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As productions show characteristics of multi-varieties and small batch in a recent new product system, it is more difficult to acquire its failure rate data. With the help of expert experience information, the authors can get the interval estimation of failure rate data under different methods, so how to make the interval convergence with the new information is an important problem to be solved. The paper aims to discuss this issue.

In this paper, the concept of generalized standard grey number is used to characterize the multi-source heterogeneous uncertainty failure rate data into a unified framework. Then, the engineering construction method is used to calculate the average failure rate and build the grey exponential distribution reliability function, whose image is presented as the possible region of the two-curve envelope.

Further, according to the normal distribution assumption of the regional convergence based on the information supplement, the convergence problem of the reliability function is transformed into the convergence of the area of the curve envelope region, and construct the multi-objective programming model with the minimum envelope area and the lowest total cost of information acquisition, acquire the conclusion that the failure rate is equal to the nuclear of the average failure rate when the envelope region converges.

Through the case analysis of the equipment ejection system of the Harbinger system, five groups of results are obtained by Matlab simulation, which verify the rationality and feasibility of the model described in this paper.

The purpose of this paper is to discuss the feasibility of preparing a statement of owner equity (SOE) and statement of cash flows (SOCF) for the agricultural sector. Also, the use of the Agricultural Resource Management Survey (ARMS) to collect data needed to supplement the US farm sector accounts to prepare a sector SOE and SOCF is discussed.

An SOE and SOCF for an individual producer was used to provide an example format for preparing an SOE and SOCF for the agricultural sector and to identify the data needed from the ARMS survey to supplement farm sector accounts.

The format and data needed to prepare a sector SOE and SOCF were identified and the feasibility of the collection of that data using current ERS/USDA survey collection methods would provide the data needed to prepare the statements. However, the use of two independent data collection authorities to collect the data would result in an agricultural sector SOE and SOCF that would not reconcile.

The paper initiates a dialog of possible alternatives available to the ERS/USDA and researchers concerning data needed and data sources available to prepare an agricultural sector SOE and SOCF, as well as the shortfalls and inaccuracies that would result.

The article discusses the future of the MARC formats and outlines how future cataloguing practice and bibliographic records might look. Background and basic functionality of the MARC formats are outlined, and it is pointed out that MARC is manifest in several different formats. This is illustrated through a comparison between the MARC21 format and the Danish MARC format “danMARC2”. It is argued that present cataloguing codes and MARC formats are based primarily on the Paris principles and that “functional requirements for bibliographic records” (FRBR) would serve as a more solid and user‐oriented platform for future development of cataloguing codes and formats. Furthermore, it is argued that MARC is a library‐specific format, which results in neither exchange with library external sectors nor inclusion of other texts being facilitated. XML could serve as the technical platform for a model for future registrations, consisting of some core data and different supplements of data necessary for different sectors and purposes.

Purpose — A new method of collecting hurricane evacuation data using time-dependent stated choice is developed and evaluated in this study.

Methodology/approach — Hypothetical storms are presented in a video in a sequence of scenarios showing prevailing conditions at discrete points in time as each storm approaches land. Respondents are exposed to nine hypothetical storms representing a range of hurricane characteristics. One of the hypothetical storms is secretly the same as an actual storm the respondents experienced in the past and for which they are required to report their behaviour in a revealed preference survey.

Findings — Stated and actual behaviour was compared and general agreement was found between what people say they would do and what they did. The revealed preference (RP) data was supplemented with time-dependent data from official sources and hurricane evacuation demand models estimated on this enhanced RP data, as well as on a combination of the enhanced RP and time-dependent stated choice (SC) data. When the models were applied to a different data set than the ones on which the models were calibrated, the combined time-dependent RP/SC model performed slightly better than the enhanced RP model. Detailed accounting revealed that time-dependent SC data is 25 percent more expensive to collect than enhanced RP data, although some of this cost may be due to the first-time collection of this type of data.

The purpose of this paper is to review and critique the current status of content analysis (CA) as a research method for inquiring into intellectual capital disclosure (ICD) to determine if CA has a continued role to play in developing new intellectual capital (IC) knowledge.

In all, 110 articles utilising CA as a research methodology for inquiring into ICD are analysed. The research is developed in line with other articles critically investigating IC research conducted by Guthrie et al. (2012), Dumay and Garanina (2013) and Dumay (2014). To help understand the impact of CA research articles, the data set is supplemented by including citation data from Google Scholar. The authors also differentiate the paper from other IC research reviews by critically examining the findings and implications of the articles analysed.

The authors do not hold a very positive view on future research which does not substantially depart from the plethora of articles examining ICD using annual reports as a data source or who do not in some way radically change the approach. The authors are of the view that early research into ICD using CA as a methodology was warranted because there was little knowledge about the pattern of IC disclosure in annual reports and other possible ICD forms. However, the research into ICD using annual reports and other data sources has added little more than prove that companies are unwilling to publicly disclose IC to their stakeholders. While the authors do not hold a positive view on future CA research based on annual reports, the authors do hold out hope that researchers will transform their understanding and application of CA as a research methodology and offer one example of how this might be achieved.

The paper presents a comprehensive critical review of published articles utilising CA as a research methodology for inquiring into ICD along with measuring the impact of the articles using citation data from Google Scholar. Hence, the research and its impacts are simultaneously assessed offering insights into the future role that CA as a research methodology has to play in developing new IC knowledge.

The chapter highlights some of the challenges of research on supplementary education, and indicates ways in which the challenges were tackled in one particular study on Chongqing Municipality, China.

The Chongqing research investigated the determinants of demand for shadow education by Grade 9 students, using a mixed-methods design. A questionnaire was used to collect data from students and parents on various background details and on the nature of the tutoring that they or their children had received. Interviews of a smaller number of respondents provided qualitative data that supplemented and illuminated the quantitative responses.

Different methodological decisions may have different implications. The sampling strategies in the study limited the generalizability but increased the feasibility of the fieldwork and comparability of findings. The top-down strategy of gaining access secured high response rates in the surveys, but to some extent decreased some participants’ willingness to provide information on gray areas. Interviews generated deep and detailed data, but some sensitive topics were intentionally avoided. Informal chats with note taking were constrained in depth and breadth by random time slots and venues, and some details were difficult to record. However, the approach expanded the horizons and facilitated the triangulation of information.

Discussion focuses on collection of data, and stresses the importance of cultural, social, and economic contexts. The chapter may be seen as a case study that provides insights into methods for researching forms of supplementary education. Methodological lessons from the fieldwork in Chongqing have wider relevance, particularly in settings where regular teachers provide extra private classes.

Recently, reverse logistics (RL) has become more prominent due to growing environmental concerns, social responsibility, competitive advantage and high efficiency by customers because of the expansion of product selection and shorter product life cycle. However, effective implementation of RL results in some direct advantages, the most important of which is winning customer satisfaction that is vital to a firm’s success. Therefore, paying attention to customer feedback in supply chain and logistics processes has recently increased so that manufacturers have decided to transform their RL into customer-centric RL. Hence, this paper aims to identify the features of a mobile phone which affect consumer purchasing behaviour and to analyse the interrelationship among them to develop a framework for customer-centric RL. These features are studied based on website analysis of several mobile phone manufacturers. The special focus of this paper is on social media data (Twitter) in an attempt to help the decision-making process in RL through a big data analysis approach.

A portfolio of mobile phone features that affect consumer’s mobile phone purchasing decisions has been taken from website analysis by several mobile phone manufacturers to achieve this objective. Then, interrelationships between the identified features have been established by using big data supplemented with interpretive structural modelling (ISM). Apart from that, cross-impact matrix multiplication, applied to classification analysis, was carried out to graphically represent these features based on their driving power and dependence.

During the study, it has been observed from the ISM that the chip (F5) is the most significant feature that affects customer’s buying behaviour; therefore, mobile phone manufacturers realize that this is to be addressed first.

The focus of this paper is on social media data (Twitter) so that experts can understand the interaction between mobile phone features that affect consumer’s decisions on mobile phone purchasing by using the results.

This research paper aims to explore the application of Statistical Process Control (SPC) methods to measure the performance of a national supermarket chain's problem suppliers. The use of SPC control charts was expected to help in the understanding of the management of buyer/supplier relationships and the effect of the suppliers' performance in the supermarket's replenishment system.

The data analysed were based on the performance of 12 suppliers to the national supermarket over a period of 77 weeks. Quantitative data were supplemented with qualitative data obtained from the suppliers' managers and the supermarket's buyers. The paper compares the measurement of the suppliers' performance using descriptive statistics such as skewness, kurtosis and correlation, with those obtained using SPC‐based control chart techniques.

The results of this analysis indicate that neither descriptive statistics nor the SPC approach were a complete answer to monitoring supplier performance in the supermarket environment. Instead a composite approach was most likely to be effective in improving buyer/supplier relationships. The use of descriptive statistics is important in establishing consistent and achievable performance targets; the use of SPC facilitates performance monitoring and enables meaningful problem‐solving dialogues to be established.

From a retail managerial perspective these results help both in the design of the performance measurement system and in the establishment of realistic performance standards throughout the supply system.

The results suggest that performance measurement of supermarket replenishment systems needs to use a range of performance measures and extend beyond conventional dyadic buyer/supplier analysis.

This paper links supply chain risk management to medicine supply chains to explore the role of policymakers in employing supply chain risk management strategies (SCRMS) to reduce generic medicine shortages.

Using secondary data supplemented with primary data, the authors map and compare seven countries' SCRMS for handling shortage risks in their paracetamol supply chains before and during the first two waves of the COVID-19 pandemic.

Consistent with recent research, the study finds that policymakers had implemented few SCRMS specifically for responding to disruptions caused by COVID-19. However, shortages were largely avoided since multiple strategies for coping with business-as-usual disruptions had been implemented prior to the pandemic. The authors did find that SCRMS implemented during COVID-19 were not always aligned with those implemented pre-pandemic. The authors also found that policymakers played both direct and indirect roles.

Combining longitudinal secondary data with interviews sheds light on how, regardless of the level of preparedness during normal times, SCRMS can be leveraged to avert shortages in abnormal times. However, the problem is highly complex, which warrants further research.

Supply chain professionals and policymakers in the healthcare sector can use the findings when developing preparedness and response plans.

The insights developed can help policymakers improve the availability of high-volume generic medicines in (ab)normal times.

The authors contribute to prior SCRM research in two ways. First, the authors operationalize SCRMS in the medicine supply chain context in (ab)normal times, thereby opening avenues for future research on SCRM in this context. Second, the authors develop insights on the role policymakers play and how they directly implement and indirectly influence the adoption of SCRMS. Based on the study findings, the authors develop a framework that captures the diverse roles of policymakers in SCRM.