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Retailers are facing challenges in making sense of the significant amount of data available for a better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on 'purchasing behavior of customers' and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600 K transaction data of a global grocery retailer are analyzed and 175 stores in Istanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.

PurposeWhile marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs).Design/methodology/approachThis research employs a fuzzy-set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs.FindingsThe key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use, and (2) the configurations for small firms are different from those for medium-sized firms.Research limitations/implicationsThe research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers.Practical implicationsThe study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.Originality/valueThe study contributes to the literature by addressing an important yet underresearched area, i.e. marketing analytics use in SMEs, applying a configurational approach to the research phenomenon. It highlights different pathways to marketing analytics use in SMEs. The findings provide empirical evidence on the possibility and implication of marketing analytics use being asymmetrical and different between small and medium-sized firms.

Food waste reduction, as a major application area of the Internet of Things (IoT) and big data technologies, has become one of the most pressing issues. In recent years, there has been an unprecedented increase in food waste, which has had a negative impact on economic growth in many countries. Food waste has also caused serious environmental problems. Agricultural production, post-harvest handling, and storage, as well as food processing, distribution, and consumption, can all lead to food wastage. This wastage is primarily caused by inefficiencies in the food supply chain and a lack of information at each stage of the food cycle. In order to minimize such effects, the Internet of Things, big data-based systems, and various management models are used to reduce food waste in food supply chains. This paper provides a comprehensive review of IoT and big data-based food waste management models, algorithms, and technologies with the aim of improving resource efficiency and highlights the key challenges and opportunities for future research.

The desk rejection of submitted articles can be a hugely frustrating and demotivating process from the perspective of the researcher, but equally, a time-consuming and vital step in the process for the Editor, tasked with selecting appropriate articles that meet the required criteria for further review and scrutiny. The feedback from journal Editors within this editorial, highlights the significant gaps in understanding from many academics of the journal assessment process and acceptance criteria for progression to the review stage. This editorial offers a valuable “lived-in” perspective on the desk rejection process through the lens of the Editor, via the differing views of nine leading journal Editors. Each Editor articulates their own perspectives on the many reasons for desk rejection, offering key insight to researchers on how to align their submissions to the specific journal requirements and required quality criteria, whilst demonstrating relevance and contribution to theory and practice. This editorial develops a succinct summary of the key findings from the differing Editor perspectives, offering a timely contribution of significant value and benefit to academics and industry researchers alike.

Antibiotics in waste activated sludge (WAS) has drawn increasing attention because of their persistent and bioaccumulation characteristics. Most study illustrated the role of antibiotics in anaerobic fermentation from WAS, but lacking the analysis at microbial level as well as the possible interaction between them. This study investigated the effect of three sulfonamide antibiotics (sulfamethoxazole (SMX), sulfaquinoxaline (SQX), and sulfadiazine (SD)) on WAS fermentation and explored its microbiological mechanism. Results indicated that the production of short-chain fatty acids (SCFAs) was significantly improved by 1.9 folds with a peak value at 4626.1 mg COD L

The genetic structure and evolutionary relationship of Dulong chicken with other native Chinese species remained unclear. In this study, the mitochondrial control region was analyzed in total of 343 samples comprising 59 from Dulong chicken and 284 from 8 other Chinese local breeds revealed 51 mutation sites that defined 42 haplotypes. The maximum genetic variation was observed between the Shimian caoke and Pengxian yellow chickens. Phylogenetic analysis revealed that these local chickens mainly scatter in two southwestern clades. Dulong chickens have close relationship with other native chicken. Finding of this study suggests a single matrilineal lineage of indigenous Dulong chickens.

Toonamicrocarpavarin (1), a new tirucallane-type triterpenoid, along with eight known ones, piscidinol A (2), toonaciliatavarin E (3), toonayunnanin A (4), 7-acetyneotrichilenone (5), hispidol A (6), odoratone (7), phellochin (8), toonaciliatavarin D (9), were isolated from T. ciliata. Their structures were identified on the basis of ESIMS, HREIMS and 1 D/2D NMR analysis. The cytotoxic activity of the new compound was also evaluated. All compounds were obtained from T. ciliata for the first time, which plays an important role in chemotaxonomy of the plant T. ciliata.

The purpose aims to examine the key factors influencing Chinese consumer’s purchasing behaviour of eco‐friendly food in China giving its context as an emerging economy and its rapidly rising importance in the world eco‐friendly food market. This paper adopts and extends the Responsible Environmental Behaviour (REB) theory by empirically testing key psychosocial factors influencing the purchase intention of eco‐friendly food and the moderating effects of consumers’ demographic characteristics on the relationship between the key psychosocial factors and the purchase intention. A number of hypotheses are proposed. A questionnaire was designed and distributed via online survey in Beijing, China. A total of 239 valid responses were received. The empirical data were used to test the research hypotheses using the hierarchical multiple regression analysis. The research finds that the personality factors in the REB model (i.e., pro‐environmental attitudes, the internal locus of control and personal responsibly) have significant positive effects on the consumers’ eco‐friendly food purchase intention. Such effect is stable across consumers with different income levels. On the other hand, the knowledge–skill factors in the REB model do not have significant effect on the purchase intention of consumers. This study contributes to a better understanding of factors affecting eco‐friendly food consumption intention in China and the behavioural characteristics of consumers in developing countries. Moreover, the findings also shed light on the applicability of the REB theory in emerging economies and a specific industrial context.

Batch experiments were conducted to investigate the effect of nitrate on indole degradation characteristics and methanogenesis under mixed denitrification and methanogenesis culture for the first time. The degradation characteristics were studied under various nitrate concentrations. Nitrate was found to facilitate indole degradation and inhibit methane formation, and the optimum concentration was 50 mg L−1 (COD/NO3−-N = 12). Balance analyses showed that nitrate mainly affected indole-C, which generated a higher degree of indole mineralization under NO3−-N treatment than in the control. Furthermore, GC–MS combined with 13C-isotope analyses conclusively identified the intermediate metabolites, which included the hydroxylated compounds oxindole and isatin, and the methylated compound 3-methylindole. The results indicated that oxindole did not accumulate and was quickly reduced when nitrate was added, but 3-methylinole, as a recalcitrant material, accumulated without reduction. High-throughput sequencing technology was used to analyse the dynamic microbial community. Results showed that the denitrifying bacterial genera Acinetobacter, Candidimonas and Longilinea accumulated in denitrification stage and decreased in methanogenesis stage, whereas the fermentation genera Alcaligenes and Thermogutta increased in methanogenesis stage. The genera Methanosphaerula and Methanothrix constituted the dominant archaeal communities. The findings obtained in this work may provide a theoretical basis for treatment with N-heterocyclic compounds under mixed culture.

Keywords: knowledge sharing, higher education, learning organization, training and development INTRODUCTION Knowledge sharing plays a vibrant role in the higher education institutions (HEIs) for enhanced performance, particularly through effective planning, organising, monitoring and coordinating the knowledge-based assets related to intellectual capital. [...]the HEIs would develop knowledge sharing mechanism for overall enhanced performance (Sanchis et al., 2020). [...]through using a profiling approach, this research paper is trying to highlight the most frequently researched elements of knowledge sharing in the higher education institutions. According to Burns and Griffith (2018), the technical information may be quantitative or qualitative, but is generally explicit and in the public domain. According to Marshall (2019) the HEIs' teaching and learning environment is required to embrace technology for knowledge sharing in order to develop variations in the fundamental principles of teaching and learning as far as the education sector performance is concerned.

As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

The treatment of waste activated sludge (WAS) is undergoing a paradigm shift from “waste disposal” toward a more promising prospect “energy and resource recovery”. Anaerobic digestion (AD) has been the core technology in the biotransformation of carbon-rich biomass, assisted by appropriate pretreatment methods to break the bottleneck of particulate organics hydrolysis, and is integrated with novel technologies, for example, microbial bioelectrochemical system (BES) or by aid of conductive materials, to accelerate the electron transfer between different species. In this chapter, efficient pretreatment methods, including physical, chemical, biological, and their combination, were covered to elucidate their performance in WAS decomposition and hydrolysis. Besides, technologies including dark fermentation (DF), microbial electrolysis cells (MECs), and their combination were introduced to comprehend the bioprocesses for hydrogen production. Recent efforts in upgrading biogas recovery were also presented, with focus on operation optimization, configuration modification, and novel technologies. Moreover, the recovery of high value-added biometabolites, including short-chain fatty acids (SCFAs) and medium chain fatty acids (MCFAs), was elaborated.

Waste activated sludge (WAS) is in dire need of prudent disposal due to its abundant organics, as well as the latent refractory contaminants and heavy metals, for instance. Applications of surfactants, especially biosurfactant, provided comprehensive opportunities for WAS treatment via alleviating the stiff protection of extracellular polymeric substances (EPS) matrix and microbial cell wall, facilitating the value-added bio-metabolites and energy recovery, decontaminating the refractory organics, dehydrating WAS flocs, and removing heavy metals. In this chapter, applications of surfactants for the short-chain fatty acid (SCFA) extraction was covered with specific attention on the promotion of both hydrolysis and acidification via increasing available organics and hydrolyzing enzyme, as well as inhibiting the methanogenesis step. Also, the effect of surfactants on bio-energy recovery, including methane and hydrogen, was discussed. Benefited from the surfactant pretreatment in anaerobic digestion (AD) process, the performance in sludge dewaterability was comprehended. Due to the hydrophobic nature of some refractory organics, the surfactant micelles were employed to decontaminate polycyclic aromatic hydrocarbons (PAHs), dyes and, polychlorinated biphenyl (PCB) in WAS. Furthermore, recent efforts in heavy metal desorption from sludge flocs were addressed. Finally, state-of-the-art processes to promote organics biotransformation from WAS were presented, including co-pretreatment, interfacing AD with bioelectrochemical systems and optimizing process conditions.

Exploration of value-added products from wastewater treatment plants (WWTPs) was promising for its sustainable development. This study simultaneously addressed the possibility of volatile fatty acids (VFAs) production boost and cellulosic components recovery from fine-sieving fractions (FSF) under initial alkaline conditions. The step utilization of FSF was relatively untapped in similar literatures. The effect of different initial pH values with 8.5, 9.5 and 10.5 (defined as F-8.5, F-9.5 and F-10.5) on fermentation performance were investigated. Then, the fermentation residues were collected to evaluate the changes in chemical structure and thermodynamic properties by fourier transform infrared spectroscopy (FTIR) and thermo-gravimetric (TG) analysis. Furthermore, analysis of the changes in microbial community structure and the interaction between functional genus and performance parameters were undertaken by high throughput sequencing and canonical correspondence analysis (CCA). Results showed that F-10.5 obtained the highest VFAs yields of 234 mg/g VSS, due to efficient polysaccharides release and inhibited methane production. However, high alkaline intensity caused proteins denaturation. Acidogenesis kinetics suggested that the fermentation rate was chemical-dominated. Although crystalline structure was more disordered with increasing alkalinity, the weight loss was lower than 2.5%, making it possible to recover cellulose from fermented residues. Interaction between functional genus and performance parameters revealed the microbial mechanism during the alkaline fermentation. Consequently, the initial-alkaline motivated fermentation was proved to be a promising technology in value-added products recovery to be cost economic, energy positive and environmental friendly.

Maximizing the internal carbon sources in raw wastewater was found to be an alternative option to alleviate the financial burden in external carbon sources (ECS) addition to the biological nutrient removal (BNR) process. Based on previous studies on particulate recovery via fine-sieving technologies, alkali pretreatment was used to improve the short-chain fatty acid (SCFA) production from the fine-sieving fractions (FSF). Hydrolysis performance and methane production were monitored to evaluate the reasons for the SCFA boost. Besides, the microbial community structure was evaluated by high-throughput sequencing. Furthermore, mass balance and financial benefits were preliminarily estimated. The results showed that alkali pretreatment effectively promoted the generation of SCFAs with 234 mg/g volatile suspended solids (VSS), almost double that of the control test. This was partially attributed to the efficient hydrolysis, with soluble polysaccharides and protein increased by 2.1 and 1.2 times compared to that of the control, respectively. Inhibition of methanogens was also devoted to the accumulation of SCFAs, with no methane production until 150 h at high pH value. Finally, a preliminary evaluation revealed that 44.51 kg/d SCFAs could be supplied as the electron donor for denitrification, significantly reducing the cost in ECS addition for most wastewater treatment plants (WWTPs) with carbon insufficiency.

To address the availability of carbon sources for denitrification, the accelerated hydrolysis of the most abundant but low-availability fraction of particulate organic matter (POM) was investigated. Mesh sieves with different pore sizes were used as primary pretreatment at the start-up-stage of the biological process to separate some POM from the liquid system. The changes in soluble carbohydrates and proteins were monitored to investigate the hydrolysis performance of the sieved POM, with waste activated sludge (WAS) as the control test. The results showed that an average of 35% POM could be entrapped before filtrate mat development. In addition, benefiting from the high polysaccharides concentration, as well as the high availability due to the relatively loose physical structure, a 23% hydrolysis efficiency of POM was obtained, in contrast to that of WAS (3.4%), with a hydrolysis constant of 0.39 h−1. The prominent performance was also attributed to the unique microbial communities having been domesticated at a lower temperature, especially the cellulose-degrading bacteria Paraclostridium and psychrophile Psychrobacter, making up 6.94% and 2.56%, respectively. Furthermore, the potential benefits and application of improved POM hydrolysis by start-up stage recovery via mesh sieves combined with anaerobic fermentation were evaluated, including selective POM entrapment, alleviation of blockage and wear, and a reduction in aeration energy. By the proposed strategy, carbon availability for biological nutrient removal (BNR) processes is anticipated to be improved more economically than that can be achieved by primary clarifier elimination.

While marketing analytics plays an important role in generating insights from big data to improve marketing decision-making and firm competitiveness, few academic studies have investigated the mechanisms through which it can be used to achieve sustained competitive advantage. To close this gap, this study draws on the dynamic capability view to posit that a firm can attain sustained competitive advantage from its sensing, seizing and reconfiguring capabilities, which are manifested by the use of marketing analytics, marketing decision-making, and product development management. This study also examines the impact of the antecedents of marketing analytics use on marketing related processes. The analysis of a survey of 221 UK firm managers demonstrates: (a) the positive impact of marketing analytics use on both marketing decision-making and product development management; (b) the effect of the latter two on sustained competitive advantage; (c) the indirect effect of data availability on both marketing decision-making and production development management; and (d) the indirect effect of managerial support on marketing decision-making. The research model proposed in this study provides insights into how marketing analytics can be used to achieve sustained competitive advantage.

Advances in Business Analytics in the era of Big Data have provided unprecedented opportunities for organizations to innovate. With insights gained from Business Analytics, companies are able to develop new or improved products/services. However, few studies have investigated the mechanism through which Business Analytics contributes to a firm's innovation success. This research aims to address this gap by theoretically and empirically investigating the relationship between Business Analytics and innovation. To achieve this aim, absorptive capacity theory is used as a theoretical lens to inform the development of a research model. Absorptive capacity theory refers to a firm’s ability to recognize the value of new, external information, assimilate it and apply it to commercial ends. The research model covers the use of Business Analytics, environmental scanning, data-driven culture, innovation (new product newness and meaningfulness), and competitive advantage. The research model is tested through a questionnaire survey of 218 UK businesses. The results suggest that Business Analytics directly improves environmental scanning which in turn helps to enhance a company's innovation. Business Analytics also directly enhances data-driven culture that in turn impacts on environmental scanning. Data-driven culture plays another important role by moderating the effect of environmental scanning on new product meaningfulness. The findings demonstrate the positive impact of business analytics on innovation and the pivotal roles of environmental scanning and data-driven culture. Organizations wishing to realize the potential of Business Analytics thus need changes in both their external and internal focus.

Extraction of short-chain fatty acids (SCFAs) from fermentation liquid of waste activated sludge (WAS) is the key bottleneck hindering its application as electron donor in denitrification. This study explores the feasibility of polyether-type polyurethane (PU)-modified layered double hydroxides (LDHs, prepared using eggshell waste as calcium source) in SCFAs adsorbing from WAS fermentation liquid (SFL). The adsorption parameters were first optimized by adsorption tests using artificial fermentation liquid (AFL). Then, adsorption kinetics, thermodynamic and isotherms were explored to further understand the adsorption mechanism. It revealed that SCFAs absorption by PU-LDHs from SFL was an endothermic and spontaneous process with positive enthalphy (ΔH◦) values and negative Gibbs free energy (ΔG◦) values. In addition, the maximum adsorption capacity of 208.0 mg SCFAs/g PU-LDHs was obtained from the Langmuir isotherm. Noting that both soluble carbohydrates and soluble proteins were simultaneously extracted, with efficiencies of 30.9%, 6.2%, respectively, compared with 62.9% SCFAs. The reuse tests confirmed that the prepared PU-LDHs can be used at least three times with high adsorptive capacity. With PU-LDHs-loaded SFL as external carbon source in the biodenitrification process, a denitrification rate of 0.014 mg NO3−-N/mg mixed liquid suspended solids (MLSS)·d was recorded. This study provided a sound basis for the preparation of cost-effective biodenitrification carbon source from SFL by a novel adsorbent.