total Interpretive Structural Modeling (TISM) methodology interspersed with fuzzy MICMAC is used to extract the interrelationships and develop a hierarchical structure among the identified KPIs. Further, the Fuzzy Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) method has been enforced to determine the intensity of these relationships and identify the most influential KPIs among identified KPIs from literature review and expert opinions. The outcome indicates that “information sharing,” “technology capacity” and “technology standards such as EDI, RFID” are the KPIs that have attained highest driving power.

This study has identified 15 KPIs of IFPRS and developed an integrated model using TISM and the fuzzy MICMAC approach, which is helpful to describe and organize the important KPIs and reveal the direct and indirect effects of each KPI on the IFPRS implementation. The integrated approach is developed, as the TISM model provides only binary relationship among KPIs, while fuzzy MICMAC analysis provides explicit analysis related to driving and dependence power of KPIs.

Structural Equation Modeling (SEM) analysis can be performed based on the adequate number of responses collected using structured questionnaire. More qualitative techniques like ELECTRE, TOPSIS, etc. can be used to establish the strength of relationship among the KPIs and ranking them to focus on the few critical KPIs.

The proposed modeling could empower various governmental and non-governmental regulatory bodies in formulation of policies to effectively tackle the problem related to product recovery systems. This study has strong practical implications, for both practitioners as well as academicians. The practitioners need to concentrate on identified KPIs more cautiously during IFPRS implementation in their organizations and the top management could formulate strategy for implementing these KPIs obtained.

There is a lack of studies related to the modeling of KPIs of IFPRS. As vast information is essential about the products returned during different product recovery stages, this study bridges the gap in literature by providing a framework for KPIs related to IFPRS. It is expected that the results originated will assist the experts to relevantly identify the significant and drop insignificant KPI for successful product recovery implementation and performance improvement of IFPRS.

The objective of this article is to carry out the driving power and dependency analysis of green human resource management (GHRM) indicators of the automotive service sector to identify the most significant ones.

The GHRM indicators were identified through exhaustive literature search and validated through the semi-structured interview with 15 domain experts. The ‘Total Interpretive Structural Modelling (TISM)’ approach was applied for exploring the contextual relationship between the indicators and simultaneously developing their structural hierarchy. The MICMAC analysis was used for categorising the indicators based on their ability to influence the other ones.

In the present study, indicators namely ‘Green organisational culture and adoption of green strategy (C5)’ and ‘Green training and development (C1)’ were found to be the significant ones, whereas ‘Green employee relations and union-management (C10)’ was found to be highly dependent on the rest of the indicators.

The proposed model has been developed in the Indian context and is limited to the automotive sector. However, the same model may apply to other domains of different economies by carrying out slight modifications to the same. Also, the inputs taken from the experts of the case sector could be biased. For the HR professionals, the present study helps to identify the key indicators which need to be considered for enlightening the environmental performance of the service organisation.

This research adds a significant assessment to the current knowledge base by assessing the contextual relationship between the indicators of GHRM as none of the past studies focused on the same by using the TISM method in the Indian service sectors context.

The purpose of this paper is to investigate relationships of urban seismic resilience assessment indicators.

To achieve this aim, construction of the urban seismic resilience assessment indicators system was conducted and 20 indicators covering five dimensions, namely building and lifeline infrastructure, environment, society, economy and institution were identified. Following this, this study used evidence fusion theory and intuitionistic fuzzy sets to process the information from experts then developed the fuzzy total interpretive structure model.

A total of 20 urban seismic resilience assessment indicators are reconstructed into a hierarchical and visual system structure including five levels. Indicators in the bottom level including debris flow risk, landslide risk, earthquake experience and demographic characteristics are fundamental indicators that significantly impact other indicators. Indicators in the top level including open space, gas system and public security are direct indicators influenced more by other indicators. Other indicators are in middle levels. Results of MICMAC analysis visually categorize these indicators into independent indicators, linkage indicators, autonomous indicators and dependent indicators according to driving power and dependence.

This paper attempts to explore relationships of urban seismic resilience assessment indicators with the interpretive structural model method. Additionally, Fuzzy total interpretive structure model is developed combined with evidence fusion theory and intuitionistic fuzzy sets, which is the extension of total interpretive structure model. Research results can assist the analytic network process method in assessing urban seismic resilience in future research.

Sustainability is an integrating concept and demands strategic attention in developing countries like India. Due to strict environmental regulations and ongoing government sustainable policies such as “Namami Gange,” leather industries are extensively facing challenges to conform themselves toward these sustainable policies. The major challenge faced by leather industries is the exponentially increasing cost of adaptation to sustainable product and process. Under these operational constraints, survival of Indian leather industries has become a major challenge. In this context, this paper aims to identify and evaluate sustainable manufacturing policies. The key performance indicators (KPIs) based on triple bottom line of sustainability can assist leather industries that are about to initiate adopting sustainable practices.

This paper demonstrates the role of KPIs for evaluating sustainable manufacturing policies for leather industries in India. Initially, an in-depth literature review analysis has been carried out to identify indicators for evaluation of sustainable manufacturing policies. In this work, an integrated methodology has been developed to refine the priority map of the aforementioned KPIs based on consensus building among experts using Kappa analysis. Total interpretive structural modeling (TISM) has been used to demonstrate relationships which explain the significance of the KPIs. Further, Matriced Impact Croises Multiplication Applique analysis has been carried out to explore the relationships amongst KPIs.

Based on above analysis, identified interactive relationships among the KPIs will assist managers and decision-makers to incorporate effective sustainable policies in leather industries.

It is expected that these identified interactive interrelationships between KPIs will certainly facilitate the leather industry to achieve higher sustainable performance and competitiveness.

This study carries out an in-depth literature review analysis of sustainable manufacturing policies in leather industry. The author proposes an integrated methodology using kappa analysis, consensus building and TISM for evaluation of sustainable policies based on the literature review analysis and expert opinion.

This paper discusses and integrates the concept of complexity in the performance measurement and management (PMM) theory by providing a comprehensive framework to design and evaluate the overall coherence alignment of an indicators hierarchy in unstable and changing environment.

An original, comprehensive and dynamic framework has been proposed and then applied on a sample case of a large-scale retail trade (LSRT) company, starting from relevant frameworks and criteria in the scientific literature.

This research shows that organizational changes may significantly impact the coherence alignment of an organization's indicators hierarchy. In addition, it finds that even though the alignment at the operational level is obtained, its effectiveness should be evaluated in relation to the organization's strategic orientation. Indeed, without assessing the strategic alignment of an indicators system, an aligned hierarchy at the operational level could lead to ineffective results.

This paper focuses on the topic of measuring the coherence inside an indicators hierarchy, which seems not to be addressed in the literature. Thus, it opens a new research stream, integrating the studies on performance indicators with an essential element that often causes flawed performance measures in organizations.

Organizations could adopt this framework to design effective PMM systems and maintain them in light of the organizational changes.

This study introduces different metrics to evaluate the coherence and alignment of an indicators system, being one of the few research studies to address this topic in the context of complex and changing environments.